Information representation device to be used in vehicle

FIELD: transport.

SUBSTANCE: invention relates to technique of driving condition representation to driver. Information representation device to be used in a vehicle includes movement conditions receiving module; the first module for movement conditions distributions calculation; the first module for driving instability determination; module for training completion determination; the second module for driving instability determination; instability selection module and module for information representation to driver. Driving conditions data is the data received in time range differing from immediately preceding time range. Other time range changes depending on training degree which is ratio of elapsed time from movement conditions data collection start to present time to training time, where time range is set so that it becomes greater while training degree increases. In another version, the device additionally contains module of driving scenario determination.

EFFECT: functionality enhancement.

17 cl, 16 dwg

 

The technical field TO WHICH the INVENTION RELATES

[0001] the Present invention relates to methods of presentation of the unstable state of the driving to the driver.

The LEVEL of TECHNOLOGY

[0002] In the device for driving the vehicle described in patent document 1, the calculated long-term distribution of the States of motion corresponding to the driving characteristics in normal operation and short distribution of States of motion corresponding to the current driving characteristics, and the driving state is unstable is determined on the basis of the absolute value of the difference calculated between the two distributions. Should indicate that it is possible to accurately detect an unstable state regardless of changes in the traffic situation according to this method.

The PRIOR art DOCUMENTS

PATENT DOCUMENTS

[0003] the Patent document 1. JP 2009-9495 A

Summary of the INVENTION

TASK TO RESOLVE

[0004] However, in the technology disclosed in patent document 1, when training for obtaining long-term distribution of States of a motion is not completed to such an extent that the characteristics of driving in normal mode are considered to be aware of, i.e. characteristics when driving in normal mode are not understood, the accuracy of ODA�dividing the unstable condition of driving is reduced.

The present invention accomplished based on the above circumstances, and its objective is to represent the unstable state of the driving to the driver, even when training on the characteristics of driving in normal mode is not completed.

The solution of the PROBLEM

[0005] to achieve the above objectives, according to the aspect of the present invention, the first determining module instability driving to assess the instability driving on the basis of the difference between multiple distributions of States of motion for different time ranges based on the data States of motion. According to the aspect of the present invention, is provided a second determining module instability driving for assessing instability of driving based on the data States of motion using a process different from the process evaluation of the first module definitions instability driving. According to the aspect of the present invention, is selected instability, assessed by means of the first module definitions instability driving, when a predetermined learning time elapses from the start of data collection States of motion and determined that training is complete, and instability, assessed by means of the second module determining instability driving when Oprah�by the that education is not completed. According to the aspect of the present invention, the information of the instability based on the selected instability is presented to the driver.

Advantages of the INVENTION

[0006] According to the aspect of the present invention, it is possible to represent the state of an unstable driving to the driver depending on the instability driving, evaluated by means of the second module determining instability driving, even when studies for obtaining the distributions of States of motion used by the first module of the definition of instability driving, not completed to such an extent that the characteristics of driving in normal mode are treated as clear.

BRIEF description of the DRAWINGS

[0007] Fig. 1 is a diagram illustrating a configuration of a vehicle according to the options of implementing the present invention;

Fig. 2 is a diagram illustrating an example system configuration according to the first to fourth variants of implementation of the present invention;

Fig. 3 is a diagram illustrating a process in a module representation of information according to the first embodiment of the present invention;

Fig. 4A and 4B are diagrams illustrating an example of the information presented to the driver;

Fig. 5 is a diagram illustrating an example vychislitielnyje entropy;

Fig. 6 is a diagram illustrating symbols used to compute the relative entropy;

Fig. 7 is a diagram illustrating a method of calculating the previous term or long-term distribution and immediately preceding distribution based on the data of the forecast error of the rotation angle when taxiing;

Fig. 8 is a diagram illustrating a method of calculating the relative entropy;

Fig. 9 is a diagram illustrating the partition of the prediction error of the rotation angle when taxiing;

Fig. 10 is a diagram illustrating a process in a module presenting information according to a second embodiment of the present invention;

Fig. 11 is a diagram illustrating a process in a module presenting information according to a third embodiment of the present invention;

Fig. 12 is a diagram illustrating a process in a module representation of information according to the fourth embodiment of the present invention;

Fig. 13 is a diagram illustrating an example system configuration according to the fifth embodiment of the present invention;

Fig. 14 is a diagram illustrating a process in a module representation of information according to the fifth embodiment of the present invention;

Fig. 15 is a diagram �llustrious an example system configuration according to the sixth embodiment of the present invention; and

Fig. 16 is a diagram illustrating a process in a module representation of information according to the sixth embodiment of the present invention.

DETAILED DESCRIPTION of embodiments of

[0008] the First variant implementation

First described first variant implementation of the present invention with reference to the accompanying drawings.

Configuration

Fig. 1 is a diagram illustrating a configuration of the vehicle with the mounted device providing information for use in a vehicle according to this variant implementation.

As shown in Fig. 1, the vehicle of this embodiment includes a sensor 1 the degree of opening of the accelerator pedal sensor 2. working size pressing the brake pedal sensor 3 angle when taxiing, the sensor 4 of the vehicle speed sensor 5 for determining the state of a direction indicator, instrument panel 6, the navigation system 7, G-sensor 8, the device 9 of the definition of vehicles traveling ahead, and the controller 100. The vehicle to which is applied the present invention, need not necessarily include the above-mentioned sensors and other complex equipment. The sensors used in other variants of implementation, are described together.

[0009] D�tcic 1 the degree of opening of the accelerator pedal determines the degree of opening (instructed acceleration value) of the accelerator pedal as instructed acceleration values. A certain opening degree is output to the controller 100.

Sensor 2 working value pressing the brake pedal detects an operation amount (instructed braking force) of the brake pedal as instructed braking force. Some working value is output to the controller 100.

Sensor 3 angle when taxiing represents, for example, an angle sensor attached near the steering column or steering wheel (not shown), and determines the rotation angle when taxiing according to operation of the taxi driver on the basis of rotation of the steering shaft. A certain angle when taxiing is output to the controller 100.

[0010] the Sensor 4 of the vehicle speed determines the speed of the vehicle, for example, by determining the number of revolutions of the vehicle wheels. A certain speed of the vehicle is output to the controller 100. The sensor 4 of the vehicle speed may determine the vehicle speed based on the signal in the dashboard 6.

Sensor 5 for determining the state of the turn signal determines the state of the turn signal handle to activate the turn signal. A particular state of the turn signal is output to the controller 100.

[0011] the Device information delivery outputs a warning signal or other performance� as sound or image in response to a control signal from the controller 100. The device of providing information includes a speaker 10, which provides information to the driver, for example, using an audio signal or speech, and a display module that provides information through the display of images or texts. The monitor of the navigation system 7 can be used, in General, as the rendering engine.

[0012] the Navigation system 7 includes receiving a GPS device, a map database, and monitor and is a system that performs a route search, the navigation instructions for the route, etc. Navigation system 7 allows you to receive information such as the type of road on which the vehicle moves, or the width of a road, based on the current position of the vehicle obtained from the GPS receiver device, and information of the road stored in the map database.

[0013] G-sensor 8 detects the longitudinal acceleration or lateral acceleration generated in the vehicle. A certain acceleration is output to the controller 100.

Device 9 definition of the rider in front of vehicle detects other vehicles and other objects present ahead in the direction of movement of the vehicle. In this embodiment, the implementation is determined by the distance to objects�and. Device 9 definition of the rider in front of vehicles includes, for example, a laser rangefinder. A certain distance is output to the controller 100 as the information for calculating the distance between vehicles, the time between vehicles, a relative speed, etc.

[0014] the Controller 100 is an electronic control module that includes a CPU and CPU peripheral components, such as ROM and RAM, and includes a module 100A reporting, which executes the process control information. The module 100A of the information presentation controller 100 analyzes driving characteristics of the driver based on the signals that are defined by means of the sensor 1 the degree of opening of the accelerator pedal sensor 2. working size pressing the brake pedal sensor 3 angle when taxiing, etc., and determines the degree of instability of driving, for example, an incorrect operation of driving the driver. The module 100A of the present information is a warning signal or other information to the driver depending on the degree of instability driving to attract the driver's attention.

[0015] Fig. 2 is a diagram illustrating an example system configuration of the device for providing information for use in the vehicle, including �Odul 100A submission of information under this option implementation.

The device of providing information for use in a vehicle according to this embodiment of the uses information from the sensor 3 angle when taxiing in the quality of the data States of motion as shown in Fig. 2. The device providing visual information and the device providing the audio information are illustrated as a device of representation. The device provide visual information represents, for example, the instrument panel 6 or a display module of the navigation system 7. The device providing the audio information represents, for example, the speaker 10.

The timer 50 is used for receiving the driving time from beginning of data collection States of motion.

Configuration identical to the configuration of the system shown in Fig. 2, is used by systems according to the second to fourth variants of implementation, which are described below.

[0016] the Process module 100A of the present information is described with reference to Fig. 3. The process module 100A of information presentation is performed in a predetermined cycle (for example, 100 MS).

First, in step S1010, the module 100A presentation of information receives the following data as information data regarding the vehicle. In�mi words, the module 100A presentation of information receives the rotation angle when taxiing in the quality of the data States of a motion of the sensor 3 angle when taxiing.

[0017] In step S1030, the module 100A of the present information defines the state of education. In this variant of implementation, the movement from the beginning of data collection is used to determine the state of learning. The degree of SD can be calculated using the number of collected fragments of data.

In particular, in step S1030, the module 100A of the present information, calculates the degree of SD training on the basis of the following expression.

The degree SD training = time (s) motion/(time range * ratio)

Movement time: the time after the start of motion

Time range: the time range (e.g., 2000 seconds) distribution of States of motion

Coefficient: the coefficient (e.g., 5), associated with convergence time

The value (time range * coefficient) corresponds to the predefined training time.

The motion is obtained from the timer 50.

[0018] In step S1030, the module 100A of the present information then determines the state of learning from the calculated degree of SD training.

In this variant implementation, when the degree SD training equal to or greater than "1", it is determined that the learning condition is�m completed training. On the other hand, when the degree SD training less than "1", it is determined that the state of education is the progress of learning.

In step S1040, the module 100A presentation of information determines the method of calculating the instability based on the state of learning, defined in step S1030. In particular, the module 100A of the present information executes a process of step S1050, when it is determined that the learning status is a status of completed training (degree SD training≥1). On the other hand, the module 100A of the present information executes a process of step S1070, when it is determined that the state of education is the progress of study (degree SD training<1).

[0019] When it is determined that training is complete, and the process continues to step S1050, the module 100A of reporting computes multiple distribution States of motion when driving using a method based on entropy taxiing and calculates the difference (relative entropy) between the distributions. Thereafter, the process proceeds to step S1060.

[0020] In particular, in step S1050, the module 100A of the present information calculates a Delta value to determine whether the current driving operation of the driver is different from the operation of driving in normal mode, i.e. whether or not current op�the radio driving unstable compared to the driving operation in a normal mode, on the basis of the rotation angle when taxiing, when the driver performs the steering operation. In other words, in step S1050, the relative entropy (typical quantitative index, the volatility) is calculated as a value indicating an incorrect operation, i.e. the operation smooth driving. In General, in a state in which the driver does not pay attention to the driving operation, the time at which fails taxiing exceeds the operation time in normal driving mode in which the driver pays attention to driving, and therefore a large error angle when taxiing accumulates. Therefore, the adjusted value of the taxi, when the driver pays attention to driving, increases again. In this variant of implementation, the relative entropy RHp is calculated using this feature. In particular, calculates the distribution of errors in taxiing (the distribution of States of motion), accumulated in the past or for a long time before the current time, and the distribution of errors in taxiing (the distribution of States of motion) of the driver at the current time, obtained in a short time, i.e. multiple distribution States of motion for different time ranges. The distribution of errors in taxiing for a long time, which is approached�Xia as characteristics of driving in normal mode as a reference value for comparison, the relative entropy RHp is calculated on the basis of long-term distribution of errors in taxiing and the current short-term distribution of errors in taxiing.

[0021] Here, the relative entropy RHp is a physical quantity that indicates the value of the difference (distance) between two distributions of errors in taxiing, and represents the degree of difference between two distributions of errors in taxiing, i.e. the extent to which these two distributions of errors in taxiing deviate from each other. Stability immediately preceding the current state of motion relative to the previous long-term state of motion (characteristics of driving in normal mode) can be estimated using the calculated values of the relative entropy.

An example of a calculation of the distribution of errors in taxiing accumulated over a long period of time, the current distribution of errors when the taxi driver received in a short time, and the values of the difference (relative entropy) between the distributions described below.

[0022] In step S1060, the module 100A presentation of information determines the driving state is unstable based on the value of the difference.

In step S1060 of this variant implementation, the value of the difference calculated in step S1050, compared with pre-defined�threshold value th for determination. When the difference exceeds the threshold value for determination, determines that the driving condition is unstable. After that, the process continues to step S1100.

[0023] on the other hand, when in step S1040 is determined that the learning is not completed, the process proceeds to step S1070.

In step S1070, the module 100A of reporting calculates current characteristic quantitative index (absolute entropy) using the current distribution of errors in taxiing for a shorter time range. Thereafter, the process proceeds to step S1080. Absolute entropy is the expected value arising in the distribution of States of motion.

In step S1080, the module 100A presentation of information reads quantitative characteristic of the previous driving. Typical quantitative index of the previous driving is the ultimate value (absolute entropy) while driving in the past. Thereafter, the process proceeds to step S1090.

[0024] In step S1090, the module 100A of information presentation compares the current characteristic quantity indicator Hp_current calculated in step S1070, with a reference characteristic quantity index obtained by multiplying the characteristic quantitative indicator Hp_old previous driving read in step S1080, by a factor k, as expressed by the following expression. When the current characteristic quantity indicator Hp_current exceeds the reference characteristic quantity indicator (Hp_old*k), determined that the state of motion is unstable. After that, the process continues to step S1100.

Hp_current/(Hp_old*k)>1

Here, the coefficient k is set equal to, for example, 1.5.

[0025] In step S1100, the module 100A of the present information executes a process for the reporting, when in step S1060, or in step S1090 determined that the state of motion is unstable.

An example of information that should be presented is shown in Fig. 4A and 4B. In other words, when the degree SD training equal to or greater than "1", and determines that the state of motion is unstable, the device is the provision of information displays a warning as shown in Fig. 4A, and is warning voice message, such as "Driving too nervous. Drive careful!".

[0026] on the other hand, when the degree SD training less than "1", and determines that the state of motion is unstable, the accuracy of the estimate may be low, and the device providing the information is a warning voice message in the form of a polite turnover, for example "How are you feeling? Keep�e caution when driving".

Thus, the information that must be provided varies depending on the degree of SD training.

In step S1110, it preserves the current characteristic quantity indicator (absolute entropy). The current characteristic quantity indicator (characteristic quantitative value calculated in step S1070) is stored for comparison during the next tour (the movement).

After that, the process ends and returns.

[0027] an example of a process of computing the distribution of the errors when taxiing (the state distribution of motion), accumulated for a long time, the current distribution of errors in taxiing (the state distribution of motion) of the driver obtained in a short time, and the values of the difference (relative entropy) between the distributions is described with reference to Fig. 5.

The details of this process are continuously performed at regular intervals, e.g. every 50 MS.

[0028] In step S10, the scenario vehicle motion is estimated (determined) to determine whether or not the scenario traffic scenario traffic, which can be calculated the relative entropy RHp. Here, when the speed V of the vehicle is within a predetermined speed range of the vehicle (for example, 40-120 km/h), opredelaetsa�, the movement script is the script of the movement in which it can be calculated the relative entropy RHp. In other words, a case where the vehicle speed is excessively low, and a case where the vehicle speed is excessively large, excluded from the calculated scenario of motion in order to efficiently compute the relative entropy RHp using the signal of the rotation angle when taxiing.

[0029] In step S20, it is determined if is or not the current speed V of the vehicle as determined by the sensor 4 of the vehicle speed, the predetermined speed range of the vehicle. When it is determined that the vehicle speed V is within a predetermined speed range of the vehicle, and the movement script is the script of the movement in which it can be calculated the relative entropy RHp, the process moves to step S30 to calculate the relative entropy RHp. On the other hand, when it is determined that the speed V of the vehicle is in the predetermined range, the process ends.

[0030] In step S30, the current signal θ angle when taxiing defined by a sensor of the rotation angle when taxiing, is read in Kutch�as the working value of driving which must be defined in order to determine the condition of unstable driving of the driver. In step S31, the error θe predict the rotation angle when steering operation is calculated on the basis of reading of the signal θ angle when taxiing.

Here, special designations and names used to calculate the relative entropy RHp shown in Fig. 6. Smoothed value θn-tilde angle when taxiing is the rotation angle when taxiing, since that decreases the influence of the quantization noise. Estimated value θn-hat angle when taxiing is a value obtained by estimating the rotation angle when taxiing during sampling, provided that taxiing is performed smoothly. As expressed by expression 1, the estimated value θn-hat of the rotation angle when steering is obtained by means of a process of decomposition in a Taylor series second order smoothing value θn-tilde angle when taxiing.

[0031] in Mathematical expression 1"

(expression 1)

[0032] In expression 1, and tn represents the time discretization of the angle θn turn when taxiing.

Smoothed value θn-tilde angle when taxiing is calculated as the average value of three adjacent angles θn turn when taxiing in accordance with the expression 2, so as to reduce �lianie of quantization noise.

[0033] in Mathematical expression 2"

(expression 2)

[0034] In the expression 2, l represents the number of samples of the angles θn turn when taxiing included in 150 MS when the time interval calculate the smoothed value θn-tilde angle when taxiing is set to 150 MS, i.e. the minimum time interval in which the person can periodically perform manipulations in manual mode.

When the sampling interval of the angle θn turn when taxiing is defined as Ts, the number l of samples is expressed by expression 3.

l=round(0,15/Ts) (expression 3)

[0035] In expression 2, k has the values 1, 2 and 3, and the smoothed value θn-tilde can be calculated using (k*l) on the basis of the three angles θn turn when taxiing as the sum of the angles of rotation when taxiing at intervals of 150 MS and nearby angle when taxiing, related. Therefore, the estimated value θn-hat, computed on the basis of the smoothed value θn-tilde is calculated on the basis of the angle θ of the turn when taxiing, obtained at intervals of 150 MS.

Error θe predict the rotation angle when taxiing at the time of sampling can be calculated by expression 4 as the difference between the estimated value θn-hat angle when taxiing, when it is assumed that the steering operation is performed smoothly, and actually�m value θn angle when taxiing.

[0036] in Mathematical expression 3"

(expression 4)

[0037] Here, the error θe predict the rotation angle when steering operation is calculated for the angle θn turn when taxiing through each minimum time interval, i.e., 150 MS, in which the person can periodically perform manipulations.

The following describes a specific method of calculation of the error θe predict the rotation angle when taxiing. The interval Ts of the sampling signal θ of the rotation angle when steering operation is set to, for example, 50 msec. First, three smoothed values θn-tilde angle when taxiing are calculated according to expression 2 by using three adjacent angles θn turn when taxiing with an interval of 150 MS. Three smoothed values θn-tilde angle when taxiing is expressed by expression 5.

[0038] in Mathematical expression 4"

(expression 5)

[0039] the Estimated value θn-hat angle when taxiing are calculated in accordance with expression 1 using the calculated three smoothed values θn-tilde angle when taxiing. Estimated value θn-hat is expressed by expression 6.

[0040] in Mathematical expression 5"

(expression 6)

[0041] Error θe predict when taxiing is calculated in accordance with expression 4 using calc�lennyh the estimated value θn-hat angle when taxiing and the actual angle θn turn when taxiing.

In step S40, the data error θe predict the rotation angle when taxiing within a predetermined time T seconds, are calculated up to the present time and stored in the memory device controller 100, are updated by summing the current value of the error θe predict the rotation angle when taxiing, calculated in step S31, with them. In other words, the first data in T seconds from the accumulated data error θe predict the rotation angle when steering is removed, and the current value calculated in step S31, is introduced instead of them as of the most recent data error θe predict the rotation angle when taxiing. Accordingly, these errors θe predict the rotation angle when taxiing for T seconds from the current values are accumulated. Predetermined time T is set equal to, for example, T=3600 seconds (=1 hour) in order to accumulate data over a long period, sufficient to calculate the long-term distribution of the error term, which is the reference value for comparison to determine the current unstable state of the driving operation.

[0042] In step S50 is calculated, the previous or the long-term distribution 1 the forecast error of the rotation angle when taxiing, which serves as a reference value to compare the distribution of �had poor prediction angle when taxiing. Here, as shown in Fig. 7, the previous distribution of the forecast error of the rotation angle when steering operation is calculated, for example, using the data in 180 seconds based on the data in T seconds. In particular, the previous accumulated error θe predict the rotation angle when steering is classified into nine sections b1-b9 of forecasting error is calculated and the probability pi (=p1-p9) frequency error θe predict the rotation angle when taxiing, included in each partition bi relative cumulative frequency. Calculated the previous distribution is used as reference value to compare the distribution of the forecast error of the rotation angle when taxiing. Range section bi of the prediction error is set in advance such that it is constant in all sections b1-b9.

[0043] When calculating a long-term distribution of the forecast error of the rotation angle when taxiing, all the data used to 3600 seconds from the time in T seconds before the current time. In particular, long-term accumulated error θe predict the rotation angle when steering is classified into nine sections b1-b9 of forecasting error is calculated and the probability pi (=p1-p9) frequency error θe predict the rotation angle when taxiing, included in each partition bi relative cumulative frequency. Calculated the previous RA�distribution (or long-term distribution) is used as the previous (or long-term) distribution 1 is the forecast error of the rotation angle when taxiing, serving as a reference value to compare.

[0044] In step S51 calculates the current distribution of 2 the forecast error of the rotation angle when taxiing. Here, as shown in Fig. 7, the current distribution of 2 the forecast error of the rotation angle when steering operation is calculated using the immediately preceding data in 180 seconds with the current time. In particular, these errors θe predict the rotation angle when taxiing for the immediately preceding 180 seconds are classified into nine sections b1-b9 of forecasting error is calculated and the probability qi (=q1-q9) frequency error θe predict the rotation angle when taxiing, included in each partition bi relative cumulative frequency.

[0045] In step S70, the relative entropy RHp is calculated using the previous (or long-term) distribution 1 is the forecast error of the rotation angle when taxiing and current distribution 2 the forecast error of the rotation angle when taxiing. As shown in Fig. 8, the relative entropy RHp is the value of the difference (distance) between the current distribution 2 the forecast error of the rotation angle when taxiing and the previous (or long-term) distribution 1 is the forecast error of the rotation angle when taxiing as the reference value for comparison. Relative entropy RHp may be in�Chilena using expression 7.

[0046] in Mathematical expression 6"

(expression 7)

[0047] the relative entropy RHp is equal to RHp=0, when the probability pi of the previous (or long-term) distribution 1 is the forecast error of the rotation angle when taxiing and the probability qi of the current distribution 2 the forecast error of the rotation angle when taxiing equal to each other, and the value of the RHp is increased when the probabilities pi and qi are increasingly differ from each other.

After which the process ends. The above process is executed repeatedly.

[0048] the Range of the section bi of the prediction error for the calculation of the previous (or long-term) distribution 1 is the forecast error of the rotation angle when taxiing and current distribution 2 the forecast error of the rotation angle when steering can be set based on the value of α used to calculate the entropy Hp taxiing, indicating the ambiguity (uncertainty) in the distribution of errors in taxiing. Here, the value of α is calculated as the 90 percentile (range of distribution, including 90% of the errors when taxiing) by computing the error when taxiing within a constant time, i.e. the difference between the evaluated value of the rotation angle when taxiing and the actual rotation angle when taxiing, when it is assumed that the operation R�the office runs smoothly on the basis of time-series data of the rotation angle when taxiing, and distribution measurements (deflection) errors when taxiing.

[0049] Consequently, the value of α is calculated on the basis of previous (or long-term) distribution of the forecast error of the rotation angle when taxiing, and is identical to the partition range bi prediction error is set to the previous (or long-term) distribution 1 is the forecast error of the rotation angle when taxiing and current distribution 2 the forecast error of the rotation angle when taxiing using the computed values of α. Fig. 9 shows the ranges of error θe predict the rotation angle when steering of the sections bi, defined using the value of α.

[0050] the Operation and other aspects

When the state of education can be seen as a status of completed training (which can be obtained characteristics in normal driving mode the driver) based on the time of the movement after data collection started, the device providing information for use in the vehicle calculates the volatility using the method based on the entropy of taxiing using the processes of steps S1050 and S1060.

[0051] At this time, the device providing information for use in the vehicle calculates the difference between the calculated multiple distributions of States of motion and op�etelaat the driving state is unstable on the basis of the absolute value of the difference. Accordingly, it is possible to accurately determine the condition of unstable movements, regardless of changes in the environment of the road. In other words, it is possible to accurately detect an unstable state based on the usual characteristics of the driver, regardless of changes in the environment of the road.

[0052] At this time, the device providing information for use in the vehicle calculates multiple distribution States of motion for different time ranges as a multiple of distributions of States of motion. For example, a device of providing information for use in the vehicle calculates the distribution of States of motion, which includes previous data States of motion and the distribution of States of motion, including the immediately preceding data States of motion and directly computes the difference of the immediately preceding distribution States of motion on the basis of a prior distribution of States of motion. As a result, it is possible to assess the stability of the immediately preceding state under continuous updating of the reference data. Thus, it is possible to accurately detect an unstable state of motion regardless of the change of the environment of the road, and reach�I.

[0053] on the other hand, when it is determined that the state of education is the progress of training (SD<1), the device providing information for use in the vehicle compares the previous data traffic with the absolute value of the single distribution of States of motion, which is the distribution of States of motion for the immediately preceding time range that indicates the current state of motion, and determines the driving state is unstable (steps S1070-S1090). In other words, through the use of a different calculation process instability during divorce the training, you can attract attention, even when the distribution of the States of motion of the driver is unknown.

[0054] In the above variant of implementation, the degree SD training is calculated using the time the motion is estimated, it is possible to estimate what the distribution of States of motion coincides with the driving characteristics of the driver on the basis of the degree of SD training, and because of this it is determined, completed, or no training. The degree SD of instruction used in order to evaluate the distribution of States of motion coincides with the driving characteristics of the driver, can be calculated on the basis of variation of the relative entropy, which is a product�Arnim quantitative measure of the distribution of States of motion. For example, when the variation characteristic of the quantitative indicator of long-term distribution of States of motion is equal to or less than a predetermined value, a value indicating the completion of training, is defined as the degree of SD training.

[0055] Here, step S1010 is a module receiving States of motion. Step S1050 is the first module for calculating the distribution of States of motion and the first determining module instability driving. Step S1030 is the module for determining the completion of the training. Steps S1070 S1080 and make up the second determining module instability driving. Step S1040 is the module of choice instability. Step S1100 is the module reporting.

[0056] the Advantages of this variant implementation

(1) the Module is receiving States of motion receives the data States of motion which includes at least one of a driving operation of the driver and condition of the vehicle. The first module for calculating the distribution of States of motion computes multiple distribution States of motion for different time ranges based on the data States of motion obtained by means of the acquisition module States of motion. The first determining module instability of estimates the driving instability driving based on the value �agnosti between multiple distributions of States of motion calculated by the first module for calculating the distribution of States of motion. The module for determining the completion of the training determines what training is completed when a predetermined learning time elapses from the start of data collection States of motion on the basis of the degree of SD training, which is the degree to which the distribution of States of motion computed by the first module for calculating the distribution of States of motion coincides with the driving characteristics of the driver. The degree SD training is evaluated as the degree to which the distribution of States of motion computed by the first module for calculating the distribution of States of motion coincides with the driving characteristics of the driver. The second module determining instability of estimates the driving instability driving by comparing comparative distribution of States of motion derived from the other data States of motion deviating from these States of motion for the immediately preceding time range, with the distribution of States of a motion for the immediately preceding time range that indicates the current state of motion and calculated based on the States of motion obtained by means of the acquisition module States of motion. Modulebase instability selects instability, estimated by a first module defining instability driving when training is completed, and selects the instability, measured by the second module determining instability driving when training is completed, based on the result of determination module to determine completion of training. Module presenting information represents information instability on the basis of instability, selected by the module selection instability, the driver.

Through the use of another module calculation of instability while not completing the training, you can attract attention, even when the distribution of the States of motion of the driver is unknown.

[0057] (2) Other data States of motion are those of the States of motion are obtained up to the immediately preceding time range. The second determining module instability driving compares the comparative distribution of the States of motion with the distribution of States of motion for the immediately preceding time range by using the ratio of characteristic quantitative measure of the distribution of States of a motion for an immediately preceding time range and the reference characteristic quantitative indicator, which is a value obtained through multiplication�Oia characteristic quantitative indicator of the distribution of States of motion received data from other States of motion at a predetermined ratio, and estimates the instability driving.

By calculating the pivot characterized by a quantitative indicator by multiplying the characteristic of the quantitative indicators of the distribution of States of motion are computed from different data movement, at a predetermined ratio, it is possible to improve the accuracy of estimating the instability of the driving by the correction factor is used, even when learning is not completed.

[0058] (3) Second determining module instability of estimates the driving instability driving on the basis of at least one of information data States of motion obtained by means of the acquisition module States of motion and information of the scenario of driving a vehicle.

Through the use of behavior data of the vehicle, other data States of motion and the determination result of the driving scenario, it is possible to accurately determine the state of driving of the driver.

[0059] (4) the Second determining module instability of estimates the driving instability driving using one of the distributions of States of motion calculated based on the data States of motion obtained by means of the acquisition module of States of motion�I.

Through the use of a distribution of States of a motion of the driver, it is possible to apply a statistical process, and thus improve accuracy.

[0060] (5) When the module selection instability selects instability, assessed by means of the second module determining instability driving, and instability, assessed by means of the second module determining instability driving exceeds a predetermined threshold value for determination, determines that the driving condition is unstable.

Accordingly, it is possible to simply determine the driving state is unstable.

(6) the Threshold value for determination is set based on historical information of the previous motion.

Through the use of the prehistory of the previous movement, you can improve the accuracy of determination.

[0061] (7) the Second determining module instability driving includes a storage module background that preserves the historical information of the previous motion and determines the unstable state in relation to the trends of history, is stored in the module storage prehistory.

Definition using the background increases the efficiency definition.

(8) Module definition of completion of training determines the degree SD learning using time motion.

Simply�PTO determine the state of learning using the motion time, it is possible to reduce erroneous determination of completion of training.

[0062] (9) the definition Module of completion of training determines the completion of training with the use of a variation in a characteristic single quantifiable indicator of the distribution of States of motion.

By determining the status of education on the basis of a variation in characteristic of the quantitative distribution of States of motion, you can quickly determine the completion of training.

(10) the Distribution of States is calculated on the basis of the operating amount of the operation of the taxi.

By defining the state distribution based motion taxiing operations that require continuous manipulation, it is possible to accurately determine the condition of driving.

[0063] (11) a Method based on the steering entropy is used to compute the distribution of States of motion on the basis of the operating amount of the operation of the taxi.

Through the use of a method based on entropy taxiing, you can improve the performance definition.

(12) Module presentation of information changes the information instability, which must be submitted, depending on the result of the completion of training defined by the definition module of completion of training.

By changing the information that should be representative�go, depending on the determination result of the completion of training, you can improve the adaptability to the drive of the driver.

[0064] the Second variant of implementation

The following describes the second variant of implementation with reference to the accompanying drawings. Elements identical with elements of the first variant of implementation, are denoted by identical reference numbers.

The basic configuration of this variant implementation is identical to the basic configuration in the first embodiment of implementation. They differ from each other by different calculation process instability that occurs when the learning status is defined as a state of incomplete learning.

In this variant implementation, the other is the process of computing the instability is performed by comparing the absolute value of the single distribution of States of motion with the value calculated on the basis of the distributions of average drivers, and determining whether or not the driving condition is unstable.

[0065] the Following describes the process module 100A representation of information according to this embodiment of the in respect of a block diagram the sequence of operations of the method of Fig. 10.

Here, the processes in steps S2010-S2070 is an are identical to the processes of steps S1010-S1070 in the first variant implementation. The processes in steps S2100 and S110 are identical to the processes of steps S1100 and S1110. Accordingly, such processes are not described repeatedly.

[0066] the Following describes the process of phase data sheet s2080.

On the data sheet s2080 stage in this variant implementation, the module 100A of the present information typical reads quantitative indicators average drivers, pre-stored in the module storage.

Typical quantitative indicators average drivers are values obtained by performing the statistical process (e.g., averaging) for typical quantitative indicators received from multiple drivers in advance. Typical quantitative indicators average drivers can properly be updated via wireless communication, etc. are Typical quantitative indicators received from drivers, are calculated from the data of the States of motion received from drivers.

[0067] Other processes are identical to the process in the first embodiment implementation.

Here, the step S2010 is a module receiving States of motion. Step S2050 is the first module for calculating the distribution of States of motion and the first determining module instability driving. Step S2030 is the module for determining the completion of the training. Steps S2070 is an data sheet s2080 and make up the second determining module instability driving. Step S2040 is mo�ul selection instability. Step S2100 is the module reporting.

[0068] the Functional advantages

In this variant of implementation, the following advantages can be obtained in addition to the advantages described in the first variant implementation.

(1) Other data States of motion used in the second module determining the instability of the driving data are States of motion obtained in advance from multiple drivers. The threshold value for determination is obtained from the characteristics of the distributions of States of motion received from multiple drivers.

Through the use of distributions that serve as source data for average drivers, it is possible to clarify the unstable condition of the driver and thus improve performance definitions.

[0069] the Third variant of implementation

The following describes the third variant of implementation with reference to the accompanying drawings. Elements identical with elements of the first variant of implementation, are denoted by identical reference numbers.

The basic configuration of this variant implementation is identical to the basic configuration in the first embodiment of implementation. In the third variant of implementation, another process to calculate the degree of instability, when the state of education opredeljaaetsja the progress of learning, is performed on the basis of prehistory characterized by single quantitative index of a distribution, obtained on the basis of the motion state for the immediately preceding time range.

[0070] the Following describes the process module 100A representation of information according to this embodiment of the in respect of a block diagram the sequence of operations of the method of Fig. 11.

The processes of steps S3010-S3070 are identical to the processes of steps S1010-S1070 in the first variant implementation. The process of step S3100 is identical to the process of step S1100. Accordingly, such processes are not described again. Since it is not necessary to preserve the characteristic quantity indicator in this variant of implementation, the process of step S1110 is not executed.

[0071] the Following describes the process steps and S3080 S3090 in this variant implementation.

In step S3080 of this variant implementation, the module 100A presentation of information keeps from several to ten fragments of instability, calculated in step S3070 constant intervals, and calculates the deviation, the variation and the absolute value in order to determine their trend.

Deviation represents the deviation (standard deviation) of the previous instability.

The variation is based on comparing the first instability with the last�days instability.

Absolute value is the absolute value of the most recent instability.

[0072] In step S3090, the module 100A of the present information defines history data calculated in step S3080.

For example, when the above three items (deviation, variation and absolute value) satisfy the conditions of large deviations, a significant variation and a large absolute value, it is determined that the driving condition is unstable. When some conditions to the three elements satisfy the conditions of, for example, satisfied when any one condition may be determined that the driving condition is unstable.

Here, when the deviation exceeds a predetermined threshold value for the deviation, it is determined that the deviation is large. When the absolute value of the variation exceeds a predetermined threshold value for the variation, it is determined that the variation is greater. When the absolute value exceeds a predetermined threshold value for the absolute value, it is determined that the absolute value is large.

[0073] Other processes are identical to the process in the first embodiment implementation.

Here, the step S3010 is a module receiving States of motion. Step 3050 is the first module for calculating the distribution of States of motion and the first determining module instability driving. Step S3030 is the module for determining the completion of the training. Stages S3070 and S3080 make up the second determining module instability driving. Step S3040 is the module of choice instability. Step S3100 is the module reporting.

[0074] the Functional advantages

As described above, in this embodiment, the implementation of typical quantitative index (entropy) of a single distribution of States of a motion properly maintained, and unstable driving is determined on the basis of their trends (variation and absolute values).

In this variant of implementation, the following advantages can be obtained in addition to the advantages described in the first variant implementation.

(1) Second determining module instability driving calculates a characteristic quantity indicator of the distribution of States of motion for the immediately preceding time range that indicates the current state of the movement, based on the data States of motion obtained by means of the acquisition module of States of motion, and assesses the degree of instability of the driving based on the calculated characteristic of the quantitative indicators.

Through the use of a quantitative characteristic exponent of the distribution of States of a motion of the driver, you can run statistics�technical process and thereby improve accuracy.

[0075] (2) Second determining module instability calculates the driving tendency of the quantitative characteristic measure based on the background characteristic of the quantitative indicators obtained through each constant interval, and estimates the degree of instability of the driving based on the calculated trends.

According to this configuration, through the use of trends characteristic of the quantitative index can identify unstable driving without using the distribution of States of a motion for a range of dates for long-term period.

[0076] the Fourth variant of implementation

Below is described a fourth variant of implementation with reference to the accompanying drawings. Elements identical with elements of the first variant of implementation, are denoted by identical reference numbers.

The basic configuration of this variant implementation is identical to the basic configuration in the first embodiment of implementation. In the fourth embodiment of the another process to calculate the degree of instability, when the state of education is defined as a state of incomplete learning is performed based on the values of the relative entropy for different time ranges.

[0077] the Following describes the process module 100A presentation of information in accordance with this variant of the OS�, made in respect of a block diagram the sequence of operations of the method of Fig. 12.

The processes of steps S4010-S4060 are identical to the processes of steps S1010-S1060 in the first variant implementation. The process of step S4100 is identical to the process of step S1100. Accordingly, such processes are not described again. Since it is not necessary to preserve the characteristic quantity indicator in this variant of implementation, the process of step S1110 is not executed.

[0078] the Following describes the process steps S4070 and S4090.

In step S4030 determines the state of learning, as described above.

The learning status is determined using the motion time, as described above. The degree SD training is calculated, for example, using the following expression.

The degree SD training = time (s) motion/(time range * ratio)

Movement time: the time after the start of motion

Time range: the time range (e.g., 2000 seconds) distribution of States of motion

Coefficient: the coefficient (e.g., 5), associated with convergence time

[0079] In step S4070, the module 100A presentation of information sets the time ranges at which to compute two distributions of errors in taxiing (the state distribution of motion), depending on the degree of SD training, calculated in step S4030. In this variant of implementation, two of the state distribution of motion include long-term distribution�their time and short-time allocation motion. The time range of long-term allocation of time motion is defined as the time range on the basis of the degree of SD training, as described below. Briefly the distribution of States is calculated as described above.

[0080] the time Range of long-term allocation of time motion = the degree SD training * total training

Degree of study: the value (0 to 1) calculated in step S4030

The coefficient of study: time of study, or a value obtained through multiplication of duration at a predetermined ratio (<1).

The coefficient of education is obtained, for example, by multiplying the training time (time) at the predetermined constant is 1 or less.

[0081] In step S4080, the module 100A of the present information, calculates a second difference between the distributions. The calculation is performed by methods identical to the methods in step S4050 (step S1050). Here, the time range for him is different from the time range of the distribution of States of motion to calculate the first difference of the distributions. The time range of the second distribution of States of motion less time ranges of the first distribution States of motion.

[0082] When the time ranges of the first distribution States of motion are set equal Tw_s1 and Tw_l1, and time ranges �first of which distribution States of motion are set equal Tw_s2 and Tw_l2, these values are specified, for example, as follows.

Tw_s1: 60 (C)

Tw_l1: 2000 (C)

Tw_s2: 20 (C)

Tw_l2: 600 (C)

[0083] In step S4080, the module 100A presenting information preparing two allocation States of motion and computes the difference between them based on them.

In step S4090, the module 100A presentation of information determines the driving state is unstable based on the difference between (quantitative value) calculated in step S4080. In step S4090 of this variant implementation, the module 100A of information presentation compares the difference calculated in step S4080, with a predefined threshold value to determine. Then, the module 100A presentation of information determines that the driving condition is unstable, when the difference exceeds the threshold value for determination. After that, the process moves to step S4100.

[0084] Here, the step S4010 is the module receiving States of motion. Step S4050 is the first module for calculating the distribution of States of motion and the first determining module instability driving. Step S4030 is the module for determining the completion of the training. Stages S4070-S4090 make up the second determining module instability driving. Step S4070 is the second module for calculating the distribution of States of a motion and a second determining module Insta�spine driving. Step S4040 is the module of choice instability. Step S4100 module is reporting.

[0085] the Functional advantages

In this variant implementation, when the state of learning is defined as a state of incomplete learning, the module 100A of reporting calculates two values of the relative entropy for different ranges of time and changes the time range of one value relative entropy depending on the status of education.

In this variant of implementation, the following advantages can be obtained in addition to the advantages of the first variant implementation.

[0086] (1) Other data States of motion used in the second module determining the instability of the driving data are States of motion obtained in a different range of dates that differ from the immediately preceding time range. Another time range varies depending on the degree of learning as the ratio of time elapsed from the beginning of the data collection state of the movement up to the present time, training time, and another time range increases with increase in the degree of learning.

Due to the forced increase of such other time range as it is nearing the end of training, you can more smoothly change�Yat info to be presented to the driver when the completion of training goes into the completion of training. For example, you can more smoothly change to attract attention, when the completion of training goes into the completion of training.

[0087] a Fifth variant of the implementation

Below is described a fifth variant of implementation with reference to the accompanying drawings. Elements identical with elements of the first variant of implementation, are denoted by identical reference numbers.

The basic configuration of this variant implementation is identical to the basic configuration in the first embodiment of implementation. In the fifth variant of the implementation of another process to calculate the degree of instability, when the state of education is defined as a state of incomplete learning is performed based on a characteristic of quantitative indicators (relative entropy) of the two single distributions.

The system configuration of this variant implementation is shown in Fig. 13. As shown in Fig. 13, the terminals on the sensor for a good work of pressing the brake pedal and the device for determining the interference introduced into the module 100A presentation of information in addition to the pins of the sensor 3 angle when taxiing and timer 50.

[0088] the Following describes the process module 100A submission of information under this option the implementation of assilmi a block diagram of the sequence of operations of way shown in Fig. 14.

First, in step S5010, the module 100A presentation of information receives the following data as information data about the vehicle.

The module 100A presentation of information receives the rotation angle when taxiing, and an operation amount of the brake pedal presses as information about the operation of the driver based on the findings from the sensor 3 angle when taxiing and sensor 2. working size pressing the brake pedal.

The module 100A presentation of information receives the vehicle speed of the vehicle, the longitudinal component of G, the transverse component G, the relative speed compared to the front obstacle and the distance between vehicles as status information of the vehicle based on the findings of the sensor 4 of the vehicle speed, G-sensor device 8 and 9 determine the vehicle traveling ahead.

[0089] In step S5030, the module 100A of the present information defines the state of education through a process identical to the process in step S1050.

In step S5040, the module 100A presentation of information determines whether or not the state of education status of study completed, on the basis of the degree SD learning through a process identical to the process in step S1040. When the condition about�tion is defined as a status of completed training the process moves to step S5050. On the other hand, when the learning is defined as a state of incomplete learning, the process moves to the steps S5080 and S5100.

[0090] On the stages S5050 and S5060 execute the processes identical to the processes at the steps S1050 and S1060. In other words, in step S5050, calculate the value of the difference between the distributions. In step S5060, the calculated value of the difference is compared with a predefined threshold value to determine, and it is determined whether or not the driving condition is unstable.

In step S5070, the module 100A of the present information executes a process for the reporting, when in step S5060 determined that the driving condition is unstable.

[0091] on the other hand, in step S5080, the module 100A of reporting calculates the value of entropy taxiing using angle when taxiing. In step S5080, the absolute value of entropy (quantitative index) as the value of the instability is calculated based on a single distribution of States of motion. After that, the process moves to step S5090.

In step S5090, the module 100A of the present information executes a process identical to the process at the stage of data sheet s2080, and reads quantitative characteristic average drivers stored in the module storage�Oia. After that, the process moves to step S5120.

[0092] In step S5100, the module 100A of the present information, calculates the absolute value of entropy (quantitative index) as the value of the instability on the basis of the absolute value of TTC (time to collision) during braking. After that, the process moves to step S5110. Here, the time to collision (TTC) is the time until the collision with the obstacle, when the vehicle is moving with maintaining a state of motion used during the calculation time before the collision.

[0093] When the condition of the driver is unstable, it is well known that the time of start of braking is delayed. Therefore, it is possible to determine the unstable condition by estimating the time of braking. The deceleration time is used after normalization through the use of statistical data the total braking operation.

For example, when the number of braking operations is N, it is assumed that the TTC during braking is defined as TTC1, TTC2,.... When the average value of the total time of braking is set as µ, and its standard deviation is defined as σ, the normalized values Std braking operations can be expressed as follows.

[0094] Std1=(µ-TTC1)/σ

Std2=(µ-TTC2)/σ

Std3=(µ-TTC3)/σ

...

Stdn=(µ-TTCn)/σ

The average Std (ΣStdn (where n is Zn�the x to 1-n)/N) is used as the degree of instability.

[0095] In step S5110, the module 100A presentation of information reads the average value of TTC (the value preset in the module storage), which, in General, is valid. This value is set to, for example, to a value between 2 and 3.

In step S5120, the module 100A presentation of information determines that the driving condition is unstable when any instability of the first single distribution using quantitative characteristic measure based on the processes of step S5080 and S5090 and instability of the second single distribution using quantitative characteristic measure based on processes of steps S5100 S5110 and satisfies the following conditional expressions. When satisfied the following conditional expression is determined that the driving condition is unstable.

[0096] the Degree of instability, calculated in step S5080 > common quantitative indicator read in step S5090

The degree of instability, calculated in step S5100 > common quantitative indicator read in step S5110

At stages S5080 and S5100 this variant implementation, respectively, are illustrated by cases in which the degree of instability is calculated based on the entropy of taxiing and TTC during braking. Instead, can be used�about any of the driving operation of the driver and the index behavior of the vehicle (in addition, the frequency distribution of the transverse component G and a longitudinal component (G) due to the driving operation.

[0097] In step S5130, when in step S5120 determined that the driving condition is unstable, the module 100A of the present information executes a process of reporting.

Other configurations are identical to the configuration in the first embodiment implementation.

Here, the step S5010 is a module receiving States of motion. Step S5050 is the first module for calculating the distribution of States of motion and the first determining module instability driving. Step S5030 is the module for determining the completion of the training. Stages S5080 and S5120 make up the second determining module instability driving. Step S5040 is the module of choice instability. Stages S5070 and S5130 constitute the module reporting.

[0098] the Functional advantages

In this variant of implementation, the degree of instability is determined based on other indices (such as the transverse component of G when right or left turn) in addition to the steering entropy. In other words, there are two types of single distributions of States of motion (which are calculated from different indexes, respectively), and it is determined that the driving condition is unstable, when it is determined that Liu�th one is unstable.

[0099] In this embodiment, the implementation, the following advantages can be obtained in addition to the advantages described in the first variant implementation.

(1) Other data States of motion used in the second module determining instability driving represent multiple types of data States of motion. The second determining module instability driving evaluates each of multiple distributions of States of a motion based on multiple types of data States of motion as comparative distributions of States of motion, respectively. In other words, the second module determining the instability of the driving assesses the degree of instability of the driving characteristic of multiple quantitative indicators of the distributions of States of motion derived from multiple types of data States of motion.

By evaluating the driver from multiple signals and special signals, you can improve the efficiency definition.

(2) the Second determining module instability driving assesses the degree of instability driving using time-to-collision (TTC).

Through the use of a time before the collision, it is possible to accurately determine the status of the operation deceleration.

[0100] the Sixth variant implementation

Neither�e described a sixth variant of implementation with reference to the accompanying drawings. Elements identical with elements of the first variant of implementation, are denoted by identical reference numbers.

The basic configuration of this variant implementation is identical to the basic configuration in the first embodiment of implementation. In the sixth embodiment of the another process to calculate the degree of instability, when the state of education is defined as a state of incomplete learning is performed by computing a characteristic of the quantitative value of a specific driving scenario.

Fig. 15 is a schematic diagram illustrating a system configuration according to this embodiment of the. In this variant of implementation, information on the intersection is obtained by using the signal indicate rotation or navigation system thus, to determine the specific driving scenario.

[0101] the Following describes the process module 100A representation of information according to this embodiment of the with reference to the block diagram of the sequence of operations of the method shown in Fig. 16.

In step S6010, the following data are obtained as information data regarding the vehicle, which is information regarding the driving operation of the driver and condition of the vehicle due to the driving operation.

The angle PN�attachment, opening degree of the accelerator pedal operating amount of the brake and signal directions of rotation are obtained as information regarding the driving operation of the driver. Signals indicate rotation is used as information for determining the driving scenario.

[0102] the vehicle Speed, the longitudinal component G and the transverse component of G is defined as the state information of the vehicle.

In step S6030, the module 100A presentation of information obtains information on crossroads in the quality of the information environment of the road through the navigation system 7.

In step S6030, the module 100A of the present information defines the state of education through a process identical to the process in step S1030.

[0103] In step S6040, the module 100A presentation of information determines whether or not the state of education status of study completed, on the basis of the degree SD learning through a process identical to the process in step S1040. When the learning is defined as a condition of study completed, the process moves to step S6050. On the other hand, when the learning is defined as a state of incomplete learning, the process moves to step S6070.

In step S6050, the module 100A of reporting calculates the value of RA�ness between the distributions through a process identical to the process in step S1050. In step S6060, the driving state is unstable is determined based on the value of the difference through a process identical to the process in step S1060. After that, the process moves to step S6100.

[0104] on the other hand, in step S6070, the module 100A presentation of information determines the driving scenario (the surroundings of the road). The script right or left turn at the intersection, the scenario you approach an oncoming vehicle, etc. can be considered as the driving scenario, and the scenario for a right or left turn at the intersection is illustrated.

Whether or not the scenario the driving scenario right or left turn at the intersection can be identified by the use of the method of determining the intersection on the navigation chart or the process of determining the intersection on the basis of the indicators or the behavior of the vehicle.

[0105] In step S6080, the module 100A of the present information calculates a characteristic quantity indicator. Method, identical to the process in step S5100, the fifth variant of implementation, is used to compute a characteristic quantity indicator in step S6080. In other words, the characteristic quantity indicator is computed by applying the absolute value of p�peppery component of G instead of braking operation to the process of step S5100.

In step S6090, the module 100A of information presentation compares the characteristic quantity indicator calculated in step S6080, with a pre-defined threshold value for determining determines that the driving condition is unstable, when the characteristic quantity indicator exceeds the threshold value to determine.

[0106] the Threshold value for determination is changed depending on a particular scenario of driving. For example, when a specific driving scenario is a scenario right or left turn at an intersection, the threshold value for determination is set below the thresholds for other driving scenarios.

In step S6100, similarly to the step S1100, the process of reporting is performed when in step S6060 or S6090 determined that the driving condition is unstable.

[0107] Here, the script for right or left turn at the intersection is illustrated as driving scenario, but can be used a scenario approach an oncoming vehicle, etc. In this case, in step S6070, it is determined if approaching or not the vehicle moving ahead of the vehicle. For example, when the distance to the vehicle in front is equal to or less than the pre-defined�enny distance, is determined that the driving scenario is a scenario you approach an oncoming vehicle. In step S6080, executes a process identical to the process of step S5100. In step S6090, the threshold value for determination is set below the thresholds of other driving scenarios, when it is determined that the driving scenario is a scenario you approach an oncoming vehicle.

[0108] Here, the steps S6010 and S6020 constitute the acquisition module States of motion. Step S6050 is the first module for calculating the distribution of States of motion and the first determining module instability driving. Step S6030 is the module for determining the completion of the training. Stages S6070-S6090 make up the second determining module instability driving. Step S6040 is the module of choice instability. Step S6100 module is reporting.

[0109] the Functional advantages

In this embodiment, the implementation is determined by the driving scenario, and then calculates a characteristic quantity indicator at a specific driving scenarios. The unstable state is determined on the basis of a quantitative characteristic of an indicator in a specific driving scenarios (for example, in the scenario of the right or left turn at an intersection or in the scenario of relatively slow moving VPE�EDI vehicle).

In this variant of implementation, the following advantages can be obtained in addition to the advantages of the first variant implementation.

[0110] (1) the Second determining module instability driving includes a module determine the driving scenario, which defines the specific driving scenario and assesses the degree of instability by calculating the characteristic measure based on the data of the driving operation for the specific driving scenario, defined by the definition module of the driving scenario.

By specifying scenarios driving, you can easily reveal a small variation in the operation of driving that occurs in an unstable state.

[0111] (2) the Script right or left turn at the intersection is determined as the specific driving scenario, characterized by a quantitative index is calculated from the characteristics of driving in that time, and the degree of instability is evaluated.

Can accurately determine the condition of the driver using the characteristics of the driving scenario right or left turn at the intersection.

(3) the characteristics of driving in the script right or left turn signal is used the absolute value of the transverse component G.

It is possible to accurately determine the state of behavior at the intersection using cross� component G.

[0112] (4) the Scenario you approach an oncoming vehicle is determined as a specific driving scenario, characterized by a quantitative index is calculated from the driving characteristics during the operation of a slowdown at this time, and the degree of instability is evaluated.

By defining the characteristics of operation of the slowdown for the scenario you approach an oncoming vehicle, can accurately determine the condition of the driver.

(5) driving Characteristics during the operation of the deceleration use the absolute value TTC (time to collision) during braking.

Through the use of time-to-collision, can accurately determine the condition of the driver in scenario you approach an oncoming vehicle.

[0113] This application claims priority of patent application (Japan) room 2011-94343, filed April 20, 2011, the contents of which are fully contained in this document by reference.

Although the present invention is described in relation to the exact number of embodiments, the scope of the present invention is not limited to this, and improvements and modifications of the embodiments on the basis of the above disclosure are obvious to those skilled in the art.

Number LIST of LINKS

[0114] SD OBU degree�"

TTC - time to collision

1 - sensor the degree of opening of the accelerator pedal

2 - sensor working values pressing the brake pedal

3 - angle sensor when taxiing

4 - sensor vehicle speed

5 - sensor for determining the state of the turn signal

6 - instrument panel

7 - navigation system

8 - G-sensor

9 - device for determination of the rider in front of vehicles

10 - speaker

50 - timer

100 controller

100A - module presentation of information

1. The device of providing information for use in a vehicle, comprising:
- the module is receiving States of motion to obtain the data States of motion which includes at least one of a driving operation of the driver and condition of the vehicle;
- the first module for calculating the distribution of States of motion to calculate the set of distributions of States of motion, including the distribution of States of motion for the immediately preceding time range that indicates the current state of the movement, and the distribution of States of motion for a time range that is different from the immediately preceding time range, based on the data States of motion, obtained via the acquisition module States of motion;
- first mo�ul definitions instability driving first to evaluate instability driving based on the value of the difference between the set of distributions of States of motion calculated by the first module for calculating the distribution of States of motion;
- determining module of completion of training to determine that the training is completed when a predetermined learning time elapses from the start of data collection States of motion;
- the second module determining instability driving for the evaluation of the second instability driving by comparing
the distribution of States of a motion for the immediately preceding time range that indicates the current state of the movement and which is calculated based on the data States of motion obtained by means of the acquisition module States of motion with the comparative distribution of the States of motion derived from the other data States of motion deviating from these States of motion for the immediately preceding time range;
- module of instability to select the first instability driving when training completed, and selecting the second instability driving when learning is not completed, based on the result of determination module to determine completion of study; and
module presentation information for presenting the instability on the basis of instability, selected by the module selection instability between the first unstable�TEW and second driving instability driving the driver,
however, other data States of motion are those of the States of motion obtained in a different range of dates that differ from the immediately preceding time range, and
the other time range varies depending on the degree of learning, which is the ratio of the elapsed time from start of data collection state of the movement to date at the time of the study, and another time range is set in such a way that it becomes larger with increasing stephanyboese.

2. The device of providing information for use in a vehicle according to claim 1, wherein the other data States of motion represent many types of data States of motion and
in which the second determining module instability driving computes the set of distributions of States of motion as comparative distribution of States of a motion based on the plurality of data States of motion.

3. The device of providing information for use in a vehicle according to claim 1 or 2, in which the distribution of States is calculated on the basis of the operating amount of the operation of the taxi.

4. The device of providing information for use in a vehicle according to claim 3, in which method on the basis of the steering entropy is used� when calculating on the basis of the operating amount of the operation of the taxi.

5. The device of providing information for use in a vehicle according to claim 1 or 2, wherein the second determining module instability driving second evaluates the instability driving using time-to-collision (TTC).

6. The device of providing information for use in a vehicle according to claim 1 or 2, wherein the module representation of information changes the information instability, which must be submitted, depending on, whether completed or no training.

7. The device of providing information for use in a vehicle, comprising:
- the module is receiving States of motion to obtain the data States of motion which includes at least one of a driving operation of the driver and condition of the vehicle;
- the first module for calculating the distribution of States of motion to calculate the set of distributions of States of motion, including the distribution of States of motion for the immediately preceding time range that indicates the current state of the movement, and the distribution of States of motion for a time range that is different from the immediately preceding time range, based on the data States of motion, obtained via the acquisition module States of motion;
the first determining module instability driving first to evaluate instability driving based on the value of the difference between the set of distributions of States of motion calculated by the first module for calculating the distribution of States of motion;
- determining module of completion of training to determine that the training is completed when a predetermined learning time elapses from the start of data collection States of motion;
- the module for determining the driving scenario to determine if a script is driving a specific driving scenario, pre-set for use when the training is not completed when it is determined that learning is not completed,
the basis of the result of the determination module determining the completion of training;
- the second module determining instability driving for calculating, when it is determined that learning is not completed, based on the result of determination module to determine completion of training module and determine the driving scenario defines a specific driving scenario, characterized by a quantitative indicator of the distribution of States of a motion for the immediately preceding time range that indicates the current state of the movement, based on the data States of motion obtained by means of the acquisition module of States of a motion in a specific driving scenarios defined by the definition module of the driving scenario, and the evaluation of the second instability driving on osnoviyanenko quantitative characteristic of the indicator;
- module of instability to select the first instability driving when training completed, and selecting the second instability driving when learning is not completed, based on the result of determination module to determine completion of study; and
module presentation information for presenting the instability on the basis of instability, selected by the module selection instability instability between the first and second driving instability driving, the driver.

8. The device of providing information for use in a vehicle according to claim 7, in which the second module
definition of instability calculates the driving tendency of the quantitative characteristic measure based on the background characteristic of the quantitative index obtained at regular intervals, and evaluates the second instability driving based on the calculated trends.

9. The device of providing information for use in a vehicle according to claim 7 or 8, in which the right or left turn at the intersection is defined as a specific driving scenario and typical quantitative index is calculated from the characteristics of driving during this time.

10. The device of providing information for use in a vehicle according to claim 9, in which the absolute�nd the magnitude of the transverse component G is used as the driving characteristics for a right or left turn.

11. The device of providing information for use in a vehicle according to claim 7 or 8, in which scenario you approach an oncoming vehicle is determined as a specific driving scenario and typical quantitative index is calculated from the driving characteristics during the operation of a slowdown at this time.

12. The device of providing information for use in a vehicle according to claim 11, in which the absolute value of time to collision (TTC) during braking is used as the driving characteristics during the operation of the deceleration.

13. The device of providing information for use in a vehicle according to claim 7 or 8, in which the second determining module instability driving determines that the state
driving is unstable when the calculated characteristic quantity indicator exceeds a predetermined threshold value to determine.

14. The device of providing information for use in a vehicle according to claim 7 or 8, in which the distribution of States is calculated on the basis of the operating amount of the operation of the taxi.

15. The device of providing information for use in a vehicle according to claim 14, in which method on the basis of entropy taxiing use�is when calculating on the basis of the operating amount of the operation of the taxi.

16. The device of providing information for use in a vehicle according to claim 7 or 8, in which the second determining module instability driving second evaluates the instability driving using time-to-collision (TTC).

17. The device of providing information for use in a vehicle according to claim 7 or 8, in which the module representation of information changes the information instability, which must be submitted, depending on, whether completed or no training.



 

Same patents:

FIELD: transport.

SUBSTANCE: proposed device comprises image capture device mounted at the vehicle to catch the imaged sideways therefrom, nearby vehicle detection unit light projecting candidate object detector projecting the light with luminescence equal to or higher than the first threshold magnitude, unit to detect the causes for revealing if light projecting candidate object exists or not as well as supplier of data to driver on nearby vehicle.

EFFECT: higher accuracy of nearby vehicle.

9 cl, 9 dwg

FIELD: physics, navigation.

SUBSTANCE: invention is intended to prevent collision of vehicles. When monitoring the surrounding space, a first initial state is obtained for a first object (18) located in the surrounding space (14) as a reference point during a first stopping event, wherein the initial state includes an indication of the initial position of the first object (18). During a second stopping event (S1), a determination (S2) is made of whether a vehicle (10) has moved between the first and second stopping events by a given minimum distance and/or whether a given minimum time has passed. The initial state is refreshed (S3) by indicating the position of at least a second object (18) located in the surrounding space (14) as a reference point if it is determined that a vehicle (10) has moved between the first and second stopping events by a distance exceeding the given minimum value and/or that a time greater than the given minimum time has passed. The actual position of the second object (18) is determined (S5) as the actual state for further intent to begin movement. A possible collision situation is detected (S6) if comparison of the actual and the initial state yields a distance greater than a given value. The invention also relates to an anti-collision apparatus.

EFFECT: warning a driver on presence of objects in the surrounding space.

8 cl, 2 dwg

FIELD: transport.

SUBSTANCE: in the first version, the method includes determination of presence of a vehicle moving ahead along adjacent lane in concurrent direction using video camera, measurement of distance to the vehicle with calculation of its side mirrors "blind area" position and rear vehicle position relative to "blind area", as well as determination of its type and dimensions, warning about hazardous proximity using indicators located in instrument cluster, assistance in the process of vehicle driving through vehicle deceleration in case when driver of vehicle moving ahead along adjacent lane begins to execute unsafe manoeuvre. In the second version, monitoring system switches on video camera, ultrasonic sensors and microcontroller. The ultrasonic sensors located on vehicle front bumper determine position of moving ahead vehicle side mirrors "blind area". Microcontroller produces warning signals for driver about vehicle entry into "blind area" of going ahead vehicle.

EFFECT: higher traffic safety due to automatic monitoring of moving ahead vehicle blind area condition.

7 cl, 3 dwg

FIELD: transport.

SUBSTANCE: alert system comprises annunciator of approach to dangerous point connected via communication line with receiver arranged at locomotive, decoder, memory unit, gate multivibrator, three-input OR element, control circuit, reset pulse generator and people presence control pickups.

EFFECT: higher safety.

1 dwg, 1 tbl

FIELD: transport.

SUBSTANCE: invention relates to automotive parking helps. Control device comprises computer to determine the distance to parking point in parking lot for vehicle to overcome, camera to register situation nearby the vehicle, interface for connection to device to output instructions on manoeuvring at motion to parking point, interface for connection with device for manual input of parking space boundaries and interface for connection to means intended for setting relationship between parking space boundaries and vehicle position. Camera is set to view the space ahead of vehicle. Portable computer comprises above described control device. Proposed method comprises registration of situation nearby vehicle by said camera, calculation of distance to parking point in parking lot for vehicle to overcome, outputting of instructions on manoeuvring at motion to parking point Picture produced by camera is used for manual input of parking space boundary to be compared with current position of vehicle.

EFFECT: efficient parking help for driver.

11 cl, 4 dwg

FIELD: transport.

SUBSTANCE: set of inventions relates to device and method for parking assistance. Device contains available parking space recorder, final target parking position setting facility, contact determination facility, final target parking position calculation facility, the first, the second and the third path calculation facility, the first path, the second path and the third path display facility. The contact determination facility provides determination whether there is the first path for vehicle to reach final target parking position without contacting with the edge of available parking space. The method consists in the following: available parking space is recorded, final target parking position is set, existence of the first path for vehicle to reach final target parking position without contacting with the edge of available parking space is determined. The first path is displayed if it exists. Angle of approach and not final target parking position is calculated if the first path does not exist. Not final target parking position is position that should be reached when vehicle is entering available parking space at approach angle. The second and the third paths are calculated and displayed.

EFFECT: higher efficiency of parking assistance.

10 cl, 15 dwg

FIELD: physics.

SUBSTANCE: device according to the first version includes a steering angle detection unit, an operation detection unit in which the steering wheel returns to a neutral position after turning right or left, and a parking mode selection unit. The device according to the second version includes a steering angle detection unit, an operation detection means in which the steering wheel returns to a neutral position after turning right or left, and a parking mode selection means. The method includes detecting the steering angle of the steering wheel, detecting an operation in which the steering wheel returns to a neutral position after turning right or left, selecting one parking mode on the right side of the vehicle or left side of the vehicle from a plurality of parking modes.

EFFECT: easy establishment of the desired parking position.

21 cl, 29 dwg

FIELD: transport.

SUBSTANCE: invention relates to aviation, in particular to methods of navigation assistance for aircraft path determination. Navigation assistance method consists in determination of future approach path using evaluation of forecasted safe radiuses on future path based on calculation of limit moment starting from which a forecasted safe radius exceeds or is equal to alarm signal generation limit and calculation of limit departure moment which corresponds to maximum moment when an aircraft should leave its predetermined path along which it has been moving, to be able to go to safe altitude.

EFFECT: limitation of departure procedures utilisation during loss of satellite navigational information thus reducing airspace saturation and limiting costs and duration of flights.

15 cl, 5 dwg

FIELD: transport.

SUBSTANCE: invention relates to vehicle driving control device. The device contains side object detector, control device to actuate lateral movement inhibition control, start of entering adjacent lane detector, actuation suppressor to suppress actuation of lateral movement inhibition control. Lateral movement is followed by changing vehicle transversal position relative to lane. Suppression of actuation of lateral movement inhibition control is performed when a vehicle has started to enter adjacent traffic lane, and side object is detected in the adjacent traffic lane.

EFFECT: prevention of improper interference in control for vehicle lateral movement inhibition during changing traffic lane.

22 cl, 11 dwg

FIELD: physics, optics.

SUBSTANCE: invention relates to optoelectronic devices to be used at carries as extra detector of objects in areas invisible for driver. Proposed optical sensor comprises transceiving channels, each including electronic unit, pulse source of optical radiation and photoreceiver, all being connected with electronic unit. Said electronic unit comprises reflected signal processor to execute control algorithm on the basis of measurement of time interval between radiation and registration of signal reflected from the object. Optical radiation source and photoreceiver are arranged, preferably, nearby so that their optical axes are parallel. The number of radiators in said sensor is defined by the relationship n ≥ β/α, where n is the number of radiators, β is central angle of controlled sector, α is beam angle.

EFFECT: higher precision and reliability of detection.

1 dwg

FIELD: electricity.

SUBSTANCE: system has RFID tags, stationary information reception and processing units with RFID tag readers connected to them, a memory unit and a controller. RFID tags are added which are configured in telecommunication devices and personal ID cards of employees, mobile information reception and processing units with RFID tag reader, and navigation and data exchange functions through the radio channel, and also the personal information memory module, the measuring bench module, the technical process diagram memory module, the device location register, the register of electronic logs and passports, the device identification register the inputs and outputs of which are connected to the respective outputs / inputs of the added operated switching unit. The radio channel is connected to the high-level data transmission network. The switching unit is connected to the memory unit and the controller, and through the high-level data transmission network - to inputs and outputs of stationary information reception and processing units. Outputs/inputs of hardware-software complexes of the integrated automated control system of financial resources, the automated workstations of the supervisory control personnel and integrated monitoring and administration system are connected to the high-level data transmission network.

EFFECT: improvement of life cycle monitoring and movement of telecommunication devices.

4 cl, 1 dwg

FIELD: transport.

SUBSTANCE: device comprises at least one mike and camera. Note here that it is equipped with measuring plate of polycrystalline material, ultrasound spectral analyser, device for machine identification of the stud strike against the plate in ultrasound band composed of computer, device for machine identification of stud image on tire tread composed of computer to compare the data received by strike identification and stud image identification devices, transducer of vehicle speed on the plate and transducer of moisture on measuring plate surface. Aforesaid camera represents an infrared design while ultrasound mike is secured at measuring plate or rigidly built therein and connected via communication line with ultrasound spectral analyser connected in its turn with stud stride identifier. Both identifiers are connected by their outputs to data comparator.

EFFECT: expanded range of valid and reliable data.

5 cl, 1 dwg

FIELD: physics.

SUBSTANCE: system is equipped with a solar panel, a battery charge controller, a proximity sensor, a GSM/GPRS unit, a control unit, a T7 traffic light, a "Pedestrian crossing" LED sign, LED lamps for lateral illumination of the pedestrian crossing, LED lamps for illuminating the waiting area, a solar panel, a control unit and two rumble strips on the road surface. Two additional LED lamps for lateral illumination of the pedestrian crossing are LED projectors, each offset from the centre axis of the pedestrian crossing towards the approaching vehicle. The control unit controls the state of the sensors, traffic light, LED projectors and the "Pedestrian crossing" LED sign and also transmits an instruction to actuate the control unit located at the opposite side of the road. The program-controlled GSM/GPRS unit enables to perform set up the controllers, automatic recording of readings of operation of basic units or systems and remote control of the entire system.

EFFECT: automating control of the pedestrian crossing, high information value for the driver and improved safety of the pedestrian crossing.

3 dwg

Pedestrian crossing // 2541589

FIELD: transport.

SUBSTANCE: unregulated pedestrian crossing consists of pedestrian path on traffic way of motor road which pedestrian path is marked at edges on sidewalks by road signs. On their pillars, video cameras are installed combined with device for vehicle speed measurement. The video cameras are oriented towards oncoming transport. The video cameras monitor vehicle presence on a road, and vehicle speed measurement device fixes vehicle speed at a distance of 100 and 50 meters from nearest edge of pedestrian crossing. At these distances additional lines of road marking are drawn, and additional informative signs are installed. When traffic regulations violations by vehicle drivers are fixed the vehicle forced stopping device is actuated. This stopping device is located at 5 meters before nearest edge of pedestrian crossing. At the moment of proposed device actuation, headlamp light is reflected by light-reflective coating of suddenly appearing obstacle thus attracting driver's attention.

EFFECT: lower accident rate on unregulated pedestrian crossings.

4 dwg

FIELD: transport.

SUBSTANCE: invention relates to traffic regulation, namely to identification of traffic violation at crossroad identification of traffic violation at crossroad. Following equipment is used: traffic light and viewing cameras, data transmission facility and electronic computing device. Viewing camera is made with possibility to from stop-line zone, as well as image of zone after imaginary line of crossroad. Traffic light camera is made with possibility to from image of traffic light signals, as well as stop-line zone and zone after imaginary line of crossroad. Electronic computing device is made with possibility to obtain and memorise images from traffic light and viewing cameras with possibility to recognise vehicle license plate number. Additionally, computer is made capable to determine violation and generate information for vehicle about traffic violation "about stopping down before stop-line when restrictive signal of traffic light is present" and "riding when restrictive signal of traffic light is present". Data transmission facility transmits information about violation fact to remote information processing centre.

EFFECT: improved functionality, as well as higher efficiency of traffic violation at crossroad identification.

36 cl, 6 dwg

FIELD: physics, computer engineering.

SUBSTANCE: group of inventions relates to public information systems and intelligent transport systems. The method comprises installing an intelligent transport system on vehicle, completely adapting the system to the electrical system of the vehicle, using the system continuously in automatic and manual mode together with cellular communication means, the Internet and navigation satellite systems, and performing video surveillance and monitoring of the vehicle at a distance using cellular equipment which supports 3G technology. The intelligent transport system consists of the following basic interlinked structural components: a video surveillance system, a video recorder, a touch screen display, a processor board with a SIM module, a transceiver, an emergency unit with an antitheft system module, a uninterrupted power supply, a multimedia device, a distance meter and a preventive security system.

EFFECT: achieving comprehensive protection of a vehicle, the driver, passengers, pedestrians and creating favourable traffic conditions overall.

4 cl

FIELD: transport.

SUBSTANCE: procedure comprises detection and identification of carrier ID to memorise its digital representation as letter-symbol line. Said ID letter-symbol line is converted into six-byte code of Wiegand standard interface format to transmit in Wiegand standard protocol to standard serial port RS232/RS485. Obtained identifier is processed in compliance with programmed logic to generated control action for transmission to actuator.

EFFECT: simplified administration and control of access, ruled out duplication of functional unit.

Ringing device // 2518081

FIELD: radio engineering, communication.

SUBSTANCE: invention relates to traffic signalling control devices. The ringing device consists of three textolite plates joined by braces and two spring-loaded slides, wherein the device is mounted on both contact lines using suspension clamps.

EFFECT: designing an automatic ringing device which can be used in trolley bus contact systems.

2 dwg

FIELD: transport.

SUBSTANCE: invention relates to traffic control, particularly, to hardware to prevent automobile-pedestrian accidents. Pedestrian with mobile electronic device, for example, mobile phone analysing traffic conditions by signals of pickups, video cameras and lights receives signals on pedestrian crossing safety level and permissive signals for crossing.

EFFECT: higher safety of crossing, particularly for poor-eyesight and blind pedestrians.

1 dwg

FIELD: physics.

SUBSTANCE: map of the controlled road network is formed; each traffic section is assigned a speed index; up-to-date information is received from fixed sensors on traffic speed on sections of the road network and based on this information, speed indices are corrected; on all sections of the road network, for which there was correction of speed indices based on up-to-date information, the average speed Va is calculated without taking into account the correction as well as the average speed Vb, for each section of the road network for which there was no correction of the speed index based on up-to-date information; the speed index is corrected by multiplying the speed index of each section of the road network by a function Ka(x) of the ratio X; data on the current location of the subscriber and the final point of the route are obtained and the optimum route is calculated.

EFFECT: high degree of optimality of the constructed route, shorter average time for completing a route in heavy traffic conditions.

13 cl

FIELD: transport.

SUBSTANCE: invention relates to agricultural vehicles. Vehicle comprises ICE, transmission to distribute ICE power between running gear and lifting equipment. Controller adjusts at first working mode the engine rpm subject to vehicle speed and changes over to second working mode whereat rpm is adjusted by first control element irrespective of vehicle speed. Additionally, said controller, at no effects on first control element, stays at first working mode while at second working mode it adjusts rpm in compliance with the degree of effects to said first control element.

EFFECT: fuel saving at low rpm and higher efficiency of cargo-handling jobs.

14 cl, 5 dwg

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