CONTROL METHOD FOR A VEHICLE, COMPUTER PROGRAM, NON-TRANSITORY COMPUTER READABLE MEDIUM, AND AUTOMATED DRIVING SYSTEM

Abstract

A control method for a host vehicle (100), comprising a) acquiring a speed (Vx) of the host vehicle, a relative speed (Vr) and distance (Dr) between a preceding vehicle (200) and the host vehicle (100); b) calculating a perceived risk level (PRL) as a function of said speed Vx of the host vehicle, said relative speed Vr, said relative distance Dr, and at least one of variables Vx*Vr and Vx.sup.2; and c) controlling at least one vehicle device (32, 34, 36, 38) of the host vehicle as a function of the perceived risk level (PRL).

A computer program, a non-transitory computer-readable medium, and an automated driving system for implementing the above method.

Claims

1. A control method for a host vehicle, the method comprising the steps of: a) acquiring a speed Vx of the host vehicle, a relative speed Vr between a preceding vehicle and the host vehicle, and a relative distance Dr between the preceding vehicle and the host vehicle; b) calculating a perceived risk level as a function of said speed Vx of the host vehicle, said relative speed Vr, said relative distance Dr, and at least one of variables Vx*Vr and Vx.sup.2; c) controlling at least one vehicle device of the host vehicle as a function of the perceived risk level.

2. A control method according to claim 1, wherein in step b), the perceived risk level PRL is calculated based on equation (1):
PRL=(DrPb*V)/(Pa*V); wherein(1) Pa, Pb are five components vectors; and V=(1; Vx; Vr; Vx*Vr; Vx.sup.2).

3. A control method according to claim 2, wherein Pa and Pb are linked by the following relations: Pa=(Pa1,Pa2,Pa3,Pa4,Pa5) Pb=(Pb1,Pb2,Pb3,Pb4,Pb5) Pa1/Pa2=0.2950.15 Pa2/Pa3=0.2360.12 Pa3/Pa4=8.24.1 Pa4/Pa5=5.722.75 Pb1/Pb2=1.80.9 Pb2/Pb3=3.982 Pb3/Pb4=32.616.3 Pb4/Pb5=1.770.89 in which Pa1, . . . Pa5 and Pb1, . . . Pb5 are real numbers.

4. A control method according to claim 2, wherein: Pa=(0.000940.00047; 0.00320.0016; 0.0140.007; 0.00170.00085; 0.000290.00015); and Pb=(4.52.25; 2.51.25; 0.620.31; 0.0190.009; 0.0110.0055).

5. A control method according to claim 1, wherein in step c), controlling said at least one vehicle device includes controlling said at least one vehicle device as a function of a difference between the perceived risk level and a predetermined maximum acceptable risk level.

6. A control method according to claim 1, wherein step c) includes actuating at least one driving actuator among said at least one vehicle device when the perceived risk level PRL exceeds a predetermined value.

7. A control method according to claim 1, wherein said at least one vehicle device includes at least one brake and/or at least one other driving actuator.

8. A computer program which is stored on a non-transitory computer-readable medium, and which is suitable for being executed by a processor, the program including instructions adapted to perform the control method according to claim 1 when it is executed by a processor.

9. A non-transitory computer-readable medium storing instructions that, when executed by a processor, cause the processor to perform the control method of claim 1.

10. An automated driving system for a host vehicle, the automated driving system comprising an electronic control unit configured a) to acquire a speed Vx of the host vehicle, a relative speed Vr between a preceding vehicle and the host vehicle, and a relative distance Dr between the preceding vehicle and the host vehicle; b) to calculate a perceived risk level as a function of said speed Vx of the host vehicle, said relative speed Vr, said relative distance Dr, and at least one of parameters Vx*Vr and Vx.sup.2; c) to control at least one vehicle device of the host vehicle as a function of the perceived risk level.

11. An automated driving system according to claim 10, wherein the control unit is configured to calculate the perceived risk level PRL based on equation (1):
PRL=(DrPb*V)/(Pa*V); wherein(1) Pa, Pb are five components vectors; and V=(1; Vx; Vr; Vx*Vr; Vx.sup.2).

12. An automated driving system according to claim 11, wherein Pa and Pb are linked by the following relations: Pa=(Pa1,Pa2,Pa3,Pa4,Pa5) Pb=(Pb1,Pb2,Pb3,Pb4,Pb5) Pa1/Pa2=0.2950.15 Pa2/Pa3=0.2360.12 Pa3/Pa4=8.24.1 Pa4/Pa5=5.722.75 Pb1/Pb2=1.80.9 Pb2/Pb3=3.982 Pb3/Pb4=32.616.3 Pb4/Pb5=1.770.89 in which Pa1, . . . Pa5 and Pb1, . . . Pb5 are real numbers.

13. An automated driving system according to claim 11, wherein Pa=(0.000940.00047; 0.00320.0016; 0.0140.007; 0.00170.00085; 0.000290.00015); and Pb=(4.52.25; 2.51.25; 0.620.31; 0.0190.009; 0.0110.0055).

14. An automated driving system according to claim 10, wherein the electronic control unit is configured, for controlling said at least one vehicle device, to control said at least one vehicle device as a function of a difference between the perceived risk level and a predetermined maximum acceptable risk level.

15. An automated driving system according to claim 10, wherein the electronic control unit is configured to actuate at least one driving actuator among said at least one vehicle device when the perceived risk level PRL exceeds a predetermined value.

16. An automated driving system according to claim 10, wherein said at least one vehicle device includes at least one brake and/or at least one other driving actuator.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0097] The present invention may be better understood and its advantages will become apparent to those skilled in the art by reference to the accompanying figures in which:

[0098] FIG. 1 is a schematic drawing of a vehicle equipped with an automated driving system according to the present disclosure, represented behind a preceding vehicle;

[0099] FIG. 2 is a flowchart illustrating a vehicle control method according to the present disclosure;

[0100] FIG. 3 is a flowchart illustrating a vehicle control method according to the present disclosure, in the specific case of braking control;

[0101] FIG. 4 is a drawing illustrating a database of braking records;

[0102] FIG. 5 is a drawing illustrating the database of braking records, wherein groups have been formed based on relative speed and vehicle speed, and each group is represented by a point;

[0103] FIG. 6 is a plot showing the variations of the perceived risk level as a function of the speed of a host vehicle, for various groups of drivers; and

[0104] FIG. 7 is a plot showing the variations of the perceived risk level as a function of the relative speed of a preceding vehicle with respect to the host vehicle, for the same groups of drivers.

DESCRIPTION OF THE EMBODIMENTS

[0105] An automated driving system 10 in which the above-mentioned method for controlling the vehicle is implemented is now going to be described.

[0106] FIG. 1 schematically represents a car 100 (an example of a host vehicle) equipped with the automated driving system 10 which forms an exemplary embodiment of the present disclosure. Car 100 follows a preceding vehicle 200. Both vehicles move in the direction shown by arrow A. The host vehicle and the preceding vehicle are separated by a relative distance Dr (Distance Dr appears proportionally much shorter on FIG. 1 than what it is in reality).

[0107] The automated driving system 10 (or, in short, the system 10) is, in the present case, an automated driving system comprising an electronic control unit 20 and several sensor units collectively referenced 30, comprising several cameras, a lidar unit, a set of radars, a close range sonar sensor unit, a GPS unit, a radio communication system for communicating with the infrastructure and/or with other vehicles, and a speed sensor measuring the speed Vx of the vehicle.

[0108] The radars of the set of radars in particular measure the relative speed Vr between the preceding vehicle 200 and the host vehicle 100.

[0109] All the above-mentioned sensor units 30 are connected to the electronic control unit 20 (ECU 20).

[0110] The ECU 20 has globally the hardware architecture of a computer. The ECU 20 comprises a microprocessor 22, a random access memory (RAM) 24, a read only memory (ROM) 26, an interface 28.

[0111] The hardware elements of ECU 20 are optionally shared with other units of the automated driving system 10 and/or other systems of the car 100.

[0112] The interface 28 includes in particular a tactile display and various displays mounted in or on the dashboard of the car.

[0113] The interface 28 therefore comprises a driver interface with a (not-shown) display to transmit information to the driver of the car 100, and interface connections with actuators and other vehicle devices of the car. In particular, interface 28 comprises a connection with several driving actuators of the car. These driving actuators include the engine 32, the steering column 34, the brakes 36, and the transmission 38.

[0114] The ECU 20 transmits torque requests to the engine ECU, and engagement controls to the respective engagement elements (e.g. clutches) of the transmission 38. Based on these controls, the engine ECU controls the torque delivered by the engine 32 and the transmission adopts the desired configuration, whereby the desired acceleration is imparted to the car.

[0115] A computer program configured to partly assume the driving task by performing lateral and longitudinal control of the vehicle is stored in memory 26.

[0116] This program is configured to calculate the controls which, at least during some driving periods, control the driving actuators of the host vehicle.

[0117] This program, and the memory 26, are examples respectively of a computer program and a non-transitory computer-readable medium pursuant to the invention.

[0118] The read-only memory 26 of the ECU 20 indeed constitutes a non-transitory computer readable medium according to the invention, readable by the processor 22. It stores instructions which, when executed by a processor, cause the processor 22 to perform the control method according to the present invention.

[0119] More specifically, the program stored in memory 26 includes instructions for executing a method for controlling the driving actuators 32, 34, 36 and 38 as a function of the perceived risk level PRL.

[0120] The automated driving system 10 is designed to handle the driving tasks only under the constant supervision of the driver. System 10 is thus considered as an automated driving system of level 2 pursuant to SAE norm J3016. The present disclosure however can be implemented on automated driving systems of any level from 1 to 5.

[0121] To perform its function, system 10 uses data provided by sensors 30, processes the data in ECU 20, and controls the driving actuators of the car on the basis of controls calculated by ECU 20. In addition, information exchange between the vehicle 100 and external devices via interface 28 may also possibly take place to improve the performance of system 10.

[0122] As mentioned above, the ECU issues controls to control the actuators of car 100; these controls are calculated as a function of a perceived risk level PRL.

[0123] In accordance with the present disclosure, the vehicle 100 can controlled during driving for instance pursuant to the control method illustrated by FIG. 2.

[0124] In this method, in a first step a), the relative speed Vr and the relative distance Dr between the host vehicle 100 and a preceding vehicle 200 are acquired by ECU 20, based on radar information provided by the radars of sensors 30.

[0125] The host vehicle speed Vx is acquired from the speed sensor of sensors 30.

[0126] Then, at step b), the perceived risk level PRL is calculated.

[0127] The perceived risk PRL can only be calculated in a situation where the host vehicle 100 is following a preceding vehicle 200, as illustrated on FIG. 1.

[0128] The perceived risk level PRL is calculated based on the speed of the host vehicle Vx, the relative speed between the host vehicle and the preceding vehicle Vr and the relative distance between the host vehicle and the preceding vehicle Dr using equation (1):

PRL=(DrPb*V)/(Pa*V); wherein(1)

[0129] Pa=(0.00094; 0.0032; 0.014; 0.0017; 0.00029);

[0130] Pb=(4.5; 2.5; 0.62; 0.019; 0.011); and

[0131] V=(1; Vx; Vr; Vx*Vr; Vx.sup.2).

[0132] In this exemplary embodiment of a control method pursuant to the present disclosure, for each component of each of vectors Pa and Pb, the central or mean value of the available range of values for each component is used. However, it is possible to use other values of these parameters.

[0133] For instance, for the first component of the vector Pa, any value equal to 0.000940.0047 can be used. The same rationale applies to the other components of vector Pa, and to the components of vector Pb.

[0134] Then, in a third step c), one or more driving actuators of the host vehicle 100 are controlled as a function of the perceived risk level PRL. For instance, the brakes 36 can be applied; the timing (or the distance Dr) at which braking is triggered is determined based on the perceived risk level PRL. Usually, the driving system 10 is configured to modify the braking force, the acceleration or torque of the engine, and/or the steering angle of the vehicle based on the perceived risk level.

[0135] The algorithm is carried out iteratively at regular time steps. After controls have been issued for the various driving actuators at step c), the algorithm is resumed at step a).

[0136] Another and more specific exemplary control method of a vehicle in accordance with the present disclosure will now be described in relation with FIG. 3.

[0137] In this embodiment, the driver of vehicle 100 has the possibility to specify the maximum accepted risk level (MRL) which is the maximum risk to which he or she wants to be exposed while the automated driving system 10 drives the car.

[0138] Based on this parameter, in vehicle 100, the controls sent to the driving actuators 32, 34, 36 and 38 take into account the difference between the calculated perceived risk level PRL, and the desired perceived risk level MRL specified by the driver.

[0139] By this setting, the driver can request the driving system to adopt a more or less aggressive driving style.

[0140] The control of vehicle 100 is realized by ECU 20 which executes an algorithm substantially identical to the algorithm of FIG. 2.

[0141] The first steps a) and b) of this algorithm are identical to steps a) and b) of the preceding method.

[0142] However, in this embodiment, step c) of controlling the driving actuators is carried out as follows in two steps c1) and c2).

[0143] Beforehand, in a step c0), the user of the vehicle is requested to input the maximum risk level MRL he or she is willing to accept during the trip, and which he or she considers acceptable.

[0144] During the trip, each time a preceding vehicle is detected in front of the host vehicle in the same lane as the host vehicle, the perceived risk level PRL is calculated at step b).

[0145] Then at a step c1), the perceived risk level PRL is compared to the maximum risk level MRL previously inputted by the user of the vehicle. That is, the difference between PRL and MRL (PRLMRL) is calculated.

[0146] If this difference is negative, that is, if the perceived risk level PRL does not exceed the maximum risk level MRL, no further action is taken and the algorithm jumps to step a), which is carried out at the next time step.

[0147] Conversely, if this difference is positive, that is, the perceived risk PRL exceeds the maximum risk level MRL, then the algorithm jumps to step c2). In step c2), the control unit 20 controls the brakes 36 to be applied. That is, in this latter case a control value is outputted by the control unit 20 and, based on this value, the brakes 36 are applied.

[0148] The control method illustrated by FIGS. 2 and 3 are only exemplary embodiments of the present disclosure.

[0149] More generally, as mentioned before, many different functions or systems of a car or a road vehicle can be controlled based on the perceived risk level. Usually, the driving system of the vehicle is configured to modify the braking force, the acceleration or torque of the engine, and/or the steering angle of the vehicle based on the perceived risk level. The driving system of the vehicle however may also trigger warning signals (visual, audible, haptic) based on the perceived risk level. Accordingly, the devices used to emit said visual, audible and/or haptic signal are other examples of vehicle devices which can be controlled based on the PRL parameter, in accordance with the present disclosure.

[0150] Although the present invention has been presented above with a PRL function based on equation (1), and with specific values of Pa and Pb, the invention is by no means limited to this specific value of the PRL function, the equation (1), and/or these specific values of Pa and Pb. The invention can be implemented with many different PRL functions.

[0151] In the development of a control system for a vehicle, if using a specific PRL function is considered, it is possible to check whether this function provides an effective value of the PRL parameter by using the following verification method (FIGS. 4 to 7).

a) Database Establishment

[0152] First, a database of exemplary brakings by drivers in representative driving situations is constituted.

[0153] This database contains records of brake applications having taken place during driving. For each brake application, the record of the database includes at least the following information: the vehicle speed Vx, the relative speed Vr and the relative distance Dr between the host vehicle and the preceding vehicle at the time the brakes were applied.

[0154] The database of braking records is represented on FIGS. 4 and 5. Each point of FIG. 4 represents a braking event which has been recorded for a vehicle. All these braking events are plotted in an axis system comprising the Vehicle speed Vx, the relative speed between the vehicle and the preceding vehicle, Vr, and the relative distance between the two vehicles, Dr.

b) Establishment of Data Groups

[0155] The braking records are then grouped based on relative speed Vr and vehicle speed Vx. For instance, the total range of speeds of the relative speeds is divided into ten ranges Vri (i=1 . . . 10); similarly, the total range of speeds of the vehicle speeds is divided into ten ranges Vxj (j=1 . . . 10).

[0156] The groups (Vri,Vxj) obtained in this manner are shown on FIG. 5. Each point is represented by a dot showing the mean relative distance for the group.

[0157] Then, in each group (Vri,Vxj) the braking records are grouped again in deciles based on relative distance Dr when braking is triggered, thus forming per-decile-groups (Vri,Vxi,k).

[0158] Deciles k can be for instance referenced by a parameter k, with k=0 to 9, corresponding respectively to groups 0-10%, 10-20%, . . . , 90-100%.

[0159] (Of course, any granularity can be chosen for the assessment. Centiles could be chosen rather than deciles, for instance).

[0160] These deciles correspond respectively to the brakings of ten groups of drivers (the ten deciles) ranging from the less risk-adverse drivers (small relative distances Dr at braking) to the most risk-adverse drivers (high relative distances Dr at braking).

c) Plotting PRL as a Function of Vx and Vr

[0161] Then, the curves representing PRL as a function respectively of the speed of the host vehicle Vx and the relative speed Vr are drawn (FIGS. 6 and 7). As an example, such curves have been drawn based on the exemplary PRL functions and the values of Pa and Pb proposed above.

[0162] In FIGS. 6 and 7, the abscissa represents respectively Vx and Vr, and the ordinate represents PRL. Each of these figures shows nine curves corresponding to the nine deciles having the lowest relative distance at braking (conversely, the decile (k=0) representing the most prudent drivers is not represented).

[0163] Based on FIGS. 6 and 7, it can be noted that for any given decile, the value of PRL is substantially constant, regardless of the value of the abscissa.

[0164] Since the exemplary PRL functions proposed above exhibit this feature, it can be concluded that these PRL function provide a satisfactory evaluation of the perceived risk level PRL for drivers having a similar driving behaviour as the drivers represented by the braking database.