EP4185504A1

EP4185504A1 - Method for determining a trajectory of a motor vehicle

Info

Publication number: EP4185504A1
Application number: EP21742847.3A
Authority: EP
Inventors: Anh-Lam Do; Thierry HERMITTE; Christian Laugier; Philippe MARTINET; Luiz-Alberto SERAFIM-GUARDINI; Anne SPALANZANI
Original assignee: Renault SAS; Institut National de Recherche en Informatique et en Automatique INRIA
Current assignee: Ampere Sas; Institut National de Recherche en Informatique et en Automatique INRIA
Priority date: 2020-07-23
Filing date: 2021-07-21
Publication date: 2023-05-31
Also published as: FR3112746A1; US20230365131A1; WO2022018110A1; FR3112746B1

Abstract

The invention relates to a method for determining a trajectory of a motor vehicle (100) characterised in that it comprises the steps of: - identifying a plurality of objects (101, 102, 200, 201, 300) present in the surroundings of the motor vehicle (100), - for each object: a) determining a speed of impact between the object of interest and the motor vehicle, b) determining a risk of injury in the event of a collision with the motor vehicle at the determined impact speed, c) determining the probability of a collision resulting in an injury between the object of interest and the motor vehicle, depending on the determined risk of injury, subsequently - determining a plurality of possible trajectories for the motor vehicle, and - determining the trajectory to be followed by the motor vehicle by optimising a cost function which depends on the determined collision probabilities and which minimises the risk of collision resulting in an injury between each object and the motor vehicle.

Description

DESCRIPTION

TITLE OF THE INVENTION: METHOD FOR DETERMINING A TRAJECTORY OF A VEHICLE

AUTOMOTIVE

TECHNICAL FIELD OF THE INVENTION

The present invention generally relates to vehicle safety, in particular with the aim of avoiding collisions between a vehicle and an object present in its environment or of minimizing the severity of collisions if the latter are not avoidable.

[0002] It relates more particularly to a method for determining a trajectory of a motor vehicle.

STATE OF THE ART

[0003] Many vehicles today are equipped with active safety systems making it possible to assess the environment in which the vehicle is moving. Among these systems, we can cite driving assistance systems (“Advanced Driver-Assistance Systems” or ADAS according to the acronym of Anglo-Saxon origin commonly used) which will activate, for example, autonomous emergency braking (“ Advanced Emergency Braking or AEB) or Autonomous Emergency Steering or AES.

[0004] If a danger arises in the environment of the vehicle, this type of system generally intervenes only as a last resort, first by alerting the driver of the vehicle, then, in the event of no response from the latter, ci, by correcting the kinematics (speed and/or trajectory) of the vehicle.

[0005] New collision risk mitigation methods are being developed with the aim of minimizing the severity of the collision when it is unavoidable.

[0006] In this context, document EP3342663 describes a pedestrian detection system and a method for mitigating the effects of collision between a vehicle and a pedestrian (detected by the detection system). On the basis of information collected such as the speed of the vehicle, the distance and the relative speed between the detected pedestrian and the vehicle, the method proposes to generate an alert concerning an imminent collision for the attention of the driver. Following this alert, emergency braking is automatically activated. The solution proposed in this document suffers from two drawbacks. The first is that it only applies to collisions between the vehicle and pedestrians. The second is that it does not reduce the severity of injuries in the event of a collision as much as desired.

PRESENTATION OF THE INVENTION

The present invention proposes to improve the trajectory determination method in the case of a risk of collision by minimizing the overall risk of injury between the motor vehicle and an object present in its environment. [0009] More particularly, according to the invention, a method for determining a trajectory of a motor vehicle is proposed which comprises steps of, each of the steps being implemented in a computer (140) included in the motor vehicle:

- identification of a plurality of objects present in the environment of the motor vehicle,

- for each object: a) determination of an impact speed between the object concerned and the motor vehicle, b) determination of a risk of injury in the event of a collision with said motor vehicle at the determined impact speed, c) determination of a probability of collision causing injury between the object concerned and the motor vehicle, depending on the risk of injury determined, then

- determination of a trajectory to be taken by the motor vehicle by optimizing a cost function which depends on the probabilities of collision determined and which makes it possible to minimize the risk of collision causing an injury between each object and the motor vehicle.

[0010] Thus, the trajectory to be taken by the motor vehicle is determined by taking into account a risk of injury in the event of an unavoidable collision. More particularly, for each object present in the environment of the motor vehicle, the probability of collision with injury is taken into account in order to determine a trajectory for the vehicle which minimizes this risk of collision with injury and which, in the event of an unavoidable collision, minimize injuries.

[0011] Other advantageous and non-limiting characteristics of the method of determination of a trajectory of a motor vehicle according to the invention, taken individually or according to all the technically possible combinations, are the following:

- said probability of collision is a function of the distance separating the motor vehicle and said object, said probability of collision being preferably calculated according to a probability of occupation of a cell of a grid by said object, said grid comprising a plurality of cells representing the environment of the motor vehicle;

- the risk of injury associated with each object is determined according to a plurality of data which respectively correspond to probabilities that the injury is more or less serious, each probability depending on the nature of the identified object, the nature of the the object being chosen from a set comprising at least the elements “pedestrian” and “car”;

- said plurality of data comprises a probability of death, a probability of serious injury and a probability of slight injury;

- the risk of injury associated with each object is determined by calculating a weighted sum of the probabilities of death, serious injury and minor injury associated with the determined impact speed;

- the cost function depends on the kinematic data of the motor vehicle, the distance between the motor vehicle and each object and the probabilities of collision determined for each object;

- the optimization of the cost function is carried out in such a way as to respect at least one constraint relating to the dynamic characteristics of the motor vehicle;

the step of determining the trajectory to be taken by the motor vehicle is based on a minimization of the cost function, said cost function being all the higher as the risks of injury caused in the event of a collision are great;

- the trajectory to be taken is displayed on a screen inside the motor vehicle intended for its driver and/or is transmitted to an autonomous driving module;

- provision is made for an alert step for the driver of the motor vehicle implemented by the computer according to the risk of collision or injury caused by a collision between an object and the motor vehicle;

- the alert step is performed if the cost function is below a threshold predetermined;

- the alert step includes the emission of an audible or haptic or visual alert. Of course, the different characteristics, variants and embodiments of the invention can be associated with each other in various combinations insofar as they are not incompatible or exclusive of each other.

DETAILED DESCRIPTION OF THE INVENTION

The following description with reference to the accompanying drawings, given by way of non-limiting examples, will make it clear what the invention consists of and how it can be implemented.

[0014] In the accompanying drawings:

[0015] [Fig. 1] is a schematic view of a motor vehicle adapted to implement a determination method according to the invention;

[0016] [Fig. 2] is a representation of the “bicycle” model applied to the motor vehicle moving in a traffic lane;

[0017] [Fig. 3] is a schematic representation of the environment of the motor vehicle;

[0018] [Fig. 4] represents, in the form of a flowchart, an example of a method in accordance with the invention;

[0019] [Fig. 5] is an example of an injury curve in the case of a collision between a pedestrian and a motor vehicle; and

[0020] [Fig. 6] is an example of possible trajectories for the motor vehicle. [0021] In Figure 1, there is shown a motor vehicle 100 seen from above. As shown in this figure, the motor vehicle 100 is here a classic car, comprising a chassis which is supported by wheels and which itself supports various equipment including a powertrain, braking means, and a steering unit.

[0023] It may be a manually operated vehicle, in which case the latter will be equipped with means for transmitting information to the driver, or an autonomous vehicle. We will consider in the following that it is an autonomous vehicle, that is to say a vehicle having the capacity to evolve in its environment without driver intervention.

[0024] This motor vehicle 100 is equipped with sensors allowing it to locate in its environment in order for example to be able to pilot itself in an autonomous way or to evaluate its environment.

Any type of sensor could be used.

In the example shown in Figure 1, the motor vehicle 100 is equipped with a camera 130 facing the front of the motor vehicle 100 to capture images of the environment located in front of the motor vehicle 100. This camera 130 is for example positioned at a high central part of the windshield in the passenger compartment of the motor vehicle 100.

The motor vehicle 100 is also equipped with at least one telemetry sensor (RADAR, LIDAR or SONAR). It is more precisely here equipped with five RADAR sensors 121, 122, 123, 124, 125 located at the four corners of the motor vehicle and in the front central position of the motor vehicle.

The motor vehicle 100 is also equipped with a geolocation system 141, comprising for example a GNSS receiver (typically a GPS sensor). In order to process the information provided by these various components, the motor vehicle 100 is equipped with a computer 140.

This computer 140 comprises a processor (CPU), an internal memory, analog-digital converters, and various input and/or output interfaces.

[0031] Thanks to its input interfaces, the computer 140 is suitable for receiving input signals from the various sensors.

The computer 140 is also connected to an external memory 142 which stores various data such as, for example, predetermined data which will be presented below.

The internal memory of the computer 140 stores for its part a computer application, consisting of computer programs comprising instructions whose execution by the processor allows the implementation by the computer 140 of the method described below.

Finally, thanks to its output interfaces, the computer 140 is suitable for transmitting instructions to various parts of the motor vehicle. These components are, for example, a power steering actuator, a brake actuator, an enclosure located in the passenger compartment of the vehicle, a display screen located in the passenger compartment of the vehicle, a vibrating motor located in the steering wheel of the vehicle.

[0035] The trajectory of the motor vehicle 100 is modeled here by a so-called model " bicycle ". Within the framework of this model, as visible in FIG. 2, the motor vehicle 100 is represented by a frame and two wheels 150, 152 (as for a bicycle).

The model is described by the following system of equations:

[0037] [Math. 1]

The variables considered in this model are as follows

- the variables X and Y which correspond to the coordinates of the center of gravity of the motor vehicle 100 in the reference (O, X, Y) (represented in FIG. 2),

- the variables V and dV/dt which correspond respectively to the speed and the acceleration of the motor vehicle 100,

- a steering angle, denoted d, of the front wheel 150, that is to say the angle that the front wheel 150 makes with the longitudinal axis of the motor vehicle 100,

- the variable dé/dt corresponding to the rate of variation of the steering angle,

- the variable d0/dt corresponding to the steering speed associated with the front wheel 150,

- the variable b(d) defined as follows:

[0039] [Math. 2]?05) = atan (tan ( _< 5^)), with If and l _r the respective distances between the center of gravity of the motor vehicle 100 and the front axle and between the center of gravity of the motor vehicle 100 and the axle back,

- a yaw rate, denoted dip/dt, of the motor vehicle 100, corresponding to the speed of rotation of the motor vehicle 100 around its center of gravity along a vertical axis, and

- a heading angle, denoted y, corresponding to the angle, called yaw, between the axis of the motor vehicle 100 and the tangent to the trajectory.

[0040] More details on this model can be found in the document "Coherence and stability of hierarchical planning and control systems for automated driving" by Philip Polack, Doctoral Thesis of the Paris Sciences and Letters Research University, 2018. The trajectory of the motor vehicle 100 is therefore defined by the set x defined by the equation:

[0042] [Math. 3] x(ί) = [X(t), Y(t),xl (t),V(t),δ(t)]

For the remainder of the presentation, the variable u(t) = [dV/dt, d5/dt] is also defined.

The computer 140 is adapted to implement the method for determining a trajectory of the motor vehicle 100.

For the description of this method, the situation shown in Figure 3 is considered as an illustrative example. The motor vehicle 100 is moving on a road on which two other motor vehicles 101, 102 are also traveling. Two pedestrians 200, 201 are present on the sidewalk along the traffic road. Finally, an immobile object 300, for example a stud 300, is placed on the roadway of the motor vehicle 100. This entire scene therefore constitutes the environment of the motor vehicle 100.

The method executed by the computer 140 is adapted to determine the trajectory that the motor vehicle 100 can take in order to minimize the risk of collision causing injuries with the other protagonists. More particularly, this method aims to minimize the risk of collision causing injuries between the motor vehicle 100 and the other protagonists of the road. For this, the computer 140 implements a method comprising several steps, which are described below.

The succession of steps implemented in the context of this process is shown in Figure 4 in the form of a flowchart.

Prior to the implementation of the method, it is assumed that the motor vehicle is moving on a traffic road, as in the example of Figure 3.

As shown in Figure 4, the method begins during step E2 by determining a representation of the environment of the motor vehicle 100. As can be seen in Figure 6, this representation of the environment of the motor vehicle 100 is here made in the form of a grid formed of a plurality of cells.

[0051] The environment of the vehicle is therefore defined here as the whole of the zone illustrated by the grid. This zone here preferably has a shape rectangular, of predetermined length and width. It can be of any other shape, for example of circular shape. The center of this zone is here located at the front of the vehicle. The characteristic dimensions of this zone (for example the length and the width in the case of a zone of rectangular shape) can in particular vary according to the speed of movement of the motor vehicle 100.

This representation comprises a set of data characterizing the motor vehicle 100, for example the location of the motor vehicle 100, the kinematic data linked to the latter such as for example its speed of movement, its acceleration, etc.

During the next step E4, the computer 140 identifies, from the data transmitted by the various sensors, a plurality of objects which are present in the environment of the motor vehicle 100. In the example represented on the Figure 3, the computer 140 identifies, during this step E4, the two other motor vehicles 101, 102, the two pedestrians 200, 201 and the object 300.

During this step, the data concerning these identified objects, in particular their location, their direction and their speed of movement, are then added to the representation of the environment of the motor vehicle 100.

[0055] The next step E6 then consists of determining, for each identified object in step E4, the Vi _mpact impact velocity between the object concerned and the vehicle 100. In the example of Figure 3, five impact speeds are therefore determined during this step E6 (motor vehicle 100-motor vehicle 101, motor vehicle 100-motor vehicle 102, motor vehicle 100-pedestrian 200, motor vehicle 100-pedestrian 201 and motor vehicle 100- object 300).

[0056] The determination of each impact velocity Vi _mpact is based on the "bicycle" model introduced above. It is determined according to the data included in the representation generated in step E2. It is expressed in the form:

[0057] [Math. 4] with v re _l ative the relative speed of movement of the motor vehicle 100 with respect to the object concerned (by calculating the impact speed), a _V hc the acceleration of the motor vehicle 100 and TTC, the collision time (or “Time-to-collision”) between the object concerned and the motor vehicle 100.

The relative speed of movement and the acceleration of the motor vehicle 100 are obtained thanks to the “bicycle” model introduced previously and are contained in the representation of the environment determined in step E2.

The collision time TTC is evaluated from a two-dimensional model, from the speed vectors of the object concerned and of the motor vehicle 100. The method of calculating the collision time TTC used is described further in detail in the document “On computing time-to-collision for automation scenarios”, C. Schwarz, Transportation Research Part F: Traffic Psychology and Behaviour, Vehicle Automation and Driver Behavior, vol. 27, p. 283-294, 2014.

As shown in Figure 4, the method continues with a step E8. During this step, the computer 140 determines, for each object concerned, a risk of injury r associated with each type of referenced object. For example here, a risk of injury (r _V hc) is determined for the vehicle-vehicle collision, another (r _P ieton) for the vehicle-pedestrian collision and another (r ₀ bj) for the vehicle-stud collision knowing that this risk of injury is determined for each impact speed determined in step E6.

In practice, the risk of injury associated with each object is determined according to a plurality of data associated with each object. These predetermined data come from injury risk curves constructed from statistical accident analysis data. FIG. 5 represents an example of an injury risk curve in the event of a collision between a motor vehicle and a pedestrian. The injury risk curves are particular in the document "A tool for Assessment to pedestrian safety: Risk curves by injury severity and Their confidence intervals for car-to-pedestrian collision front" S. Cooney, E. ^The squire, T Hermitte, N. Bertholon, and H. Chajmowicz, IRCOBI 2018 or the document “Association for the Advancement of Automotive Medicine: The Abbreviated Injury Scale 1990 Revision - Update 98. 1998', Barrington, Association for the Advancement of Automotive Medicine.

[0062] The injury risk curves represent the probability of injury as a function of impact velocity. In particular, the data from these injury risk curves correspond to an illustration of the variation in the severity of the injury caused by the collision as a function of the impact speed.

[0063] These injury risk curves are established here, on the basis of models statistics, based on French accident analysis data and distinguish the probability of minor injuries (requiring less than 24 hours or no hospitalization), serious (requiring 24 hours to 30 days of hospitalization) or fatal (related to an accident within 30 days).

[0064] FIG. 5 therefore represents the respective variation in the probability of death (curve a), the probability of serious injury (curve b) and the probability of slight injury (curve c) as a function of the impact speed in the event of a collision between a motor vehicle and a pedestrian. Each of the curves a, b and c is surrounded by two other curves which illustrate the uncertainties associated with it.

The three curves a, b and c have been represented here in the case of a collision between a motor vehicle and a pedestrian. Other curves a, b and c of different shapes are also determined, in the same way, for the case of a collision between two motor vehicles and/or for the case of a collision between a motor vehicle and a fixed infrastructure of the environment. We could also consider obtaining other curves for other scenarios (collision with a bicycle, with a motorcycle, etc.).

The risk of injury associated with each type of object (pedestrian, vehicle, infrastructure, etc.) is determined on the basis of these injury risk curves and as a function of the determined impact speed. More specifically, the risk of injury associated with each type of referenced object is determined by calculating a weighted sum of the probabilities of death, serious injury and minor injury associated with the determined impact velocity.

For example, in the case of a collision between a motor vehicle and a pedestrian, at the determined impact speed, the associated risk of injury is defined by the following formula:

[0068] [Math. 5]

T pedestrian ^~ ^dec- Pdec ^blg - Pblg ^bll - Pbll with p _d ec, P _big and p _bii respectively the probabilities of death, serious injury and light injury associated with the determined impact speed (and resulting from injury risk curves previously described) and W _d ec, W _big and W _bii of the respective weights associated with these probabilities.

The values of these different weighting weights are determined according to social and ethical parameters. For example, they will allow you to put priority on some objects over others. For example, it is possible to prioritize a collision with fixed infrastructure or a motor vehicle over a collision with pedestrians or cyclists.

These weighting weights make it possible to reflect the real situation of collisions between the different types of objects. For example, in a collision between a motor vehicle and a pedestrian at an impact speed greater than 80 km/h, the probability of death of the pedestrian is very high compared to the probability of minor injury or serious injury. These observed facts can therefore be translated into the determination of the risk of injury, for example by assigning a higher weight to the probability of death compared to the probabilities of serious injuries and minor injuries in the event of a collision between a motor vehicle and a pedestrian at high impact speed.

In practice, the weighting weights are for example between 0 and 3. Preferably, they can be between 0 and 1 so as to obtain a normalized risk of injury.

As shown in Figure 4, the method continues with step E10 during which is determined, for each object identified in step E4, a probability of collision causing injury between the object concerned and the vehicle. car 100.

This probability of collision causing injury takes into account the position of the object in the environment of the motor vehicle 100 and the risk of injury which is associated with this object. It is an indicator of possible collisions with injuries that could occur between the motor vehicle 100 and this object present in its environment.

More particularly, this probability of collision is a function of the risk of injury associated with the object and determined in step E8. It also depends on a probability of occupation of a cell of the representation grid by the object concerned.

It is defined according to the following formula:

[0076] [Math. 6]

Pcol_bls_obj — Pocc- ^obj with p _C oi_bis_obj the probability of collision causing an injury between an object and the motor vehicle 100, p _occ the probability of occupation of one of the grid by the object concerned and r _0bj , the risk of injury associated with this object (determined in step E8).

Thus, for each cell of the grid representing the environment of the motor vehicle 100, an indication concerning a possible collision with injury with this vehicle is obtained via this probability.

This probability therefore depends on the location of the objects relative to the motor vehicle 100. It is therefore higher (therefore close to 1) for the objects closest to the motor vehicle 100 than for the objects further away. This makes it possible in particular to take into account the measurement uncertainties of the sensors. Finally, at the end of step E10, the grid representing the environment of the motor vehicle 100 is completed by taking into account the different values of probabilities of collision causing an injury, for all the objects identified in the motor vehicle environment 100.

The method continues at step E12 during which the computer 140 determines a plurality of possible trajectories for the motor vehicle 100. This plurality of trajectories is determined for a time window of the order of a few seconds (for example , of the order of 4 seconds).

[0081] “Possible trajectories” means the trajectories that the motor vehicle 100 could take while maneuvering in a reasonable manner. For example, a trajectory along which motor vehicle 100 would move in reverse is not considered a possible trajectory.

Thus, in step E12, a predefined number of trajectories, which the motor vehicle 100 could take from the initial position in which it is at time t=0, is determined. This plurality of trajectories is determined by using the bicycle model described previously, over the previously introduced time window and by imposing the initial position of the motor vehicle 100. This predefined number of trajectories therefore also depends on the speed of movement of the motor vehicle 100.

FIG. 6 represents, by way of example, four possible trajectories T1, T2, T3, T4.

One of the main objectives of the invention is therefore to determine, among this plurality of possible trajectories, the one which will minimize the probability of a collision causing an injury.

Thus, during step E14, the computer 140 determines the trajectory to be taken by the motor vehicle 100. This trajectory to be taken is determined by optimizing a cost function J. The optimization of the cost function J then makes it possible to minimize the risks of collision causing an injury between the motor vehicle 100 and each identified object.

This cost function J is defined by the following formula:

[0087] [Math. 7] with pcoi-bis-obj the probability of collision causing an injury for each object identified in the environment of the motor vehicle 100, L _p.0 bj the distance between the motor vehicle 100 and each object and h and g predetermined constants. This cost function therefore depends on the probabilities of collision determined in step E10 previously described. It also depends on the kinematic data of the motor vehicle 100, of the distance between the motor vehicle 100 and each object present in the environment of the motor vehicle 100.

The cost function J therefore takes into account, for all the objects identified in the environment of the motor vehicle 100 and for each trajectory determined in step E12, the probability of collision causing an injury as well as the distance separating each object from the motor vehicle 100. The value of the cost function therefore quantifies the risks of collision with injury of the determined trajectories. The more trajectories with the risk of collision causing an injury, the higher the value of the cost function will be.

In practice, the optimization of the cost function J is carried out in such a way as to satisfy at least one dynamic constraint of the motor vehicle 100.

[0091] Here, seven dynamic constraints must be satisfied when optimizing the cost function J.

These seven dynamic constraints are written in the form:

[0093] [Math. 8]

( = f(((t),u(t)) v t e [0,T], (Cl)

[0094] [Math. 9]

^(0) = < f ₀ , (C2)

[0095] [Math. 10]

\(X(T), Y(T)) - (X, Y) _but \ < e, (C3)

[0096] [Math. 11] Smin£d£ _max , (C4)

[0097] [Math. 12] with a _x and a _y the longitudinal and transverse components of the acceleration, g the gravitational acceleration and m the friction coefficient of the ground.

[0099] [Math. 14]

The constraint (C1) makes it possible to ensure that the trajectory corresponds to the dynamics of the motor vehicle 100. In other words, the constraint (C1) makes it possible to ensure that the trajectory obtained conforms to the bicycle model used to describe the movement of motor vehicle 100.

The constraint (C2) fixes the initial state of the motor vehicle 100. This initial state is that listed in the representation of the environment as the location of the motor vehicle 100.

The condition (C3) defines the final position of the object (denoted (X, Y)but). This final position corresponds, in the case of a trajectory without collision, to an optimal position of the motor vehicle 100, after having avoided all the objects present in the environment (position of the motor vehicle at time t=T). In the event of a trajectory with collision, the final position of the motor vehicle 100 corresponds to a position close to that of the object with which the motor vehicle 100 collided.

The conditions (C4) and (C5) impose constraints (minimum and maximum) respectively on the angle and the steering speed. Limitations are imposed on these parameters by the mechanical characteristics of the motor vehicle 100 (it will not be possible, for example, to impose a steering angle greater than 60 degrees).

The condition (C6) imposes a constraint on the pneumatic capacity (by defining a constraint on the components a _x and a _y of the acceleration of the vehicle). This constraint reflects the fact that the maximum longitudinal braking acceleration depends on the transverse acceleration. In other words, this condition makes it possible in particular to take into account the fact that it is not possible to brake as much in a curve only in a straight line.

Finally, condition (C7) makes it possible to ensure that the trajectory is a real trajectory (therefore included in the representation grid defined by the coordinates of its ends X _gr iiie and Ygriiie).

In practice here, the determination of the trajectory to be taken is based on a minimization of the cost function J. Indeed, according to the definition used here for the cost function J, the latter will be all the more great that the risk of injury caused by the collision between the object and the motor vehicle 100 is great.

The trajectory to be taken by the motor vehicle 100 is therefore the one which minimizes the cost function J while satisfying the constraints (C1) to (C7) stated previously.

This trajectory to be taken is displayed inside the motor vehicle 100 so as to be visible to the driver. It is for example displayed on the screen of a man-machine interface arranged inside the motor vehicle 100.

In the case of an autonomous vehicle, the trajectory to be taken (and displayed) is imposed for the movement of the motor vehicle 100. In practice, a command instruction based on the trajectory to be taken is transmitted to a driving module autonomous which controls the trajectory to follow.

In the example of FIG. 6, the trajectory T2 is the trajectory which minimizes the cost function J. It also corresponds to a collision-free trajectory.

In an example where all the trajectories would generate a collision, the chosen trajectory would be the one which would minimize the risks of injury and death. In the case where the trajectory to be taken inevitably leads to a collision, the method continues with step E16 during which the cost function is compared with a predetermined threshold. This predetermined threshold corresponds to an alert threshold from which the driver of the motor vehicle 100 must be warned of the imminence of a collision with a high risk of injury. This alert threshold also depends on an average reaction time available to the driver of the motor vehicle 100. This average reaction time is here of the order of a few seconds, for example approximately 2 seconds.

If the cost function J is greater than this alert threshold, the method continues at step E18 during which the driver receives an alert as to the imminence of a collision with an object in its environment and risking causing injury. This alert is here audible (via the vehicle speakers), haptic (via steering wheel vibrations) or visual (via the display screen), depending on a danger threshold associated with the trajectory determined in step E14. If the cost function J is below this alert threshold, the method continues at step E20 during which the driver continues to drive his vehicle without being alerted to his environment.

The invention therefore makes it possible to mitigate the severity of the collision when a collision is unavoidable, the overall set of risks of injury generated in the event of a collision being used as a constraint of the cost function. The global approach of the invention makes it possible to take into consideration all the objects in the scene to calculate the global risk associated with the corresponding avoidance maneuvers in the event of a collision between the ego and each object. Unlike a decision matrix, which uses predefined trajectories or scenarios, the invention explores the accessible space of the vehicle for the constraints considered, which makes it possible to determine a tailor-made trajectory, the determined optimized trajectory being the best - adapted to the context. The use of impact velocity to determine the risk of injury associated with each object using injury risk curves (also called severity curves) makes it possible to determine a probability of collision associated with the risk of injury which provides a measure more qualitative than a fixed weight per object and which constitutes data that can be easily used within the framework of a global approach, unlike areas of influence for example.

Claims

[Claim 1] Method for determining a trajectory of a motor vehicle (100) characterized in that it comprises steps of, each of the steps being implemented in a computer (140) included in the motor vehicle (100 ):

- identification of objects (101, 102, 200, 201, 300) present in the environment of the motor vehicle (100),

- for each object (101; 102; 200; 201; 300) identified: a) determination of an impact speed (vimpact) between the object (101; 102; 200; 201; 300) concerned and the motor vehicle (100), b) determining a risk (r) of injury in the event of a collision between the object (101; 102; 200; 201; 300) concerned and the motor vehicle (100) at the impact speed ( Vimpact) determined, c) determination of a probability of collision causing an injury as a function of the risk (r) of injury determined, then

- determination of a trajectory to be taken by the motor vehicle (100) by optimizing a cost function (J) which depends on the probabilities of collision causing an injury determined and which makes it possible to minimize the risk of collision causing an injury between each object (101, 102, 200, 201, 300) and the motor vehicle (100).

[Claim 2] A method according to claim 1, wherein said probability of collision resulting in injury is a function of the distance between the motor vehicle (100) and said object (101; 102; 200; 201; 300), said probability of collision causing an injury being preferably calculated according to a probability of occupation of a cell of a grid by said object (101; 102; 200; 201; 300), said grid comprising a plurality of cells representing the environment of the motor vehicle.

[Claim 3] Method according to claim 1 or 2, in which the risk (r) of injury associated with each object (101; 102; 200; 201; 300) is determined according to a plurality of data which correspond respectively to from probabilities that the injury is more or less serious, each probability depending on the nature of the identified object.

[Claim 4] A method according to claim 3, wherein said plurality of data includes a probability of death, a probability of serious injury and a probability of slight injury, and wherein the risk (r) of injury associated with each object is determined by calculating a weighted sum of the probabilities of death, serious injury and minor injury associated with the determined impact velocity (Vimpact).

[Claim 5] Method according to any one of Claims 1 to 4, in which the cost function (J) depends on the kinematic data of the motor vehicle (100), the distance between the motor vehicle (100) and each object ( 101; 102; 200; 201; 300) and probabilities of collision causing injury determined for each object (101; 102; 200; 201; 300).

[Claim 6] Method according to any one of Claims 1 to 5, in which the optimization of the cost function (J) is carried out so as to respect at least one constraint relating to the dynamic characteristics of the motor vehicle (100).

[Claim 7] Method according to any one of Claims 1 to 6, in which the step of determining the trajectory to be taken by the motor vehicle (100) is based on a minimization of the cost function (J), said cost function (J) being all the higher as the risks of injury caused in the event of a collision are great.

[Claim 8] A method comprising a method for determining a trajectory of a motor vehicle (100) according to any one of claims 1 to 7, in which the trajectory to be taken is displayed on a screen inside the vehicle automobile (100) intended for its driver and/or is transmitted to an autonomous driving module.

[Claim 9] Method according to claim 8, also comprising a step of alerting the driver of the motor vehicle (100) implemented by the computer (140) according to the risk of collision or injury caused by a collision between an object (101; 102; 200; 201; 300) and the motor vehicle (100).

[Claim 10] Method according to claim 9, in which the step of alerting is carried out if the cost function (J) is lower than a predetermined threshold.

[Claim 11] A method according to claim 10, wherein the step of alerting includes issuing an audible or haptic or visual alert.