SYSTEM AND METHOD FOR PREDICTING VEHICLE TRAJECTORY - Patent application

Abstract

A method (50) for predicting the trajectory of an ego vehicle traveling in a primary lane, wherein a lane change by the ego vehicle from the primary lane to an adjacent lane is determined according to an estimation of the dynamic behavior of a group of vehicles traveling in the adjacent lane, the group of vehicles including at least one primary vehicle located near the ego vehicle and a secondary vehicle located behind the ego vehicle.

Description

[Technical field]

[0001] The present invention relates to the field of devices and methods for predicting the path and / or trajectory of a motor vehicle, as well as computer programs intended to implement such methods.

[0002] More specifically, the invention relates to the field of driver assistance for motor vehicles, in particular for assisting the driver to activate or deactivate a driver assistance system. [Background technology]

[0003] Automobiles are now equipped with increasingly efficient advanced driver assistance systems (ADAS). The objective of an ADAS is to allow an automobile to be driven autonomously, i.e. without any intervention from the driver or by sharing the driving with the vehicle's driver to keep the vehicle in the traffic lane and / or reduce the speed. In particular, an ADAS can be used to predict the path and / or trajectory of the automobile. In this application, the path of the vehicle is considered to be the geometric shape corresponding to the progression of the vehicle between a starting point and a destination point. The trajectory of the vehicle is considered to be the time evolution of the position of the vehicle between a starting point and a destination point.

[0004] Vehicles that are referred to as "autonomous" or partially assisted driving vehicles require a sufficient model of the vehicle's environment to allow algorithms to make decisions. This is made possible by proprioceptive sensors such as accelerometers, gyrometers, etc., exteroceptive sensors such as cameras, radar, LIDAR, ultrasonic sensors, etc., and data fusion methods configured to process the information received and calculate the status (position, velocity, acceleration, yaw, etc.) of the vehicle and surrounding objects.

[0005] It is therefore essential for such vehicles to predict the movement of the ego-vehicle and other moving objects present in the vehicle's immediate environment that may become obstacles when the ego-vehicle's trajectory interferes with the object's trajectory.

[0006] For example, lane departure prevention assist (LKA) systems are known that allow a vehicle to automatically reposition itself in its lane, or even lane change assist (LCA) systems are known that allow a vehicle to change lanes.

[0007] Other examples of driver assistance are known, such as Automatic Emergency Steering (AES) assistance systems that are able to detect any obstacles and perform emergency steering, allowing potential collision risks to be analyzed by predicting the trajectory of moving objects, or Adaptive Cruise Control (ACC) assistance systems that are able to control the speed and automatically maintain a safe distance to the vehicle ahead.

[0008] A method for controlling a lane change by an ego vehicle according to a first distance between two vehicles adjacent to the ego vehicle as a target for the lane change by the ego vehicle is known from EP 3 056 405 A1.

[0009] The main drawback of these driver assistance systems is the lack of fluidity in their response: in fact, it is particularly difficult to predict the behavior of vehicles located in adjacent lanes to the ego vehicle, which makes the decision-making times of these assistance systems particularly long.

[0010] Therefore, a need exists for optimizing lane changes by an ego vehicle from a primary lane to an adjacent lane. Summary of the Invention

[0011] In view of the above, the object of the present invention is to enable the trajectory of a vehicle to be predicted whilst overcoming said drawbacks.

[0012] The object of the invention is a method for predicting the trajectory of an ego vehicle traveling in a main lane, in which a lane change by the ego vehicle from the main lane to an adjacent lane is determined according to an estimation of the dynamic behavior of a group of vehicles traveling in the adjacent lane, said group of vehicles comprising at least one main vehicle located close to the ego vehicle and a secondary vehicle located behind the ego vehicle.

[0013] Therefore, traffic movements corresponding to vehicle dynamics in lanes adjacent to the ego vehicle's lane are predicted to make lane change decisions.

[0014] The prediction of the ego vehicle's trajectory is determined according to the behavior of a group of vehicles present in lanes adjacent to the ego vehicle's traffic lane.

[0015] Advantageously, the position, orientation and speed information of the ego vehicle and the group of vehicles is collected, and a pairwise dynamic model of the successive vehicles traveling in adjacent lanes is established according to the collected information, so that an accurate dynamic model can be obtained for each of the adjacent vehicles according to the data of the adjacent vehicles in the same traffic lane.

[0016] A dynamic model for each successive vehicle pair is obtained by using an autoregressive exogenous (ARX) computational model to obtain dynamic data for the successive vehicle pairs and determining a quadratic transfer function corresponding to the behavior of the ego vehicle for the considered successive vehicle pair. The behavior of the ego vehicle depends on the longitudinal model and the longitudinal controller.

[0017] The position, orientation and speed of the vehicle are obtained, in particular, by various proprioceptive and exteroceptive sensors of the ego-vehicle's perception system.

[0018] For example, the established dynamic model is validated by comparing the error of the autoregressive exogenous computation model with a threshold value that depends on the actual speed of each vehicle at a given moment and the speed of said vehicles at the previous moment.

[0019] According to subsequent steps, the movement of adjacent vehicles is predicted according to the verified dynamic model and the initial position of said vehicle, the movement of the own vehicle is predicted according to a prediction of the movement of adjacent vehicles and information originating from a proprioceptive sensor of the own vehicle, and lane changes by the own vehicle are determined according to said prediction of the movement of the vehicle and adjacent vehicles, the overall trajectory of the own vehicle and information originating, for example, from a map of the road along which the own vehicle is traveling.

[0020] The step of determining whether the ego vehicle will change lanes allows for an estimation of an appropriate time window in which the ego vehicle can safely change lanes.

[0021] A lane change command for the ego vehicle is then sent to a module for executing the lane change by the ego vehicle.

[0022] If the ego vehicle has no possibility to change lanes, the ego vehicle is notified of a request to modify its parameters, in particular its speed.

[0023] Advantageously, the steps of the method are repeated until a lane change possibility is found.

[0024] According to a second aspect, the present invention relates to a system for predicting a trajectory of an ego vehicle traveling in a main lane, configured to determine a lane change by the ego vehicle from the main lane to an adjacent lane according to an estimation of the dynamic behavior of a group of vehicles traveling in an adjacent lane, the group of vehicles including at least one main vehicle located close to the ego vehicle and a secondary vehicle located behind the ego vehicle.

[0025] Advantageously, the system comprises: - a module for collecting or retrieving position, orientation and speed information of the ego vehicle and of a group of vehicles; a module for estimating pairwise dynamic models of successive vehicles traveling in adjacent lanes according to the collected information, thereby obtaining accurate dynamic models for each of the adjacent vehicles according to data of nearby vehicles in the same traffic lane.

[0026] The dynamic model for each successive vehicle pair is obtained, for example, by using an autoregressive exogenous (ARX) computational model to obtain the dynamic data of the successive vehicle pairs and determining a quadratic transfer function corresponding to the behavior of the ego vehicle compared to the successive vehicle pairs considered. The behavior of the ego vehicle depends on the longitudinal model and the longitudinal controller.

[0027] The position, orientation and speed of the vehicle are obtained, in particular, by various proprioceptive and exteroceptive sensors of the ego-vehicle's perception system.

[0028] Advantageously, the system comprises: a module for validating the established dynamic model by comparing the error of the autoregressive exogenous computation model with a threshold value that depends on the actual speed of each vehicle at a given moment and on the speed of said ego vehicle at a previous moment; a module for predicting the movement of adjacent vehicles according to the ascertained dynamic model and the initial position of said vehicle; a module for predicting the movement of the ego vehicle according to prediction of the movement of adjacent vehicles and information arising from a proprioceptive sensor of the ego vehicle, a module for determining lane changes by the ego vehicle according to said prediction of the movement of the ego vehicle and adjacent vehicles, the overall trajectory of the ego vehicle and information resulting, for example, from a map of the road on which the ego vehicle is traveling.

[0029] According to another aspect, the invention relates to an ego vehicle including a system for perceiving the trajectory of the ego vehicle and a system for predicting the trajectory of the ego vehicle as previously described.

[0030] Further objects, features and advantages of the present invention will become apparent from reading the following description, given by way of non-limiting example only and made with reference to the accompanying drawings, in which: [Brief description of the drawings]

[0031] [Figure 1]1 is a schematic diagram of two adjacent lanes in which an ego vehicle and multiple adjacent vehicles are traveling, the ego vehicle including a trajectory prediction system according to an embodiment of the present invention. [Diagram 2] FIG. 2 shows a schematic diagram of a system for predicting the trajectory of an ego vehicle according to the embodiment of FIG. 1; [Diagram 3] 2 is a flowchart of a method for predicting a trajectory of an ego vehicle according to an embodiment of the present invention implemented by the system of FIG. 1 . DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0032] FIG. 1 shows very diagrammatically two adjacent traffic lanes 1, 2 in which vehicles are traveling in the same direction of travel.

[0033] As shown, an ego vehicle 10 is traveling in a first traffic lane 1, and four vehicles 3, 4, 5, 6 are traveling in an adjacent lane 2 adjacent to the first lane.

[0034] Vehicles 3 , 4 , 5 , 6 form a group 7 of vehicles traveling in an adjacent lane 2 adjacent to the traffic lane 1 of the ego vehicle 10 .

[0035] The ego vehicle 10 comprises a system 11 for perceiving the environment of said vehicle, configured to detect a group 7 of vehicles traveling in an adjacent lane 2 .

[0036] Typically, the group 7 of vehicles traveling in the adjacent lane 2 includes at least one primary vehicle located in the immediate vicinity of the ego vehicle 10 and at least one secondary vehicle located behind the ego vehicle 10 .

[0037] The perception system 11 makes it possible to detect a main vehicle 6 located in the immediate vicinity of the ego vehicle 10 in an adjacent lane 2 .

[0038] The perception system 11 includes various proprioceptive sensors such as accelerometers, gyrometers, exteroceptive sensors such as cameras, radar, LIDAR, ultrasonic sensors, as well as data fusion methods configured to process the received information and calculate the status (position, velocity, acceleration, yaw, etc.) of the ego-vehicle 10 and surrounding objects 7.

[0039] By detecting the vehicle behavior of the main vehicle 6, it is possible to determine a prediction of the position of the secondary vehicles 3, 4, 5 which are located behind the main vehicle 6 in the direction of travel of the vehicles.

[0040] The primary vehicle's speed oscillations are propagated to the secondary vehicles, which results in a greater ability to predict the time window for a lane change by the ego vehicle 10 as the number of secondary vehicles increases.

[0041] The prediction makes it possible to generate a time window for a change by the ego vehicle 10 according to all the vehicles traveling in the lanes adjacent to the ego vehicle 10.

[0042] The ego vehicle includes a system 12 for predicting a trajectory of said ego vehicle, configured to transmit lane change commands for said ego vehicle according to an estimation of the behavior of vehicles traveling in adjacent lanes 2 .

[0043] As shown in detail in FIG. 2, the system 12 for predicting the trajectory of an ego vehicle 10 includes a module 13 for determining the status of vehicles 3 , 4 , 5 , 6 traveling in adjacent lanes 2 adjacent to the ego vehicle 10 .

[0044] For this purpose, the module 13 comprises a module 13a for determining the position P and orientation O of the vehicles 3, 4, 5, 6 travelling in the adjacent lane 2 adjacent to the ego vehicle 10, and a module 13b for determining the speed V of the vehicles 3, 4, 5, 6 travelling in the adjacent lane 2 adjacent to the ego vehicle 10. The position P, orientation O and speed V of the adjacent vehicles are obtained in particular by various proprioceptive and exteroceptive sensors of the perception system 11 of the ego vehicle 10.

[0045] The system 12 for predicting the trajectory of the ego vehicle 10 further includes a module 14 for estimating dynamic models of vehicles traveling in adjacent lanes 2. The module 14 is configured to establish a dynamic model for each pair of successive vehicles traveling in adjacent lanes 2. This allows obtaining an accurate dynamic model for each of the adjacent vehicles according to data of nearby vehicles in the same traffic lane.

[0046] The module 14 for estimating a dynamic model of the vehicle is configured to determine a quadratic transfer function corresponding to the vehicle behavior relative to adjacent vehicles. The behavior of the ego vehicle depends on the longitudinal model and the longitudinal controller.

[0047] Model 14 uses an autoregressive exogenous (ARX) computational model to obtain dynamic data for successive vehicle pairs.

[0048] The autoregressive exogenous computation model is expressed according to the following formula: A(z) y(t) = B(z) u(t-nk) + e(t) Where: z is the time shift, nk is delay, u(t) is the input data, in this case the speed of the leading secondary vehicle, y(t) is the output data, in this case the speed of the main vehicle, e(t) is the error value, and A(z) and B(z) are second order polynomials.

[0049] The polynomials A(z) and B(z) are expressed according to the following formula: A(z) = 1 + a1 z -1 +a2·z -2 B(z) = b1 + b2 z -1 +b3·z -2

[0050] The estimated dynamic model is then validated in a module 16 for validating the dynamic model, configured to validate the dynamic model according to the actual speed V(t) at the instant t and the previous speed V(t-1) at the previous instant t-1.

[0051] The system 12 for predicting the trajectory of the ego vehicle 10 further includes a module 18 for predicting the movement of adjacent vehicles according to the confirmed dynamic model and the initial position, and a module 20 for predicting the movement of the ego vehicle 10 according to the movement prediction provided by the module 18 and information arising from the proprioceptive sensor C of the ego vehicle 10.

[0052] The system 12 for predicting the trajectory of the vehicle 10 further includes a module 22 for determining lane changes by the vehicle 10 according to prediction of the movement of the vehicle and adjacent vehicles, as well as the overall trajectory T of the vehicle 10, and, as a non-limiting example, information resulting from a map Cart of the road on which the vehicle is traveling.

[0053] The module 22 for determining a lane change by the ego vehicle 10 is configured to estimate an appropriate time window within which the ego vehicle can safely make a lane change.

[0054] The lane change command for the ego vehicle 10 is sent to a module 24 for executing the lane change.

[0055] If there is no possibility for the vehicle to change lanes, the module for determining lane changes 22 can be configured to inform the module for predicting the trajectory of the vehicle 10 20 in order to modify the parameters, in particular the speed, etc.

[0056] As shown in FIG. 3, a method 50 for predicting a trajectory of an ego vehicle 10 comprises a step 51 of determining the status of vehicles 3 , 4 , 5 , 6 traveling in adjacent lanes 2 adjacent to the ego vehicle 10 .

[0057] The decision step 51 enables position P, orientation O and speed information to be collected or retrieved of the ego vehicle 10 and of the vehicles 3, 4, 5, 6 traveling in the adjacent lanes 2 adjacent to the ego vehicle 10. The position P, orientation O and speed V of the adjacent vehicles are obtained in particular by various proprioceptive and exteroceptive sensors of the perception system 11 of the ego vehicle 10.

[0058] The method 50 for predicting the trajectory of the ego vehicle 10 further comprises a step 52 of estimating dynamic models of vehicles traveling in adjacent lanes 2. During this step 52, pairwise dynamic models of successive vehicles circulating in adjacent lanes 2 are established, which allows obtaining accurate dynamic models for each of the adjacent vehicles according to data of nearby vehicles in the same traffic lane.

[0059] A dynamic model for each pair of consecutive vehicles is obtained by determining a quadratic transfer function corresponding to the vehicle behavior relative to the adjacent vehicles using an autoregressive exogenous (ARX) computational model to obtain the dynamic data of the pair of consecutive vehicles. The behavior of the ego vehicle depends on the longitudinal model and the longitudinal controller. The autoregressive exogenous computational model is described in relation to the above equations Math1 and Math3.

[0060] The estimated dynamic model is then validated in a step 53 of validating the dynamic model, which is configured to validate the dynamic model according to the actual speed V(t) of the vehicle at the instant t and the previous speed V(t-1) of the vehicle at the previous instant t-1. For example, to validate the dynamic model, the error of the ARX calculation model is compared with a threshold value. If the error is lower than said threshold value, the dynamic model is validated.

[0061] The method 50 for predicting the trajectory of the ego vehicle 10 further comprises a step 54 of predicting the movement of the adjacent vehicles according to the dynamic model and the initial positions ascertained in step 53 .

[0062] The method 50 for predicting the trajectory of the ego vehicle 10 further comprises a step 55 of predicting the movement of the ego vehicle 10 according to the movement prediction provided in step 53 and information arising from the proprioceptive sensor C of the ego vehicle 10 .

[0063] In step 56, a lane change by the ego vehicle 10 is then determined according to information resulting from a prediction of the movement of the ego vehicle and adjacent vehicles, the overall trajectory T of the ego vehicle 10, and a map Cart of the road on which the ego vehicle is traveling.

[0064] The step 56 of determining whether the ego vehicle 10 will change lanes allows an appropriate time window to be estimated to allow the ego vehicle to safely change lanes.

[0065] The lane change command for the ego vehicle 10 is sent in step 57 to the module 24 for executing the lane change by the ego vehicle 10 .

[0066] If there is no possibility for the ego vehicle to change lanes, the ego vehicle 10 is informed of a request to modify its parameters, in particular its speed.

[0067] The method 50 for predicting the ego vehicle's trajectory is repeated until a possible lane change is found.

[0068] The present invention makes it possible to reliably predict in real time the traffic movement in lanes adjacent to the vehicle's traffic lane and to predict the vehicle's position after changing from the main lane to the adjacent lane.

[0069] Furthermore, the invention makes it possible to take into account the limitations of the road on which the vehicle is traveling.

[0070] Such a system and method for predicting the trajectory of the ego vehicle allows traffic to flow more smoothly without compromising the safety of the ego vehicle and surrounding vehicles.

Claims

1. A method (50) for determining a trajectory of an ego vehicle (10) traveling in a main lane (1), wherein lane changes by the ego vehicle from the main lane (1) to an adjacent lane (2) are determined according to an estimation of a dynamic behavior of a group of vehicles traveling in the adjacent lane (2), the group of vehicles (7) comprising at least one main vehicle located close to the ego vehicle and a secondary vehicle located behind the ego vehicle, collecting position (P), orientation (O) and speed (V) information of the ego vehicle (10) and the vehicles (3, 4, 5, 6) of the group of vehicles (7); and establishing pairwise dynamic models of one of the vehicles traveling in the adjacent lane (2) and a vehicle following the one of the vehicles according to the collected information; The method (50), wherein the dynamic model for each pair of successive vehicles is obtained by determining, using an autoregressive exogenous computational model, a quadratic transfer function corresponding to the behavior of the ego vehicle for each pair of successive vehicles considered, the behavior of the ego vehicle depending on a longitudinal model of the ego vehicle (10) and a longitudinal controller of the ego vehicle (10).

2. 2. The method (50) of claim 1, wherein the established dynamic model is validated by comparing the error (e(t)) of the autoregressive exogenous computational model with a threshold value that depends on the actual speed (V(t)) of each vehicle at a given instant (t) and the speed (V(t-1)) of the vehicle at a previous instant (t-1).

3. 3. The method (50) according to claim 2, wherein the movement of adjacent vehicles is predicted according to the verified dynamic model and an initial position of the vehicle, the movement of the ego vehicle (10) is determined according to the prediction of the movement of the adjacent vehicles and information resulting from a proprioceptive sensor (C) that detects the state of the ego vehicle (10), and lane changes by the ego vehicle (10) are determined according to the prediction of the movement of the ego vehicle and the adjacent vehicles and the overall trajectory (T) of the ego vehicle (10).

4. The method (50) of any one of claims 1 to 3, wherein the steps of the method (50) are repeated until a lane change possibility is found.

5. A system (12) for determining a trajectory of an ego vehicle (10) traveling in a main lane (1), configured to determine a lane change by the ego vehicle from a main lane (1) to an adjacent lane (2) according to an estimation of a dynamic behavior of a group of vehicles traveling in the adjacent lane (2), the group of vehicles (7) including at least one main vehicle located near the ego vehicle and a secondary vehicle located behind the ego vehicle, the system (12) comprising: a module (13) for collecting position (P), orientation (O) and speed (V) information of said vehicle (10) and of the vehicles (3, 4, 5, 6) of said group of vehicles (7); a module (14) for estimating a pairwise dynamic model of one of the vehicles traveling in the adjacent lanes (2) and a vehicle following said one according to the collected information; a module (16) for validating said established dynamic model by comparing the error (e(t)) of the autoregressive exogenous computation model with a threshold value that depends on the actual speed (V(t)) of each vehicle at a given instant (t) and on its previous speed (V(t-1)) at a previous instant (t-1); a module (18) for predicting the movement of adjacent vehicles according to the verified dynamic model and the initial position of the vehicle; a module (20) for determining the movement of said vehicle (10) according to predictions of the movement of the neighboring vehicles and information coming from a proprioceptive sensor (C) for detecting the state of said vehicle (10); and a module for determining a lane change by the ego vehicle (10) according to the prediction of the movement of the vehicle and the adjacent vehicles and a general trajectory (T) of the ego vehicle (10).

6. An ego-vehicle (10) comprising a system (11) for perceiving and a system (12) for determining the trajectory of the ego-vehicle according to claim 5.

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