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EP-4056441-B1 - BACK PROPAGATION PLANNING FOR ADAS/AD MOTION PLANNING AND CONTROL

EP4056441B1EP 4056441 B1EP4056441 B1EP 4056441B1EP-4056441-B1

Inventors

  • Svensson, Viktor

Dates

Publication Date
20260506
Application Date
20210312

Claims (7)

  1. Computer implemented method for scheduling a trajectory of a vehicle (10), the method comprising: detecting kinematic parameters of the vehicle (10) by using at least one vehicle state sensor (13), detecting environmental parameters of the vehicle (10) by using at least one environment sensor (15), estimating, via a processing unit (17), a curvature rate and a longitudinal jerk based on respective cost functions depending from the kinematic parameters and the environmental parameters of the vehicle (10), and estimating, via the processing unit (17), a scheduled trajectory (19) of the vehicle (10) based on the curvature rate and the longitudinal jerk wherein the estimated curvature rate and the estimated longitudinal jerk each comprise a stabilizing contribution and an equilibrium contribution which depend on the respective cost functions, the estimated curvature rate K i is given by: K i = K stab , i + K eq , i , wherein K eq , i = f k C k , eq , i wherein K stab,i is the stabilizing contribution of the curvature rate, K eq,i is the equilibrium contribution of the curvature rate, f k is a gain function for the curvature rate, and C k,eq,i denotes an equilibrium cost for the curvature rate, defined as C k,eq,i = -(C o,i + C h,i + C c,i ), and wherein K stab,i is defined as K stab , i = c k , eq , i − d dt C o , i + d dt C h , i dC c , i dC i wherein C o,i is a lateral offset cost function, C h,i is a heading cost function, C c,i is a curvature cost function, and dC c , i dC i is a change of curvature cost per curvature, the estimated longitudinal jerk j i is given by: j i = j stab , i + j eq , i wherein j eq , i = f j C j , eq , i wherein j stab,i is the stabilizing contribution of the longitudinal jerk, j eq,i is the equilibrium contribution of the longitudinal jerk, f j is a gain function for longitudinal jerk, and C j,eq,i denotes an equilibrium cost for the longitudinal jerk, defined as C j,eq,i = -(C r,i + C rr,i + C a,i ), and wherein j stab,i is defined as j stab , i = c j , eq , i − d dt C r , i + d dt C rr , i dC a , i d A i wherein C r,i is a range cost function, C rr,i is a range rate cost function, C a,i is an acceleration cost function, and dC a , i dA i is a change of acceleration cost per acceleration, and i indicates a number of a respective time increment.
  2. Method according to claim 1, wherein the respective equilibrium cost functions are smoothed with respect to the time increment after estimating the scheduled trajectory (19), a revised curvature rate and a revised longitudinal jerk are estimated based on the smoothed equilibrium cost functions, and a revised scheduled trajectory (19) is estimated based on the revised curvature rate and the revised longitudinal jerk.
  3. Method according to any one of claims 1 to 2, wherein cost related to lateral dynamics of the vehicle (10) is estimated based on a subset of the kinematic parameters and of the environmental parameters of the vehicle (10), and the cost related to lateral dynamics is transformed in order to determine the curvature rate cost function, the lateral offset cost function, the heading cost function, and the curvature cost function.
  4. Method according to anyone of claims 1 to 3, wherein cost related to longitudinal dynamics of the vehicle (10) is estimated based on a subset of the kinematic parameters and of the environmental parameters of the vehicle (10), the cost related to longitudinal dynamics is transformed in order to determine the longitudinal jerk cost function, the range cost function, the range rate cost function, and the acceleration cost function.
  5. Method according to anyone of claims 1 to 4, wherein estimating the scheduled trajectory (19) of the vehicle (10) is further based on a reference curvature (51) which is determined based a course of a lane (45) derived from the environmental parameters.
  6. Device (11) for scheduling a trajectory of a vehicle (10), the device (11) comprising: at least one vehicle state sensor (13) configured to detect kinematic parameters of the vehicle, at least one environment sensor (15) configured to detect environmental parameters of the vehicle, and a processing unit (17) configured to estimate a curvature rate and a longitudinal jerk based on respective cost functions depending from the kinematic parameters and the environmental parameters of the vehicle (10), and estimate a scheduled trajectory (19) of the vehicle (10) based on the curvature rate and the longitudinal jerk wherein the estimated curvature rate and the estimated longitudinal jerk each comprise a stabilizing contribution and an equilibrium contribution which depend on the respective cost functions, the estimated curvature rate K i is given by: K i = K stab , i + K eq , i , wherein K eq , i = f k C k , eq , i wherein K stab,i is the stabilizing contribution of the curvature rate, K eq,i is the equilibrium contribution of the curvature rate, f k is a gain function for the curvature rate, and C k,eq,i denotes an equilibrium cost for the curvature rate, defined as C k,eq,i = -(C o,i + C h,i + C c,i ), and wherein K stab,i is defined as K stab , i = c k , eq , i − d dt C o , i + d dt C h , i dC c , i d C i wherein C o,i is a lateral offset cost function, C h,i is a heading cost function, C c,i is a curvature cost function, and dC c , i dC i is a change of curvature cost per curvature, the estimated longitudinal jerk j i is given by: j i = j stab , i + j eq , i , wherein j eq , i = f j C j , eq , i wherein j stab,i is the stabilizing contribution of the longitudinal jerk, j eq,i is the equilibrium contribution of the longitudinal jerk, f j is a gain function for longitudinal jerk, and C j,eq,i denotes an equilibrium cost for the longitudinal jerk, defined as C j,eq,i = -(C r,i + C rr,i + C a,i ), and wherein j stab,i is defined as j stab , i = C j , eq , i − d dt C r , i + d dt C rr , i dC a , i d A i wherein C r,i is the range cost function, C rr,i is the range rate cost function, C a,i is the acceleration cost function, and dC a , i dA i is a change of acceleration cost per acceleration, and i indicates a number of a respective time increment.
  7. Non-transitory computer readable medium comprising instructions for carrying out the computer implemented method of at least one of claims 1 to 5.

Description

FIELD The present invention relates to a method and a device for scheduling a trajectory of a vehicle. BACKGROUND Advanced driver assistance systems (ADAS) support drivers in order to drive a vehicle more safely and comfortably. These systems are provided e.g. for keeping the vehicle within lane boundaries and to avoid getting too close to other objects by steering and/or braking/accelerating. Such safety functionalities are usually realized separately by individual functions of an advanced driver assistance system which are optimized for a specific purpose. An adaptive cruise control, for example, attempts to maintain a speed set by a driver of the vehicle or to keep a certain distance to a target vehicle in front of a host vehicle in which the adaptive cruise control is installed. Therefore, the adaptive cruise control is effective only to control the speed of the vehicle in a longitudinal direction, and the driver has to control a steering wheel of the vehicle, i.e. any lateral movement. On the other hand, a lane centering and/or lane keeping assistance is provided for lateral control and keeps a vehicle in a lane by steering the wheels in order to compensate any undesired lateral deviations with respect e.g. to a distance from lane markers. However, if a lane keeping assistance is activated, there is no speed control for the vehicle. Also, the steering is controlled by the lane keeping assistance, a driver has still to control a steering wheel and to take over control if necessary. Furthermore, there are methods and devices for planning or scheduling a trajectory of a vehicle which are known in the related art. The scheduled trajectory of the vehicle may be used as an input for further assistance systems, e.g. in order to coordinate the adaptive cruise control for the longitudinal direction and the lane centering and/or lane keeping assistance for the lateral movement. However, these methods and devices for trajectory planning may be computationally expensive, e.g. if they are implemented based on a model predictive control. Y. Zhang et al.: "Optimal Trajectory Generation for Autonomous Vehicles Under Centripetal Acceleration Constraints for In-lane Driving Scenarios", 2019 IEEE Intelligent Transportation Systems Conference (ITSC), IEEE, October 27, 2019, pages 3619 to 3626, discloses a method and a device for generating an optimized trajectory for a vehicle. A curvature derivative and a longitudinal acceleration change rate or jerk are determined and used, amongst others, to estimate the optimized trajectory for the vehicle based on an objective function or cost function. KR 2016 0050441 A discloses a prediction of a longitudinal jerk as a function of forward traffic. Accordingly, there is a need to have a method and a device for scheduling a trajectory of a vehicle requiring a low computational effort while providing a flexible configuration. SUMMARY The present invention provides a computer implemented method, a device and a non-transitory computer readable medium according to the independent claims. Embodiments are given in the subclaims, the description and the drawings. In one aspect, the present invention is directed at a computer implemented method for scheduling a trajectory of a vehicle. According to the method, kinematic parameters of the vehicle are detected by using at least one vehicle state sensor, and environmental parameters of the vehicle are detected by using at least one environment sensor. Via a processing unit, a curvature rate and a longitudinal jerk are estimated based on respective cost functions depending from the kinematic parameters and the environmental parameters of the vehicle. A scheduled trajectory of the vehicle is estimated based on the curvature rate and the longitudinal jerk via the processing unit. The at least one vehicle state sensor is able to provide the dynamic and static states of the vehicle, e.g. the current position, the longitudinal and lateral velocity and therefore the heading of the vehicle, the longitudinal acceleration and the lateral acceleration of the vehicle etc. Hence, the vehicle state sensors may include a global positioning system (GPS), a speedometer, an accelerometer, for example. The at least one environment sensor may be able to determine parameters and/or properties of the road or lane on which the vehicle is currently driving and parameters and/or properties of objects in the environment of the vehicle, e.g. other vehicles, pedestrians etc. For scheduling the trajectory of the vehicle, the at least one environment sensor may determine the curvature and the width of the lane, for example. In detail, the at least one environment sensor may include a camera, a radar system and/or a Lidar system. Estimating the scheduled trajectory relies on the curvature rate for the lateral direction and on the longitudinal jerk for the longitudinal direction which is tangent to the direction in which the vehicle is currently driving. It turned out that th