METHOD AND DEVICE FOR TRAJECTORY PLANNING FOR A VEHICLE

Abstract

A method for trajectory planning of a vehicle includes storing a desired driving path of the vehicle. The method then includes observing external interference factors (2) on the vehicle. The method proceeds by using the driving path and the interference factors (2) to calculate tracking errors (3) and secondary conditions (4). The method then includes optimizing a trajectory (5) in such a way that the tracking errors (3) are reduced within the secondary conditions (4). A corresponding device, a corresponding computer program, and a corresponding storage medium also are provided.

Claims

1. A method (13) for trajectory planning for a vehicle, comprising: storing a desired driving path (1) of the vehicle; observing external interference factors (2) on the vehicle; calculating tracking errors (3) and secondary conditions (4) on the basis of the driving path (1) and the interference factors (2); and optimizing a trajectory (5) in such a way that the tracking errors (3) are reduced within the secondary conditions (4).

2. The method (13) of claim 1, wherein: if optimizing (5) the trajectory fails, the method includes determining an avoidance path (6) that partially satisfies the secondary conditions (4).

3. The method (13) of claim 2, wherein the optimizing (5) is carried out starting from the stored driving path (1) in such a way that the interference factors (2) are contained.

4. The method (13) of claim 1, wherein the step of observing external interference factors (2) on the vehicle further includes observing a status of the vehicle and further basing the calculation (3, 4, 5) on the status.

5. The method (13) of claim 1, further comprising: determining the driving path (1) by a position evaluation (11); and controlling pedal and steering movements (16) of the vehicle so that the vehicle follows the trajectory.

6. The method (13) of claim 5, further comprising: feeding longitudinal acceleration and curvature on the trajectory to a control device (15) as control commands (14); and using the control device to convert the control commands the into pedal and steering movements (16).

7. The method (13) of claim 5, further comprising directly controlling the pedal and steering movements (16) by using the trajectory based on a model or model prediction.

8. A device for carrying out the method (13) of claim 1, comprising: a driving path memory for storing the driving path (1), means for monitoring at least the interference factors (2), means for calculating the tracking errors (3), means for calculating the secondary conditions (4) and means for optimizing (5) the trajectory.

9. A computer program that is configured to carry out all steps of the method (13) of claim 5.

10. A machine-readable storage medium having the computer program of claim 9 stored therein.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0016] FIG. 1 shows a first embodiment based on a step-by-step architecture.

[0017] FIG. 2 shows a second embodiment based on a monolithic architecture.

[0018] FIG. 3 illustrates the interdependence of the components used in the local planning program, with arrows indicating the direction of signal flow.

DETAILED DESCRIPTION

[0019] FIG. 1 illustrates the proposed architecture based on an interface that is compatible with the cascaded software architecture for autonomous vehicles. In this case, longitudinal acceleration and curvature on the trajectory planned locally according to the method 13 are fed as control commands 14 to a control device 15 that converts the control commands 14 into pedal and steering movements 16 of the vehicle.

[0020] The model-based framework shown in FIG. 2 makes it possible to extend this system to include models of the engine, powertrain and steering column dynamics to control the pedal and steering movements 16 directly based on the trajectory, for example using model-based prediction (model predictive control, MPC).

[0021] In both embodiments, the input 12 to the method 13 determined in the context of a position analysis 11 includes a driving path or path—for example in the form of the center and edges of the driving lane or desired geo-coordinates—the vehicle status, for example in the form of position, driving direction and speed, as well as relevant secondary conditions for the driving behavior, for example, regarding the tire grip, the maximum speed, and the position and speed of other moving objects and obstacles. The position and/or speed of other objects may be provided from at least one data source selected from an environment model, traffic information, swarm data and/or a vehicle-based camera system or radar systems such as LIDAR.

[0022] The control device 15 may comprise a computing unit for carrying out at least certain of the steps of the above-described method. A computing unit or a computer-assisted device comprises one or more processors, for example an all-purpose processor (CPU) or a microprocessor, RISC processor, GPU and/or DSP. By way of example, the computer-assisted device comprises additional elements such as storage device interfaces. Optionally or in addition, the terms denote a device that is able to execute a provided or incorporated program, preferably using a standardized programming language such as C++, JavaScript or Python, for example, and/or to control and/or access data storage apparatuses and/or other apparatuses such as input interfaces and output interfaces. The term computer-assisted device also refers to a multiplicity of processors or a multiplicity of (sub-) computers that are interconnected and/or otherwise communicatively connected and which possibly use one or more other resources, for example a storage device, together.

[0023] The process sequence of the method 13, controlled, for example, by software on board the vehicle, will now explained in detail based on FIG. 3.

[0024] In a first step (1), a driving path memory stores the last determined desired driving path of the vehicle at an overall planning level and delivers a preview for a narrow time window on a regular basis. Possible approaches include, for example, search-based or sample-based planning that can take place at a lower speed. The driving path memory or (data) storage device is for example a hard disk drive (HDD, SSD, HHD) or a (non-volatile) solid-state storage device, for example a ROM storage device or a flash storage device (flash EEPROM) The storage device often comprises a plurality of individual physical units or is distributed over a multiplicity of separate apparatuses such that access to said device is implemented by way of data communication, for example a package data service. The latter is a decentralized solution where storage devices and processors of a multiplicity of separate computing units are used instead of a (single unit) central on-board computer or in addition to a central on-board computer.

[0025] In a second step (2), the status of the vehicle and interference factors acting on the vehicle are estimated as part of a status monitoring process. Interference factors acting on the vehicle may be provided from at least one data source selected from an environment model, traffic information, swarm data, ego-sensor data, transverse offset, and planned trajectory/actual trajectory. Swarm data originates, for example, from other vehicles that have driven sections of the same route or formed similar trajectories. Such trajectories are valuable when combined with an evaluation, i.e. frequency, reason (e.g. evasive maneuver), etc. Interference factors also can be provided, for example, by a vehicle-based camera system or radar systems such as LIDAR.

[0026] In a third step (3), the current tracking errors are calculated on the basis of the driving path and the interfering factors (2), for example, in the form of course and position deviations along the driving path and transverse to the driving path.

[0027] In a fourth step (4), secondary conditions to be observed are compiled on the basis of the driving path and the interference factors (2) and are assigned to each time period of the time window considered in the first step (1). It is necessary to consider both static conditions imposed externally, such as the course of the road ahead according to a map, or the current weather, as well as dynamic conditions determined by other on-board software modules, such as the composition of the road surface or the location of obstacles and moving objects.

[0028] In a fifth step (5), the trajectory is optimized iteratively starting from the stored driving path in such a way that the tracking errors (3) are reduced and interference factors (2) are contained, but without violating the secondary conditions (4).

[0029] In a sixth step (6), an avoidance path (6) is determined on the basis of the first to fourth steps (1-4) according to an explicit calculation rule without real-time optimization, in order to provide an alternative in case the optimization algorithm (5) fails.

[0030] Finally, in the case of a step-by-step architecture, the trajectory or avoidance path (6) thus determined is fed to the dynamic vehicle controller (15—FIG. 1) on a subordinate level or, in the case of a monolithic architecture, directly to a model-predictive control system for the pedal and steering movements (16—FIG. 2).

[0031] This disclosure also relates to a computer program product having a computer-readable medium on which a program code executable on at least one computer unit of a vehicle is stored. The program code, when executed on the at least one computer unit, causes the at least one computer unit to perform at least one of the following steps in continuous iterative execution.