An autonomous parking trajectory planning method suitable for multiple scenarios

Abstract

The invention discloses an autonomous parking trajectory planning method applicable to multiple scenarios, comprising: (1) constructing a parking map; (2) determining the current position of the vehicle and the position of a target parking space; (3) determining the physical parameters of the vehicle, and initializing the vehicle The expansion coefficient of the virtual protection frame; (4) express the vehicle kinematics differential equation as the vehicle motion state constraint; (5) use the virtual protection frame and the expansion coefficient of the virtual protection frame to establish the collision avoidance constraints between the vehicle and the obstacle; (6) establish the state Variables and input variable constraints; (7) Determine the optimization goal and establish an optimization problem; (8) The solver solves the optimization problem to obtain an alternative autonomous parking trajectory; (9) Recalculate the expansion coefficient of the virtual protection frame, if the stop condition is met , to obtain the final autonomous parking trajectory, otherwise update the expansion coefficient of the virtual protective frame, and repeat steps (4) to (8). Utilizing the present invention, collision-free parking tracks can be planned in various scenarios.

Description

technical field [0001] The invention belongs to the field of automatic driving trajectory planning, and in particular relates to an autonomous parking trajectory planning method applicable to multiple scenarios. Background technique [0002] Autonomous parking is an important function in autonomous driving. At present, some commercial vehicles with autonomous parking have appeared on the market. The traditional autonomous parking trajectory planning method mainly includes two links. First, the parking map is divided into network Grid, using search algorithms such as A*, state lattice, etc. to calculate the parking path in the grid, and then use the optimization method to calculate the path speed according to the parking path, and finally form the parking trajectory. This solution is complex and cannot Guaranteed optimality, deviations are prone to occur during the controller implementation phase. [0003] Another method to calculate the parking trajectory is the optimizatio...

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Problems solved by technology

[0002] Autonomous parking is an important function in autonomous driving. At present, some commercial vehicles with autonomous parking have appeared on the market. The traditional autonomous parking trajectory planning method mainly includes two links. First, the parking map is divided into network Grid, using search algorithms such as A*, state lattice, etc. to calculate the parking path in the grid, and then use the optimization method to calculate the path speed according to the parking path, and finally form the parking trajectory. This solution is complex and cannot Guaranteed optimality, prone to deviations during the controller implementation phase

[0003] Another method to calculate the parking trajectory is the optimization method based on the vehicle kinematics model. This method uses optimal control to combine the two steps of path planning and pace, and directly calculates the parking trajectory. However, this method generally requires Manually select the number of optimization points, and after discretizing the continuous optimization problem, there is often a constraint violation problem, which eventually leads to collisions between the path and obstacles, causing parking safety hazards

[0004] Currently, there are some methods that use the dual method to calculate the shortest distance between obstacles and vehicles, and some trajectory planning methods treat vehicles and obstacles as polygons, and use the area method to represent vehicles and obstacles to avoid collision constraints. These methods are implemented in In the process, there are still constraints violations in the optimization problem. Although some studies have proposed the method of re-refining optimization points to increase the number of optimization points in the area where constraint violations occur, this method often leads to more complex problems. The time becomes longer, so the formulation of collision avoidance constraints and optimization problems needs to be improved in principle

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