Research on Path Planning and Tracking Method of Auto-driving Vehicles Under Complex Constraints

To solve the problems of path planning and path tracking in the field of automatic driving, the path planning and tracking methods applicable to the actual environment under complex constraints are studied. Firstly, the improved double-tree RRT algorithm (RRT-Connect) based on bidirectional extended balance was introduced. Under the advantage of avoiding the modeling of space, the target preference function and the metric function were introduced in combination with the environmental constraints and the constraints of the vehicle itself. At the same time, combined with the regression detection and collision detection mechanism to solve the local minimum problems in motion planning, and greatly improve the effectiveness of path planning. Then generate a smooth continuous executable track based on the cubic spline interpolation function. Finally, the heading angle is obtained according to the vector field method and converted into a tangential angle to continuously track the path. The effectiveness, correctness and practicability of the algorithm are verified by simulation experiments.