Browse Publications Technical Papers 2020-01-0118

An Efficient Path Planning Methodology Based on the Starting Region Selection 2020-01-0118

Automated parking is an efficient way to solve parking difficulties and path planning is of great concern for parking maneuvers [1]. Meanwhile, the starting region of path planning greatly affects the parking process and efficiency. The present research of the starting region are mostly determined based on a single algorithm, which limits the flexibility and efficiency of planning feasible paths. This paper, taking parallel parking and vertical parking for example, proposes a method to calculate the starting region and select the most suitable path planning algorithm for parking, which can improve the parking efficiency and reduce the complexity. The collision situations of each path planning algorithm are analyzed under collision-free conditions based on parallel and vertical parking. The starting region for each algorithm can then be calculated under collision-free conditions. After that, applicable starting regions for parking can be obtained, and each of those regions corresponds to a parking path planning algorithm. However, there always exists overlapped starting regions, which can be applied to multiple parking path planning algorithms. In order to select the most suitable algorithm to plan the parking path, the priority order of algorithms is decided based on the preference criterion function. The collision-free parking path can be generated following the priority order. Based on the modified B-spline curves, a continuous-curvature path is presented. The simulation results based on MATLAB/Simulink and PreScan show that the methodology can smoothly judge the feasibility of automated parking in vehicle’s current position and plan the most suitable parking path. The proposed methodology can calculate the starting region of automated parking rapidly and plan more efficient parking path compared with other methods.


Subscribers can view annotate, and download all of SAE's content. Learn More »


Members save up to 18% off list price.
Login to see discount.