Forward and Backward Motion Planning for Autonomous Parking Using Smooth-feedback Bidirectional Rapidly-exploring Random Trees? with Pattern Cost Penalty
Journal
IEEE International Conference on Automation Science and Engineering
Journal Volume
2020-August
Pages
260-265
Date Issued
2020
Author(s)
Abstract
Nowadays, techniques of autonomous vehicles are devoted to providing convenience for people in everyday life. However, existing applications of Advanced Driver Assistance Systems (ADAS) can just be categorized into autonomous driving level 1-4 but not full automation in level 5. With the considerations of risks and environments, autonomous parking may be the first fully autonomous applications for autonomous vehicles. A sampling based motion planner is proposed in this paper to generate a human-like and collision-free parking path efficiently with any feasible start and goal configurations for parking scenarios in everyday life. Moreover, the proposed method aims to deal with the common defects of sampling based motion planners to maintain high path quality and consistency for each execution. To verify the effects of the proposed method, the simulations are conducted in common and strict parking scenarios, such as perpendicular and parallel parking. The simulation results show the potential to deal with the various parking scenarios in everyday life. © 2020 IEEE.
Subjects
autonomous vehicles; human-like driving; motion planning; parking; Reeds-Shepp curve; Smooth-feedback sampling based planner; strict environments; Uturn
SDGs
Other Subjects
Advanced driver assistance systems; Automation; Automobile drivers; Autonomous driving; Autonomous Parking; Collision-free; Forward-and-backward; Goal configuration; Motion planners; Parallel parking; Rapidly-exploring random trees; Autonomous vehicles
Type
conference paper