Human-like motion planning for autonomous parking based on revised bidirectional rapidly-exploring random tree* with Reeds-Shepp curve
Journal
Asian Journal of Control
Journal Volume
23
Journal Issue
3
Pages
1146-1160
Date Issued
2021
Author(s)
Jhang J.-H
Abstract
Autonomous parking is one significant autonomous application and will be implemented in daily life in the near future. Due to encountered narrow environments, the issues related to autonomous parking, such as path quality requirements, strict collision avoidance, and motion direction changes, must be overcome properly. Moreover, to be applied in daily driving activities, real-time planning and human preference should be fulfilled by the designed motion planners. Therefore, an efficient and human-like motion planning method based on the revised Bidirectional Rapidly-Exploring Random Tree* (Bi-RRT*) with Reeds-Shepp curve is presented. The proposed method results in human-like paths which have high trajectory quality and consistency for parking scenarios due to the revised Bi-RRT* framework. Strict collision checking model guarantees the resulting paths to be collision-free and even leaves safe distance from obstacles and uncertainties. State space adjustment makes the path optimization more efficient and effective. On the other hand, the cost function revision makes the resulting paths meet human driving behavior, such as less backward driving and motion direction changes. In addition, rigorous simulations and analysis demonstrate the effectiveness of the cost function revision and the state space adjustment and illustrate good performance of the proposed approach in common and even complex parking scenarios. ? 2020 Chinese Automatic Control Society and John Wiley & Sons Australia, Ltd
Subjects
autonomous parking
human-like driving
motion planning
rapidly-exploring random tree (RRT)
Reeds-Shepp curve
Collision avoidance
Cost benefit analysis
Cost functions
Motion planning
Autonomous Parking
Collision-checking
Human driving behavior
Narrow environment
Quality requirements
Rapidly-exploring random trees
Real-time planning
Rigorous simulation
Behavioral research
Type
conference paper