CL-infoRRT Collision-Avoidance Trajectory Planning for Autonomous Vehicle

2025-01-8027

04/01/2025

Features
Event
WCX SAE World Congress Experience
Authors Abstract
Content
To ensure the safety and stability of road traffic, autonomous vehicles must proactively avoid collisions with traffic participants when driving on public roads. Collision avoidance refers to the process by which autonomous vehicles detect and avoid static and dynamic obstacles on the road, ensuring safe navigation in complex traffic environments. To achieve effective obstacle avoidance, this paper proposes a CL-infoRRT planning algorithm. CL-infoRRT consists of two parts. The first part is the informed RRT algorithm for structured roads, which is used to plan the reference path for obstacle avoidance. The second part is a closed-loop simulation module that incorporates vehicle kinematics to smooth the planned obstacle avoidance reference path, resulting in an executable obstacle avoidance trajectory. To verify the effectiveness of the proposed method, four static obstacle test scenarios and four RRT comparison algorithms were designed. The implementation results show that all five algorithms can generate obstacle avoidance trajectories in the four scenarios. However, compared with the comparison algorithms, the proposed method uses fewer nodes. In Scenario 1, the proposed method uses 3.82% fewer nodes than RRT-Basic, 0.96% fewer nodes than RRT-Goal, 0.77% fewer nodes than RRT-Star, and 4.77% fewer nodes than RRT-Connect. In Scenario 2, the proposed method uses 3.76% fewer nodes than RRT-Basic, 1.35% fewer nodes than RRT-Goal, 0.12% fewer nodes than RRT-Star, and 13.14% fewer nodes than RRT-Connect. In Scenario 3, the proposed method uses 4.48% fewer nodes than RRT-Basic, 2.01% fewer nodes than RRT-Goal, 0.57% fewer nodes than RRT-Star, and 5.87% fewer nodes than RRT-Connect. In Scenario 4, the proposed method uses 3.59% fewer nodes than RRT-Basic, 1.76% fewer nodes than RRT-Goal, 0.16% fewer nodes than RRT-Star, and 5.77% fewer nodes than RRT-Connect. This indicates that the proposed method can effectively plan optimal and safe obstacle avoidance trajectories.
Meta TagsDetails
DOI
https://doi.org/10.4271/2025-01-8027
Pages
11
Citation
Wu, W., Lu, J., Zeng, D., Yang, J. et al., "CL-infoRRT Collision-Avoidance Trajectory Planning for Autonomous Vehicle," SAE Technical Paper 2025-01-8027, 2025, https://doi.org/10.4271/2025-01-8027.
Additional Details
Publisher
Published
Apr 01
Product Code
2025-01-8027
Content Type
Technical Paper
Language
English