CL-infoRRT Collision-Avoidance Trajectory Planning for Autonomous Vehicle East China Jiaotong University

Format:

Book

Conference/Event

Author/Creator:

Wu, Wei, author.

Contributor:

Hu, Yiming

Lu, Jun

Wang, Xiaoliang

Yang, Jinwen

Yu, Qin

Zeng, Dequan

Conference Name:

WCX SAE World Congress Experience (2025-04-08 : Detroit, Michigan, United States)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2025

Summary:

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

Notes:

Vendor supplied data

Publisher Number:

2025-01-8027

Access Restriction:

Restricted for use by site license