Multi-Objective Optimization for Lane-Change Decision and Trajectory Planning in Autonomous Buses Tianjin University

Format:

Book

Conference/Event

Author/Creator:

Jin, Jianfeng, author.

Contributor:

Kang, Song

Xie, Hui

Yan, Long

Conference Name:

SAE 2024 Vehicle Powertrain Diversification Technology Forum (2024-12-06 : Xi'An, China)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2025

Summary:

To improve the real-time performance and safety of intelligent bus lane-changing and obstacle avoidance in complex road environments, this study proposes a multi-objective optimization algorithm called LMCTS. L-MCTS integrates a lane-changing benefit model, an LSTM network, and Monte Carlo Tree Search. First, the NGSIM dataset was utilized to filter lane-changing intention points and surrounding traffic flow information, and classification rules were established to process lane-changing behaviors. Based on these decision outcomes, a multi-objective trajectory planning method was designed, taking into account factors such as comfort, safety, and smoothness. The proposed algorithm was validated on the CARLA simulation platform and compared with traditional MCTS and DP+QP algorithms. Results indicated that, in actual driving scenarios, the safety evaluation of L-MCTS improved by 10.71% compared to MCTS and by 17.72% compared to DP+QP. Additionally, L-MCTS enhanced comfort by 4.94% over MCTS and by 2.41% over DP+QP, significantly enhancing passenger comfort. The average algorithm execution time was recorded at 6.21 ms, which represented a 14.12% improvement over MCTS, demonstrating excellent real-time performance

Notes:

Vendor supplied data

Publisher Number:

2025-01-7037

Access Restriction:

Restricted for use by site license