ITSC 2025 Paper Abstract

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Paper WE-EA-T5.6

Ye, Lei (Chongqing University), Li, Linhao (Chongqing University), Han, Qingwen (Chongqing University), Zeng, Lingqiu (Chongqing University), Li, Jianzhong (Chongqing University)

Trajectory Adjustment Algorithm for Intelligent Connected Vehicles (ICV) Live Testing

Scheduled for presentation during the Regular Session "S05b-Deployment, Modeling, and Optimization in Intelligent Transportation Systems" (WE-EA-T5), Wednesday, November 19, 2025, 14:50−15:30, Surfers Paradise 2

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on October 19, 2025

Keywords Field Test Methodologies for ITS Integration in Smart Cities, Cloud and Edge Computing Integration in ITS for Real-time Traffic Data Processing, AI, Machine Learning for Dynamic Traffic Signal Control and Optimization

Abstract

There is a significant problem in V2X outfield live testing in the field of Intelligent Connected Vehicles (ICVs): the vehicle trajectory data predicted by the live broadcast is unreasonable, and the changes of the key motion parameters are not in accordance with the laws of kinematics. Therefore, based on the digital twin-based outfield live broadcast system, the artificial potential field force model (APFM) is introduced, and then a new combination particle swarm optimization (PSO) -reinforcement learning (RL) algorithm is designed to optimize the potential field force coefficients. In this way, it achieves online adjustment of vehicle motion status through offline parameter training in dynamic live streaming scenarios, making vehicle trajectories smoother and data more reliable. Finally, the smoothness of the adjusted vehicle trajectory is analyzed by road testing, which verifies the effectiveness and practicality of the new algorithm.

 

 

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