ITSC 2025 Paper Abstract

Paper TH-EA-T23.1

Fang, Ruiqi (Southeast University), Bai, Xin (Southeast University), Ding, Haonan (Southeast University), Wang, Fanxun (Southeast University), Wang, Yanlin (Southeast University), liang, jinhao (Southeast University), Yin, Guodong (Southeast University)

Enhancing Obstacle Avoidance Stability of the MDED-HDV in High-Speed and Dynamic Scenarios Via Stability Region Estimation

Scheduled for presentation during the Invited Session "S23b-Trustworthy AI for Traffic Sensing and Control" (TH-EA-T23), Thursday, November 20, 2025, 13:30−13:50, Coolangata 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 18, 2025

Keywords Real-time Motion Planning and Control for Autonomous Vehicles in ITS Networks

Abstract

Modular Distributed Electric Drive Heavy-Duty Vehicles (MDED-HDV) represent a novel vehicle architecture that provides a new technological foundation for operation in high-speed and dynamic scenarios. This paper aims to address two critical challenges in obstacle avoidance for MDED-HDV: (1) how to identify the stability boundary of an MDED-HDV in dynamic environments; and (2) how to design a planning and control framework that ensures both effective obstacle avoidance and dynamic stability based on the estimated stability region. First, a nonlinear dynamic model of the MDED-HDV is developed and reformulated into a rational polynomial form. Then, the stability region of the MDED-HDV is estimated using SOSP method. The influence of different states on the shape and boundary of the stability region is analyzed. Subsequently, a trajectory planning and control framework tailored to high-speed and dynamic scenarios is proposed. The stability region constraints are embedded into the trajectory generation, thereby improving the dynamic feasibility and stability of planned trajectories. It prevents control infeasibility during tracking. Finally, two high-speed and dynamic obstacle avoidance scenarios are designed to validate the effectiveness of the proposed method.