Wang, Fanxun (Southeast University), Shen, Tong (Southeast University), Lu, Yanbo (Tsinghua University), Fang, Ruiqi (Southeast University), Wang, Ziwei (Southeast University), Yin, Guodong (Southeast University)

Safety-Guaranteed Optimal Control Strategy for DDEVs in High-Dynamic Scenarios: A Reconstructed Stability Region Method

Scheduled for presentation during the Regular Session "S11b-Motion Planning, Trajectory Optimization, and Control for Autonomous Vehicles" (WE-EA-T11), Wednesday, November 19, 2025, 14:50−15:30, Broadbeach 1&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

Abstract

The design of motion controllers that are theoretically guaranteed in both stability and safety is essential for enhancing the performance of distributed drive electric vehicles (DDEVs) under high-dynamic scenarios. To address the inherent trade-off issue between tracking accuracy and vehicle stability, this paper proposes a safety-guaranteed optimal control strategy (SGOCS), which is developed based on a reconstructed stability region-based method. Since existing methods for identifying stability boundaries for DDEVs lack theoretical completeness, due to the strong nonlinearity of tire-road interaction under time-varying conditions, this paper investigates how the introduction of Direct Yaw Moment (DYM) influences the sequence in which tires reach saturation, based on which a dynamic stability region is constructed, which possesses explicit mathematical formulations. Based on the reconstructed stability regions (RSRs), a motion controller is designed within the control Lyapunov function, control barrier function, and quadratic programming (CLF-CBF-QP) framework, which ensures both stability and safety from a theoretical perspective. The effectiveness of the proposed controller is validated through high-fidelity co-simulations that are carried out under real racetrack scenarios, where the results demonstrate that it can ensure stability, safety, and robustness.