Multimodal Fusion Lateral Stability Control Strategy for Autonomous Vehicles

Abstract

In recent years, the rapid development of autonomous driving technology has placed increasingly high demands on vehicle lateral stability control. Traditional control methods often rely on single-source information, which may lead to insufficient adaptability and robustness in complex driving scenarios. To address this challenge, this paper proposes a multimodal fusion lateral stability control strategy for autonomous vehicles. By integrating data from multiple sensors such as vision, radar, and vehicle dynamics, the framework establishes a comprehensive perception of the driving environment and vehicle state. The proposed method processes and fuses heterogeneous information through a carefully designed fusion algorithm, enabling more accurate identification of potential instability risks. Based on the fused decision-making information, a coordinated control module adjusts steering and braking interventions in real-time to maintain lateral stability. Experimental validations demonstrate that this strategy significantly enhances the vehicle's ability to resist lateral disturbances, improves trajectory tracking accuracy, and ensures smoother and safer handling under various road conditions. The research provides a practical and effective solution for the lateral stability control of autonomous vehicles, contributing to the enhancement of driving safety and system reliability. Future work will focus on optimizing the fusion algorithm's computational efficiency and extending its application to more extreme and unpredictable traffic environments.