Traffic Stability and Synchronization Dynamics across Autonomous Vehicle Platoons

Abstract

The emergence of autonomous vehicle platooning presents a transformative paradigm for mitigating traffic congestion and enhancing roadway safety. Central to this paradigm is the concept of synchronization dynamics, wherein individual vehicles coordinate their microscopic acceleration and deceleration behaviors to achieve macroscopic traffic flow stability. Despite extensive theoretical modeling, empirical validation connecting sensor-level data fidelity to system-wide stability remains scarce. This paper provides a comprehensive investigation into the relationship between synchronization dynamics and traffic stability, utilizing high-fidelity sensor validation evidence from autonomous vehicle platoons. By employing a multi-modal sensor fusion framework comprising Light Detection and Ranging, automotive radar, and high-resolution optical cameras, the research evaluates the latency, accuracy, and reliability of inter-vehicle state estimation. The study systematically analyzes how perturbations in lead-vehicle velocity propagate through the platoon string, examining the damping effects facilitated by synchronized control strategies. The findings demonstrate that tight synchronization, validated through robust sensor data, significantly attenuates shockwave propagation, thereby enhancing string stability and increasing overall highway capacity. Furthermore, the analysis reveals critical thresholds of sensor degradation beyond which synchronization breaks down, leading to cascading instabilities. This research bridges the gap between cyber-physical system control and macroscopic traffic flow theory, offering critical insights for the deployment of intelligent transportation systems.