Situation-Aware Feedback-Predictive Control Framework for Lane-Less Dense Traffic

摘要

Navigating dense, lane-less traffic remains one of the most challenging scenarios for autonomous vehicles, especially in emerging regions where road structure and driver behavior are highly unpredictable. This paper presents a hybrid control framework tailored for such environments, integrating a $360^\circ$ zone-based perception module with a dual-layer control strategy that combines classical feedback and predictive optimization. The longitudinal feedback controller computes reference speed based on braking distance and steering dynamics, while the lateral controller tracks a virtual optimal lane derived from the spatial distribution of neighboring vehicles. The predictive planner samples control inputs over a time horizon and selects the most feasible trajectory using a multi-term cost function. Simulation results across diverse one-way traffic scenarios demonstrate the framework's robustness, responsiveness, and suitability for chaotic, unstructured traffic.