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

TL;DR

This paper introduces a hybrid control framework for autonomous vehicles to navigate dense, lane-less traffic by combining perception, feedback, and predictive optimization, demonstrating robustness in simulations.

Contribution

It presents a novel hybrid control approach integrating perception, feedback, and predictive planning specifically designed for unstructured, chaotic traffic environments.

Findings

Framework demonstrates robustness in diverse simulations

Responsive control suitable for unstructured traffic

Effective virtual lane tracking in dense scenarios

Abstract

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.