Abstract

In the emerging hybrid traffic flow environment, which includes both human-driven vehicles (HDVs) and autonomous vehicles (AVs), ensuring safe and robust decision-making and control is crucial for the effective operation of self-driving formations. Current systems for cooperative adaptive cruise control and lane changing are inadequate in responding to real-world emergency situations, limiting the potential of autonomous vehicle platooning technology. To address the aforementioned challenges, we propose a Twin-World Safety-Enhanced Data-Model-Knowledge Hybrid-Driven autonomous vehicle platooning Cooperative Control Framework. Within this framework, a deep reinforcement learning formation decision model integrating traffic priors is designed, and a twin-world deduction model based on safety priority judgment is proposed. Subsequently, an optimal control-based multi-scenario decision-control right adaptive switching mechanism is designed to achieve adaptive switching between data-driven and model-driven methods. Through simulation experiments and hardware-in-the-loop tests, our algorithm has demonstrated excellent performance in terms of safety, robustness, and flexibility.