In hybrid traffic environments where human-driven vehicles (HDVs) and autonomous vehicles (AVs) coexist, achieving safe and robust decision-making for AV platooning remains a complex challenge. Existing platooning systems often struggle with dynamic formation management and adaptability, especially in unpredictable, mixed-traffic conditions. To enhance autonomous vehicle platooning within these hybrid environments, this paper presents TriCoD, a twin-world safety-enhanced Data-Model-Knowledge Triple-Driven Cooperative Decision-making Framework. This framework integrates deep reinforcement learning (DRL) with model-driven approaches, enabling dynamic formation dissolution and reconfiguration through a safety-prioritized twin-world deduction mechanism. The DRL component augments traditional model-driven methods, enhancing both safety and operational efficiency, especially under emergency conditions. Additionally, an adaptive switching mechanism allows the system to seamlessly shift between data-driven and model-driven strategies based on real-time traffic demands, thereby optimizing decision-making ability and adaptability. Simulation experiments and hardware-in-the-loop tests demonstrate that the proposed framework significantly improves safety, robustness, and flexibility. A detailed account of the validation results for the model can be found in \href{https://perfectxu88.github.io/towardssafeandrobust.github.io/}{Our Website}.

翻译：在人工驾驶车辆（HDVs）与自动驾驶车辆（AVs）共存的混合交通环境中，实现安全稳健的自动驾驶车辆编队决策仍是一项复杂挑战。现有编队系统通常在动态编队管理与适应性方面存在不足，尤其是在不可预测的混合交通条件下。为提升此类混合环境中自动驾驶车辆的编队性能，本文提出TriCoD——一种基于孪生世界安全增强的数据-模型-知识三重驱动协同决策框架。该框架将深度强化学习（DRL）与模型驱动方法相结合，通过一种以安全为先的孪生世界推演机制，实现动态编队解散与重组。DRL组件增强了传统模型驱动方法，尤其在紧急条件下，提升了安全性与运行效率。此外，一种自适应切换机制使系统能够根据实时交通需求，在数据驱动与模型驱动策略间无缝切换，从而优化决策能力与适应性。仿真实验与硬件在环测试表明，所提框架显著提升了安全性、稳健性与灵活性。模型验证结果的详细说明请参见\href{https://perfectxu88.github.io/towardssafeandrobust.github.io/}{我们的网站}。