Multi-Agent Reinforcement Learning (MARL) holds significant promise for enabling cooperative driving among Connected and Automated Vehicles (CAVs). However, its practical application is hindered by a critical limitation, i.e., insufficient fault tolerance against observational faults. Such faults, which appear as perturbations in the vehicles' perceived data, can substantially compromise the performance of MARL-based driving systems. Addressing this problem presents two primary challenges. One is to generate adversarial perturbations that effectively stress the policy during training, and the other is to equip vehicles with the capability to mitigate the impact of corrupted observations. To overcome the challenges, we propose a fault-tolerant MARL method for cooperative on-ramp vehicles incorporating two key agents. First, an adversarial fault injection agent is co-trained to generate perturbations that actively challenge and harden the vehicle policies. Second, we design a novel fault-tolerant vehicle agent equipped with a self-diagnosis capability, which leverages the inherent spatio-temporal correlations in vehicle state sequences to detect faults and reconstruct credible observations, thereby shielding the policy from misleading inputs. Experiments in a simulated highway merging scenario demonstrate that our method significantly outperforms baseline MARL approaches, achieving near-fault-free levels of safety and efficiency under various observation fault patterns.

翻译：多智能体强化学习（MARL）在实现网联自动驾驶车辆（CAVs）协同驾驶方面具有重要潜力。然而，其实际应用受到一个关键限制的阻碍，即对观测故障的容错能力不足。此类故障表现为车辆感知数据中的扰动，可能严重损害基于MARL的驾驶系统性能。解决该问题面临两大主要挑战：一是在训练中生成能有效冲击策略的对抗性扰动，二是使车辆具备减轻受损观测影响的能力。为克服这些挑战，我们提出一种面向协同匝道汇入车辆的容错MARL方法，包含两个关键智能体。首先，通过协同训练一个对抗性故障注入智能体，主动生成挑战并强化车辆策略的扰动。其次，我们设计了一种具备自诊断能力的新型容错车辆智能体，其利用车辆状态序列中固有的时空相关性来检测故障并重建可信观测，从而保护策略免受误导性输入的影响。在模拟高速匝道汇入场景中的实验表明，我们的方法显著优于基线MARL方法，在各种观测故障模式下实现了接近无故障水平的安全性与效率。