Dual-Discriminator Generative Adversarial Network With Long-Tail Feature Capture for Extreme Scenarios in Human–Machine Shared Driving

Robust and reliable human–machine shared driving (HMSD) is essential for balancing safety and comfort. Within a connected urban arterial system, rare high-risk long-tail disturbances can trigger conflicts, lane departures, and oscillatory flow, degrading system safety and efficiency of the HMSD. To mitigate these effects, an interactive learning framework is built by coupling a scalable environment model with a dual-discriminator generative adversarial network to synthesise diverse, high-fidelity extreme scenarios, thereby enlarging the training domain of HMSD. A bidirectional loop between the environment model and the decision maker enables continuous refinement of the control policy, risk suppression, and improvement in response efficiency. The trained controller leverages cooperative vehicle–infrastructure sensing to derive shared risk states and to adaptively allocate authority between the human driver and automation in real time. The robustness of the proposed method is validated by comparing it with other shared control frameworks on a hierarchical validation platform, including a driver-in-the-loop (DiL) system. The results demonstrate that this method offers a superior balance between driving safety, stability, and pleasure, while demonstrating practical and robust control performance under long-tail events.