Fault Detection and Human Intervention in Vehicle Platooning: A Multi-Model Framework

摘要

Vehicle platooning has been a promising solution for improving traffic efficiency and throughput. However, a failure in a single vehicle, including communication loss with neighboring vehicles, can significantly disrupt platoon performance and potentially trigger cascading effects. Similar to modern autonomous vehicles, platoon systems require human drivers to take control during failures, leading to scenarios where vehicles are operated by drivers with diverse driving styles. This paper presents a novel multi-model approach for simultaneously identifying signal drop locations and driver attitudes in vehicular platoons using only tail vehicle measurements. The proposed method distinguishes between attentive and distracted driver behaviors by analyzing the propagation patterns of disturbances through the platoon system. Beyond its application in platooning, our methodology for detecting driver behavior using a multi-model approach provides a novel framework for human driver identification. To enhance computational efficiency for real-time applications, we introduce a blending-based identification method utilizing chosen models and weighted interpolation, significantly reducing the number of required models while maintaining detection accuracy. The effectiveness of our approach is validated through high-fidelity CarSim/Simulink environment simulations. Results demonstrate that the proposed method can accurately identify both the location of signal drops and the corresponding driver behavior. This approach minimizes the complexity and cost of fault detection while ensuring accuracy and reliability.