Transmissibility-based DAgger For Fault Classification in Connected Autonomous Vehicles

Abstract

Fault mitigation in Connected Autonomous Vehicle (CAV) platoons is faster and more reliable if the fault structure is known. In this paper we propose using transmissibility operators, which are relationships that relate a set of velocities with another in the platoon, to classify the faults. Transmissibility operators were shown to be exceptional in signals estimation; however, its also shown to be noncausal and thus can only be used offline. To this end, we propose using Data Aggregation (DAgger), which is an extension in imitation learning to transfer the classification experience from transmissibility operators to a novice machine learning agent to be used online. A heterogeneous CAV platoon was modeled with three different faults separately. These faults are actuator disturbances, false data injection attacks, and communication time delay. The proposed algorithm is then tested on the platoon model and then applied to an experimental setup that consists of three autonomous robots. The overall classification accuracy achieved was 95.8% for the experiment.