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In this paper, we propose a human-robot interaction (HRI) framework to reduce the vulnerability of an unmanned ground vehicle (UGV) platoon under cyber attacks. An observer-based autonomous resilient control strategy is first designed to mitigate the effects of vehicle-to-vehicle (V2V) cyber attacks. Rigorous proof of the safety conditions of the platoon under V2V cyber attacks is derived. Next, to facilitate human supervision in emergencies and uncertainties, a decision-making aid system is developed, which consists of an anomaly reporting system (ARS), a trust-based information management system (TIMS), and a graphical user interface (GUI). ARS is designed to detect the abnormality of the neighbor UGVs based on the model-based residual generation and analysis. TIMS is developed to rule out information with low trustworthiness, which is fulfilled by a Bayesian-based information pre-processor followed by an enhanced median filter with trustworthiness regulation. It is rigorously proved that TIMS can recover the tempered information under the mild cyber attack scenario autonomously. To deal with unexpected severe vehicle-to-cloud (V2C) cyber attacks, human observations can also be leveraged to bolster the resilience of the TIMS. Representative simulation and humans-in-the-loop experiment (fulfilled by the designed GUI) demonstrate that the proposed framework can effectively guide human operators when working with UGV platoons under cyber attacks. On average, compared to a multi-screen surveillance benchmark approach, the proposed framework can achieve 78.4% decrements of platoon vulnerability and 39.0% decrements in human workload.
Li et al. (Fri,) studied this question.