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.

Information-aware connected and automated vehicles (CAVs) have drawn great attention in recent years due to their potentially significant positive impacts on roadway safety and operational efficiency. In this paper, we conduct an in-depth review of three basic and key interrelated aspects of a CAV: sensing and communication technologies; human factors; and information-aware controller design. First, the different vehicular sensing and communication technologies and their protocol stacks, to provide reliable information to the information-aware CAV controller, are thoroughly discussed. Diverse human factors, such as user comfort, preferences, and reliability, to design the CAV systems for mass adaptation are also discussed. Then, the different layers of a CAV controller (route planning, driving mode execution, and driving model selection) considering human factors and information through connectivity are reviewed. In addition, the critical challenges for the sensing and communication technologies, human factors, and information-aware controller are identified to support the design of a safe and efficient CAV system while considering user acceptance and comfort. Finally, the promising future research directions of these three aspects are discussed to overcome existing challenges to realize a safe and operationally efficient CAV.

With the emergence of vehicle-to-vehicle communication technology, cooperative adaptive cruise control (CACC) cars can be expected in the near future. In this paper, novel criteria for string stability are proposed for mixed traffic platoons that consist of both automated and manual driving cars. A mixed traffic string stability definition is proposed to guarantee the boundedness of the motion states fluctuation upstream as well as the safety of the entire platoon. With proper arrangements of the platoon sequence as well as mild restrictions on the leading car's velocity overshoot, the proposed mixed traffic string stability can be realized via some suitable controller design for the automated cars. The benchmark controller with feedback and feedforward path is first used to verify the above results with rigorous proof. Furthermore, a human-centered CACC system is designed to improve both physical and psychological comfort of a driver under the guarantee of mixed traffic string stability. The action-point car-following model is adapted to quantify the driver's psychological comfort in combination with the quantification of physical comfort based on jerk. A model predictive control-like blending ratio controller is developed to obtain the optimal time headway in the feedback controller for tradeoffs among driver comfort, fuel efficiency, and traffic throughput under string stability constraints. Finally, a seven-car CACC mixed traffic platoon scenario is simulated. Compared with the benchmark CACC controller design, the human-centered CACC design is shown to largely improve the driving comfort.