On Connected Autonomous Vehicles With Unknown Human Driven Vehicles Effects Using Transmissibility Operators

This study proposes an algorithm for fault detection and mitigation of mixed autonomous and human-driven vehicle platoons based on transmissibility identification. This work is motivated by the fact that on-road human-drivers’ behaviour is unknown and difficult to be predicted. Transmissibility operators are mathematical operators that relate one subset of outputs to another in the same system. The transmissibility superiority is represented in the in-dependency on the system excitation signals. We reformulate the system dynamics to render the system inputs, external disturbances, as well as the human-drivers’ behaviour along with any other nonlinearities as independent excitation signals on the system. Therefore, the transmissibility operators become independent of the human-drivers’ behaviour and robust against external disturbances. Transmissibilities are then applied to detect and localize physical and cyber faults within the platoon. Then these faults are mitigated using a transmissibility-based sliding mode controller. The controller stability and the string stability are investigated while the controller is active and the faults are mitigated. We validate the proposed algorithm on a model of the platoon obtained using the bond graph approach. Moreover, we apply the proposed algorithm experimentally to a platoon consisting of three robots (i.e. two autonomous robots and a human-driven robot), that is connected using wireless communications.
Note to Practitioners
—The existence of connected autonomous vehicles depends greatly on the smooth transition between the current on-road human-driven vehicles to autonomous vehicles. The typical methods of securing dynamic systems depend on estimating the system behaviour and responses. Increasing the number of autonomous vehicles on roads necessitates the typical securing techniques to estimate the human-drivers’ behaviour. Thus, securing the connected autonomous vehicles during this transition is challenging since the on-road human-driver behavior is unknown and difficult to be estimated. Moreover, connected autonomous vehicles should adapt to their environment while maintaining their role within the autonomous platoon. This adaptation includes adapting to the unknown human-driver behaviour. This inspired the authors to develop the proposed transmissibility-based fault mitigation. The proposed technique is shown to be able to handle unknown human-driver behaviors, different driving conditions such as road irregularities and different weather conditions, and different physical and cyber faults (i.e., in the vehicles or in the communication links). The platoon stability is then investigated while the faults are mitigated, and shown to guarantee the platoon stability.