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Autonomous cars can reduce road traffic accidents and provide a safer mode oftransport. However, key technical challenges, such as safe navigation incomplex urban environments, need to be addressed before deploying thesevehicles on the market. Teleoperation can help smooth the transition from humanoperated to fully autonomous vehicles since it still has human in the loopproviding the scope of fallback on driver. This paper presents an Active SafetySystem (ASS) approach for teleoperated driving. The proposed approach helps theoperator ensure the safety of the vehicle in complex environments, that is,avoid collisions with static or dynamic obstacles. Our ASS relies on a modelpredictive control (MPC) formulation to control both the lateral andlongitudinal dynamics of the vehicle. By exploiting the ability of the MPCframework to deal with constraints, our ASS restricts the controller'sauthority to intervene for lateral correction of the human operator's commands,avoiding counter-intuitive driving experience for the human operator. Further,we design a visual feedback to enhance the operator's trust over the ASS. Inaddition, we propose an MPC's prediction horizon data based novel predictivedisplay to mitigate the effects of large latency in the teleoperation system.We tested the performance of the proposed approach on a high-fidelity vehiclesimulator in the presence of dynamic obstacles and latency.

Active Safety System for Semi-Autonomous Teleoperated Vehicles