Autonomous cars can reduce road traffic accidents and provide a safer mode of transport. However, key technical challenges, such as safe navigation in complex urban environments, need to be addressed before deploying these vehicles on the market. Teleoperation can help smooth the transition from human operated to fully autonomous vehicles since it still has human in the loop providing the scope of fallback on driver. This paper presents an Active Safety System (ASS) approach for teleoperated driving. The proposed approach helps the operator ensure the safety of the vehicle in complex environments, that is, avoid collisions with static or dynamic obstacles. Our ASS relies on a model predictive control (MPC) formulation to control both the lateral and longitudinal dynamics of the vehicle. By exploiting the ability of the MPC framework to deal with constraints, our ASS restricts the controller’s authority 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. In addition, we propose an MPC’s prediction horizon data based novel predictive display to mitigate the effects of large latency in the teleoperation system. We tested the performance of the proposed approach on a high-fidelity vehicle simulator in the presence of dynamic obstacles and latency.
|Title of host publication||Proceedings of the 2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops)|
|Publication status||Published - 2021|
|Event||2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops) - Virtual at Nagoya, Japan|
Duration: 11 Jul 2021 → 17 Jul 2021
|Workshop||2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops)|
|City||Virtual at Nagoya|
|Period||11/07/21 → 17/07/21|