Shared Steering Control With Predictive Risk Field Enabled by Digital Twin
Yang Liang, Zhishuai Yin, Linzhen Nie, Yuanxin Ba
Abstract
A core issue of safe human-machine cooperative driving lies in dynamic assessment of the rapidly evolving driving risks. Given that, this paper proposes a shared controller for safe and human-friendly cooperative driving based on predictive risk assessment enabled by Digital Twin technologies. In the digital world, we create a fine-grained digital replica of the driving scene comprising historical motions of vehicles, as well as roadway geometries and topologies. On the basis of that, spatial-temporal interactive features are obtained with a deep learning-based model and subsequently decoded to predict future trajectories of each target vehicle in the neighborhood of the ego-vehicle. In the physical world, the predicted trajectories of neighboring vehicles are integrated into the risk distribution to construct predictive risk fields. A novel shared controller in the framework of multi-objective MPC is designed to minimize the driving risk while matching driver's commands, so that safe cooperative driving is achieved in a smooth and minimal-intervention manner. The results of driver-in-the-loop simulation experiments demonstrate the enabling role of the Digital Twin in improving the assessment of risk in highly dynamic scenes through taking the motion trends of dynamic agents into account. The results also show the superiority of the Digital Twin-based shared controller in terms of implementing cooperation in time while honoring the driver's commands whenever possible.