When Autonomous Vehicles Meet Accidents: A DT-Enabled Post-Accident Maintenance Scheme

Abstract

The autonomous vehicles (AVs), as intelligent mobile robots, can undertake tasks to facilitate various computation-intensive services in intelligent transportation system (ITS). Due to hardware device failures or environmental identification errors, the AVs controlled by intelligent algorithms may cause accidents during driving. However, the existing studies in the post-accident stage lack the analysis of the impact degree of the accidents and the computing tasks undertaken by the AVs to determine the optimal maintenance strategy. In this article, we consider the accidents in a continuous period of time and design a digital twin (DT)-enabled post-accident maintenance scheme. Specifically, by considering the computing tasks undertaken by the AVs and the impact degree of the accidents, we first design a DT-enabled post-accident maintenance architecture. With the designed architecture, an optimal maintenance method under an incomplete information scenario is then proposed to help each accident AV decide its optimal maintenance strategy. Besides, based on the maintenance strategies of the AVs and the capacities of the maintenance service providers (MSPs), the two-way selection problem between the AVs and the MSPs in the continuous period of time is modeled as a dynamic matching game to obtain the optimal AV-MSP pairs. Simulation results demonstrate that the proposed scheme outperforms the benchmark schemes in terms of the maintenance rate of the accident AVs, the average utility of the MSPs, and the average social welfare.