Cognitive-Digital-Twin-Based Driving Assistance

Advanced driver assistance systems (ADAS) have been developed to enhance driving safety by issuing timely warnings to drivers. However, current ADAS do not take into account the driver's cognitive state when delivering warnings, which can result in false alarms and impact the driver's trust in the system. To address this issue, we propose a Cognitive Digital-twin-based Assistance System (CDAS) that issues warnings tailored to the driver's perception of the driving environment and driving style. In this paper, we present a model of the driver's decision-making process that explicitly captures their perception of the driving environment, their utility evaluation of predicted future environments, and their driving style in terms of minimum acceptable risk. The cognitive digital twin of the driver is then created and updated by minimizing the discrepancy between the predicted and actual behaviors of the driver. With the cognitive digital twin, the CDAS warns the driver when there is a significant discrepancy between the predicted driving strategy based on partial observation and that based on full observation. This approach can more accurately identify risks that the driver is not aware of and provide warnings only when necessary. We conducted human and simulated experiments in a virtual driving environment, and our results demonstrate that our proposed CDAS has a similar perception of risky behaviors compared to humans. Furthermore, the digital twin learning framework can identi