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Conditionally automated vehicles can be operated on most regular roads without a driver’s supervision. They show excellent potential for market adoption and are now being targeted by numerous auto manufacturers for mass production. The system of such a vehicle enables it to autonomously perform dynamic driving tasks within the operational design domain, but once this system fails or malfunctions, the vehicle will be unable to reliably complete a dynamic driving task. In such cases, the system will send a takeover request, following which the driver needs to immediately take control of the vehicle. The driver’s physical and mental state, as well as the non-driving-related tasks that they are engaged in, affects the time required for them to perform the takeover and the quality of the takeover. To manage driving risks and guarantee the safety of drivers during automated driving, an automated driving system should be able to monitor a driver’s state and behavior, assess their level of alertness, and perform the appropriate actions as required. In recent years, techniques for monitoring a driver’s state have been widely researched, and several practical methods have been proposed. In this review, we review representative methods, aiming to introduce the concept of driver state monitoring to a broader audience. First, we identified a few typical driver states that are important in driver state monitoring from the perspective of application demands for driver state monitoring in conditionally automated driving. Then, we categorized and reviewed existing studies on driver state monitoring according to the types of sensing data employed by previously proposed methods. Additionally, we collected datasets corresponding to different data types for driver state monitoring. Finally, by analyzing existing issues in driver state monitoring in relation to conditionally automated driving, we provided several suggestions for future research directions in this area, and discussed potential challenges and possible solutions.
Qu et al. (Sun,) studied this question.