Driver monitoring is added as a primary safety measure in the EuroNCAP roadmap 2025. Especially with the introduction of automated driving systems into the market, new requirements are set to driver monitoring systems (DMSs). When not being actively involved in driving, the risk of drivers becoming drowsy and even falling asleep at the wheel increases. Modern DMSs will have to be able to detect a driver falling asleep or sleeping in order for the automation to take appropriate actions. Conventional measures for detecting the driver state such as analysing the driving behaviour are not available in automated driving. The aim of the study was to identify potential physiological measures as a basis for the development of systems that are able to detect sleep in drivers during automated driving. A within‐subjects study with N = 21 subjects was conducted in a high‐fidelity driving simulator. Electromyography, electrodermal activity (EDA), respiration and electrocardiography (ECG) were measured in drivers during states of wakefulness and sleep. Sleep stages were assigned with the electroencephalography as a ground truth. The results indicate the potential of EDA and ECG parameters to differentiate between sleep and wakefulness. Implications for the implementation in DMS are discussed.

Driver drowsiness and attention warning systems are mandatory for new vehicles in the EU by 2022. Requirements for the test procedure for type approval are laid down in the Annex of EU regulation C/2021/2639. The aim of the presented study was to establish a robust test procedure that enables the collection of drowsy driving data in real traffic while accounting for safety and study ethics. Compared to other circadian timings, most participants reached the required drowsiness threshold when starting the test drive at 1 p.m. The drowsiness threshold of $>$ 7 defined by the EU regulation was considered suitable.

When highly automated driving is realized, the role of the driver will change dramatically. Drivers will even be able to sleep during the drive. However, when awaking from sleep, drivers often experience sleep inertia, meaning they are feeling groggy and are impaired in their driving performance―which can be an issue with the concept of dual-mode vehicles that allow both manual and automated driving. Proactive methods to avoid sleep inertia like the widely applied ‘NASA nap’ are not immediately practicable in automated driving. Therefore, a reactive countermeasure, the sleep inertia counter-procedure for drivers (SICD), has been developed with the aim to activate and motivate the driver as well as to measure the driver’s alertness level. The SICD is evaluated in a study with N = 21 drivers in a level highly automation driving simulator. The SICD was able to activate the driver after sleep and was perceived as “assisting” by the drivers. It was not capable of measuring the driver’s alertness level. The interpretation of the findings is limited due to a lack of a comparative baseline condition. Future research is needed on direct comparisons of different countermeasures to sleep inertia that are effective and accepted by drivers.

Most studies on users’ perception of highly automated driving functions are focused on first contact/single usage. Nevertheless, it is expected that with repeated usage, acceptance and usage of automated driving functions might change this perception (behavioural adaptation). Changes can occur in drivers’ evaluation, in function usage and in drivers’ reactions to take-over situations. In a driving simulator study, N = 30 drivers used a level 3 (L3) automated driving function for motorways during six experimental sessions. They were free to activate/deactivate that system as they liked and to spend driving time on self-chosen side tasks. Results already show an increase of experienced trust and safety, together with an increase of time spent on side tasks between the first and fourth sessions. Furthermore, attention directed to the road decreases with growing experience with the system. The results are discussed with regard to the theory of behavioural adaptation. Results indicate that the adaptation of acceptance and usage of the highly automated driving function occurs rather quickly. At the same time, no behavioural adaptation for the reaction to take-over situations could be found.