Search

Higher levels of automated driving may offer the possibility to sleep in the driver’s seat in the car, and it is foreseeable that drivers will voluntarily or involuntarily fall asleep when they do not need to drive. Post-sleep performance impairments due to sleep inertia, a brief period of impaired cognitive performance after waking up, is a potential safety issue when drivers need to take over and drive manually. The present study assessed whether sleep inertia has an effect on driving and cognitive performance after different sleep durations. A driving simulator study with n = 13 participants was conducted. Driving and cognitive performance were analyzed after waking up from a 10–20 min sleep, a 30–60 min sleep, and after resting without sleep. The study’s results indicate that a short sleep duration does not reliably prevent sleep inertia. After the 10–20 min sleep, cognitive performance upon waking up was decreased, but the sleep inertia impairment faded within 15 min. Although the driving parameters showed no significant difference between the conditions, participants subjectively felt more tired after both sleep durations compared to resting. The small sample size of 13 participants, tested in a within-design, may have prevented medium and small effects from becoming significant. In our study, take-over was offered without time pressure, and take-over times ranged from 3.15 min to 4.09 min after the alarm bell, with a mean value of 3.56 min in both sleeping conditions. The results suggest that daytime naps without previous sleep deprivation result in mild and short-term impairments. Further research is recommended to understand the severity of impairments caused by different intensities of sleep inertia.

Automated vehicles are entering the roads and automation is applied to cars, trucks, buses, and even motorcycles today. High automation foresees transitions during driving in both directions. The driver and rider state become a critical parameter since reliable automation allows safe intervention and transit control to the automation when manual driving is not performed safely anymore. When the control transits from automation to manual an appropriate driver state needs to be identified before releasing the automated control. The detection of driver states during manual and automated driving and an appropriate design of the human-machine interaction (HMI) are crucial steps to support these transitions. State‐of‐the‐art systems do not take the driver state, personal preferences, and predictions of road conditions into account. The ADAS&ME project, funded by the H2020 Programme of the European Commission, proposes an innovative and fully adaptive HMI framework, able to support driver/rider state monitoring‐based transitions in automated driving. The HMI framework is applied in the target vehicles: passenger car, truck, bus, and motorcycle, and in seven different use cases.

The issue of driving performance after sleeping becomes highly interesting when introducing automated driving in Level 4 (SAE). It is known that fatigue as well as hypovigilance are severe risk factors for traffic accidents while an effective countermeasure is sleeping. In Level 4 automation sleeping while driving becomes a possible scenario, including the issues of transitions. Several seconds for take-over as known from distraction studies are not enough to adjust the seat, regain control and recover after sleeping. Furthermore, when analyzing human performance after sleeping, the phenomenon of sleep inertia may affect reaction time and quality of performance.Take-over time and the effect of sleep inertia are investigated in a driving simulator study with 44 participants. As independent variable, the time after waking-up until taking over is varied: The participants had to take-over the driving task one minute, seven minutes or 15 min after waking up. A control group completed the task without sleeping. Lane keeping, deviation of line during the lane change task, braking time and mental workload served as dependent variables. There were no significant differences in driving performance between the groups when looking at driving performance, safety aspects and mental workload, hence no effect of sleep inertia was measured here. Further studies should investigate individual performance of more outliers and apply even more controlled measures to understand better if and how sleep inertia can become a problem for driving and what countermeasures should be applied. The study also shows, that take-over time of one minute leads to successful, but hectic take-overs, whereas a take-over time of 15 min induces fatigue in some drivers. We conclude that powernaps have a good chance to become a safe reality in Level 4 automation with a suitable take-over time between one and seven minutes. Keywords: Autonomous driving, Sleep inertia, Take-over, Driving performance, Driving simulation

Developing proof-of-concept solutions for new integrated rescue support systems is challenging, due to the different restrictions and procedures at different installation sites. One way of dealing with this conflict is the provision of one prototype for a mininumand one for the maximum number of functions. This article presents a support module for the operators of traffic infrastructures, developed with minimum functionalities, which allows for implementing an automated critical event detection and passenger rescue, supported by the SAVE ME system. Here, its functions, the implementation and test results from two pilot tests are described.

This paper describes the framework and development process of adaptive user interfaces within the OASIS project. After presenting a rationale for user interface adaptation to address the needs and requirements of older users, the paper presents and discusses the architecture and functionality of the OASIS adaptation framework, focussing in particular on an advanced library of adaptive widgets, as well as on the process of elaborating the adaptation rules. The results of the adopted approach are discussed and hints to future developments are provided.

Automated driving is no longer a future scenario. Several automotive OEM have already presented automated vehicles, which do not require driver's constant attention on the road. But, there are still some challenges to solve before series vehicles can pass from assisted driving to highly automated driving [1; 2]. A principal research question to deal with is how to design Take-Over-Requests (TOR) with respect to the human machine interface (HMI) and reaction times to comply with a TOR. On this account, a driving simulator study with 44 drivers has been conducted at the Fraunhofer Institute for Industrial Engineering. The study took place in a highly automated driving vehicle which controlled longitudinal and lateral control on a highway scenario. Approaching a construction site different TOR strategies were presented. Within this study the time users needed to react on a TOR was measured for a highway scenario. The drivers were fully distracted by a secondary task, a challenging quiz game on a mobile phone. The different TOR strategies comprised a variation of the location for TOR presentation (integrated mobile phone or in-vehicle HMI) as well as a variation of the TOR modality (TOR with brake jerk/without brake jerk). This paper will present and discuss the results in terms of reaction times and driver behavior strategies to comply with the TOR. It delivers advice on the design of transition strategies between automated and manual driving.