This work investigates which conditions lead to co-driver discomfort aside from classical motion sickness, what characterizes uncomfortable situations, and why these conditions have a negative effect. The automobile is called a “passenger vehicle” as its main purpose is the transportation of people. However, passengers in the car are rarely considered in research concerning driving discomfort. The few studies in this area focus on driver discomfort, automated vehicles, or driver assistant systems. An earlier public survey indicated that discomfort is also a relevant problem for co-drivers. In this paper, these results are confirmed and extended through an online questionnaire with N = 119 participants and a detailed follow-up interview study with N = 24 participants was conducted. The results of the online questionnaire show that co-driver discomfort is a widespread problem (88%). The interviews indicate that the driving style is one factor contributing to co-driver discomfort, in particular close following or fast driving. In those situations, participants experienced a feeling of being exposed, which additionally contributed to their discomfort. Uncomfortable situations were also perceived as safety critical. A model for possible cognitive origins of discomfort in co-drivers, extending theories from the areas of stress and self-regulation, is developed based on the results. Co-driver discomfort is a common problem, highlighting the relevance of further research on supporting co-drivers. The reported correlations and the proposed model can help to explain the origin of this discomfort. The results provide a foundation for the future design of interventions like human machine interfaces aiming at reducing co-driver discomfort.

Automated driving vehicles will allow all occupants to spend their time with various non-driving related tasks like relaxing, working, or reading during the journey. However, a significant percentage of people is susceptible to motion sickness, which limits the comfort of engaging in those tasks during automated driving. Therefore, it is necessary to investigate the phenomenon of motion sickness during automated driving and to develop countermeasures. As most existing studies concerning motion sickness are fundamental research studies, a methodology for driving studies is yet missing. This paper discusses methodological aspects for investigating motion sickness in the context of driving including measurement tools, test environments, sample, and ethical restrictions. Additionally, methodological considerations guided by different underlying research questions and hypotheses are provided. Selected results from own studies concerning motion sickness during automated driving which were conducted in a motion-based driving simulation and a real vehicle are used to support the discussion.

ADAS have the potential to optimize safety and efficiency in road traffic. In order to reach this objective, human-centered design principles have to be considered. Therefore, the effects of such devices on driver behavior and emotion are often analyzed quantitatively by using driving simulator studies that measure effects on individual level. But traditional driving simulation reaches its limits, if the effects of cooperative ADAS on the interactions of several road users (group level) should be analyzed. The paper describes the approach of a multi-driver simulation for the analysis of two cooperative ADAS that assist the driver: merging assistant and hazard warning. Results of experimental studies are presented and show positive as well as negative effects of both systems on driving behavior and interactions as well as drivers' emotional response.

ADAS have the potential to optimize safety and efficiency in road traffic. In order to reach this objective, human-centered design principles have to be considered. Therefore, the effects of such devices on driver behavior and emotion are often analyzed quantitatively by using driving simulator studies that measure effects on individual level. But traditional driving simulation reaches its limits, if the effects of cooperative ADAS on the interactions of several road users (group level) should be analyzed. The paper describes the approach of a multi-driver simulation for the analysis of two cooperative ADAS that assist the driver: merging assistant and hazard warning. Results of experimental studies are presented and show positive as well as negative effects of both systems on driving behavior and interactions as well as drivers' emotional response.

A few studies have demonstrated positive effects of Advanced Parking As-sists (APA) on driver comfort and parking performance. Learning effects while handling the APA system and possible transfer effects on manual parking have not been discussed yet. In this study, N = 18 subjects parked parallel in a test area (26 manoeuvres) and in real traffic (9 manoeuvres). One half of the manoeuvres was done without the parking assist, one half with a semi-autonomous APA system which utilized automatic steering. The APA system did not control speed by accelerating or braking. Consistent with earlier studies, the APA system facilitates parking. Learning effects particularly ap-pear in glance behaviour and maximum velocity during the first parking motion. Using the APA over a large number of manoeuvres might influence parking without assistant: The more manoeuvres are done with the APA, the more often the drivers look into the display during manual parking.