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.
To develop a driver assistance system with the goal to increase driving efficiency, we aimed at understanding unassisted driving behaviour. With this knowledge, we will then be able to estimate the potential of the assistance system to support drivers in avoiding unnecessary deceleration and acceleration when approaching traffic lights and to estimate the amount of influence the driver assistance system could have on normal driving. Efficient driving was defined as driving behaviour that leads to reduced fuel consumption and emissions. In a driving simulator experiment with twelve participants and a within-subjects design, drivers approached intersections while the traffic light was either solid green or solid red, or changed from red to green or from green to red during the approach. In addition, we varied whether there was a lead vehicle present and manipulated visibility through the presence or absence of fog. Driving speed, acceleration and pedal usage were analysed and interpreted due to their relation with fuel consumptions and emissions, which is well known from the literature. Participants avoided strong accelerations and decelerations when approaching a solid green traffic light compared to a changing red to green traffic light. Speed was reduced earlier, when the traffic light was solid red compared to when the traffic light changed from green to red. Higher visibility in the non-fog conditions compared to the fog condition was only an advantage in terms of more efficient driving behaviour when the traffic light phase did not change during the approach. The potential for improvements in driving efficiency was higher when drivers were in free driving compared to when following a lead vehicle. We propose that approaching traffic light intersections takes place in three phases: an orientation, a preparation and a realisation phase. A driver assistance system is expected to improve drivers' anticipation of the driving scene and could recommend efficient driving behaviour in all three phases.
Positive user experiences (PUX) in the vehicle interior will be enabled by choosing the technologies with the potential to provide such experiences. Design for PUX in general exists, but methods to assess and compare technologies regarding their PUX potential are missing. Building on the insight that fulfillment of basic psychological needs may lead to PUX (Hassenzahl et al., 2010), this paper presents the first iteration of the user-centered method Tec4UXNeeds. Tec4UXNeeds combines VR representations of technologies and half-structured interviews to identify PUX potential of technologies: which basic psychological needs a technology may fulfill and in which use cases the technology could be used to enable need fulfillment. The method is applied for two display technologies in a standardized within-subjects study (n = 27). The study investigates whether the method Tech4UX enables participants to describe whether a technology has a potential to fulfill psychological needs for them and whether the method is specific enough to find differences in need fulfillment potential between technologies described by participants.Preliminary results identified distinct levels of need fulfillment for the first and second display technology (Display on Demand & Holography). Data will be analyzed further using qualitative content analysis. The method will be optimized iteratively in the future.
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