Modeling Motion Sickness toward Comfortable Automated Vehicles

-Motion Sickness to Motion Happiness-

Abstract:

Automated driving systems release human drivers from driving tasks and allow them to enjoy their subtasks. Besides, there is concern that these changes will lead to an increase in motion sickness, and the related research studies have drawn much attention. Recall that the purpose of automated driving is to reduce the driving workload and improve productivity during transportation. An increase in motion sickness in automated driving is clearly contrary to this. Quantification of motion sickness is important to improve vehicle motion in automated driving. In this talk, I will introduce research studies on computational modeling of motion sickness as a technique for quantifying it and its application to automatic driving.

Motion Sickness Modeling with Visual Vertical Estimation and Its Application to Autonomous Personal Mobility Vehicles

Abstract:

Passengers (drivers) of level 3-5 autonomous personal mobility vehicles (APMV) and cars can perform non-driving tasks, such as reading books and smartphones, while driving. It has been pointed out that such activities may increase motion sickness. Many studies have been conducted to build countermeasures, of which various computational motion sickness models have been developed. Many of these are based on subjective vertical conflict (SVC) theory, which describes vertical changes in direction sensed by human sensory organs vs. those expected by the central nervous system. Such models are expected to be applied to autonomous driving scenarios. However, no current computational model can integrate visual vertical information with vestibular sensations. We proposed a 6 DoF SVC-VV model which add a visually perceived vertical block into a conventional six-degrees-of-freedom SVC model to predict VV directions from image data simulating the visual input of a human. Hence, a simple image-based VV estimation method is proposed. As the validation of the proposed model, this paper focuses on describing the fact that the motion sickness increases as a passenger reads a book while using an AMPV, assuming that visual vertical (VV) plays an important role. In the static experiment, it is demonstrated that the estimated VV by the proposed method accurately described the gravitational acceleration direction with a low mean absolute deviation. In addition, the results of the driving experiment using an APMV demonstrated that the proposed 6 DoF SVC-VV model could describe that the increased motion sickness experienced when the VV and gravitational acceleration directions were different.

Revisiting the design of automated vehicles for enhancing motion comfort

Abstract:

Automated vehicles (AVs) are considered as one of the major technological developments within the automotive industry, able to influence future mobility and improve life quality. At the same time, there are still important challenges that need to be overcome before AVs be part of our daily life. The ability to engage in non-driving activities during the ride is considered by consumers one of the key reasons for adoption of AVs, while motion comfort is among the most important factors for affecting public trust. Hence, a refocus on motion comfort is crucial. Regarding its assessment and modelling, there are no models incorporating posture maintenance factors, and a first attempt is briefly described in this talk. Regarding the countermeasures to enhance motion comfort, the loss of controllability is the paradigm shift from the role of humans as drivers, to the role of passengers in automated vehicles. Due to this, the users might perceive the AVs driving style more aggressive. Hence, the need to consider requirements related to motion comfort in the motion-planning control modules has emerged. At the same time, the velocity decrease as a countermeasure for motion sickness incidence could cause dissatisfaction to the occupants as the journey time will increase. Therefore, other solutions should be considered. AVs are expected to have complete interior redesign for people to engage in non-driving activities while being driven. The additional space will allow the inclusion of larger seats which could employ suspension systems that can help us further enhance motion comfort.

Are you yielding for me or not? The role of kinematics information and eHMI

Abstract:

In the future, Automated Vehicles (AVs) will need to interact with other road users, such as cyclists, pedestrians, and other vehicles. To enhance safety, improve traffic flow, and increase user acceptance and trust in AVs; pedestrians and other road users need to understand the AVs' intentions, communication, and behaviour. Kinematics cues was found to be the most important and used information when pedestrians make a crossing decision. However, perceiving kinematics cues can sometimes be challenging. This presentation will look into how implicit cues (i.e., vehicle movement) and eHMIs affect pedestrians' crossing behaviour and subjective evaluations; specifically looking at scenarios in which an approaching vehicle decelerates but does not always intend to yield. How would pedestrians react to these situations? Can they accurately judge the intention of the approaching decelerating vehicles? How do eHMIs affect pedestrians' crossing decisions?

Designing Automated Vehicles as the Prosocial Actors of Future Traffic

Abstract

Road traffic is a social situation where many participants are expected to interact with caution and mutual respect. Consequently, communication plays an important role. Typically, the communication between pedestrians and drivers is nonverbal and consists of a combination of gestures, eye contact, and body movement. With increasing levels of vehicle automation, and decreasing human control and engagement in the driving task itself, this communication will inevitably change. The question is how “mindful” and “prosocial” automated vehicles (AV) behave and communicate in traffic. In the last years, several researchers studied the design of communication between AVs and other road users. However, it remains unclear, how this impacts the perceptions of the quality of communication and impressions of mindfulness and prosociality. While the decisions related to the design of technology for automated vehicles are mostly technical, they have social consequences.

Heavy, bold and kind: Interactions between heavy automated vehicles and other road users

Abstract

A great majority of the existing research on interactions between automated vehicles and other road users addresses interactions between automated passenger cars and pedestrians. However, it is largely unknown whether insights from these studies are transferable to heavy automated vehicles or other traffic contexts. At the same time, development of heavy automated vehicles is being intensified due to business opportunities that such vehicles are likely to unlock. In this presentation, I’ll discuss potential challenges when it comes to interactions between heavy automated vehicles and other road users. I’ll also present some preliminary results from our recent studies where we explored the effect of additional external communication signals on such interactions, both in urban and highway contexts. The studies have been conducted within the research project External interaction principles for creating trust in heavy automated vehicles (co-financed by Strategic vehicle research and innovation program FFI).

Need-based User Experience Research for Automated Driving

Abstract

User acceptance and experience of technology are firmly based on satisfying basic psychological needs. A need-based development approach in the design of automated vehicles can reveal emerging problems and pain points and allow the development of control interfaces beyond what the SAE levels of automation typically offer. In this presentation, I will show the results of different user studies that utilized this method in different levels of automation, ranging from emerging trust issues at SAE level 2, user experience issues in driver take-over at SAE level 3, and the benefits of shared control at SAE level 5.

Building Trust in Automation considering Driver Uncertainty and System Uncertainty

Abstract

Inspired by the emancipation theory of trust in social psychology, it is proposed to build trust with the consideration of driver uncertainty and system uncertainty, which is demonstrated by empirical studies. For instance, driver's trust in automation can be built by developing a lane change assistance system that can adapt to driver uncertainty during decision-making in lane change maneuvers. Furthermore, it is also shown that spatially visualizing system's uncertainty can influence driver's decisions for actions, which implies the change of trust in automation behind it.

Ambient Light Displays for Automated Driving

Abstract

As automation increases, drivers are increasingly engaged in tasks other than driving. However, depending on the level of information, the passenger may still need to monitor the vehicle's decisions or be prepared to take control. Ambient displays can help here, conveying information without distracting drivers from their primary task. Even in fully automated driving, ambient displays can provide feedback on the vehicle's state to increase confidence in the vehicle or serve as a modality for novel applications with new goals, like increasing comfort or decreasing motion sickness.

Focusing on ambient light displays, I will introduce ambient displays and discuss their design possibilities in cars. In addition, I will give examples from previous research and point out future directions for ambient displays in the era of automated driving.