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How to Increase Automated Vehicles’ Acceptance through In-Vehicle Interaction Design: A Review
(2020)
Automated vehicles (AVs) are on the edge of being available on the mass market. Research often focuses on technical aspects of automation, such as computer vision, sensing, or artificial intelligence. Nevertheless, researchers also identified several challenges from a human perspective that need to be considered for a successful introduction of these technologies. In this paper, we first analyze human needs and system acceptance in the context of AVs. Then, based on a literature review, we provide a summary of current research on in-car driver-vehicle interaction and related human factor issues. This work helps researchers, designers, and practitioners to get an overview of the current state of the art.
Self-driving cars will relief the human from the driving task. Nevertheless, the human might want to intervene in the driving process and thus needs the possibility to control the car. Switching back to fully manual controls is uncomfortable once being passive and engaging in non-driving-related activities. A more comfortable way is controlling the car with elemental maneuvers (e.g., "turn left" or "stop"). Whereas touch interaction concepts exist, contactless interaction through voice and mid-air gestures has not yet been explored for maneuver-based car control. In this paper, we, therefore, compare the general eligibility of voice and mid-air gesture with touch interaction as the primary maneuver selection mechanism in a driving simulator study. Our results show high usability for all modalities. Contactless interaction leads to a more positive emotional perception of the interaction, yet mid-air gestures lead to higher task load. Overall, voice and touch control are preferred over mid-air gestures by most users.
Currently, car assistant systems mainly try to prevent accidents. Increasing built-in car technology also extends the potential applications in vehicles. Future cars might have virtual windshields that augment the traffic or individual virtual assistants interacting with the user. In this paper, we explore the potential of an assistant system that helps the car’s occupants to calm down and reduce stress when they experience an accident in front of them. We present requirements from a discussion (N= 11) and derive a system design from them. Further, we test the system design in a video-based simulator study (N= 43). Our results indicate that an accident support system increases perceived control and trust and helps to calm down the user.
Human emotion detection in automated vehicles helps to improve comfort and safety. Research in the automotive domain focuses a lot on sensing drivers' drowsiness and aggression. We present a new form of implicit driver-vehicle cooperation, where emotion detection is integrated into an automated vehicle's decision-making process. Constant evaluation of the driver's reaction to vehicle behavior allows us to revise decisions and helps to increase the safety of future automated vehicles.
The way we communicate with autonomous cars will fundamentally change as soon as manual input is no longer required as back-up for the autonomous system. Maneuver-based driving is a potential way to allow still the user to intervene with the autonomous car to communicate requests such as stopping at the next parking lot. In this work, we highlight different research questions that still need to be explored to gain insights into how such control can be realized in the future.
In automated vehicles, it is essential to feedforward motion intentions to users so that they understand the vehicle’s actions. Otherwise, non-transparency limits situation awareness and leads to mistrust. In this work, we are communicating the vehicle’s actions to the user either by displaying icons (planar HUD) or through augmented reality (contact-analog HUD) to increase transparency. We developed both concepts in a user-centered design process. Further, we evaluated them in two subsequent user studies (N = 27). In the first study, we focused on UX and trust in higher automation levels (cf. SAE level 3-5). In the second study, we focused on safety and error prevention in lower automation levels (cf. SAE levels 1-2). Our results show that both visualizations increase UX and trust in an automated system. Nevertheless, the AR approach outperforms the icon-based approach by achieving higher user experience as well as faster and less error-prone take-overs of participants.
Future mobility will be highly automated, multimodal, and ubiquitous and thus have the potential to address a broader range of users. Yet non-average users with special needs are often underrepresented or simply not thought of in design processes of vehicles and mobility services, leading to exclusion from standard transportation. In consequence, it is crucial for designers of such vehicles and services to consider the needs of non-average users from the begin on. In this paper, we present a design framework that helps designers taking the perspective and thinking of the needs of non-average users. We present a set of exemplary applications from the literature and interviews and show how they fit into the framework, indicating room for further developments. We further demonstrate how the framework supports in designing a mobility service in a fictional design process. Overall, our work contributes to universal design of future mobility.
Understanding user needs and behavior in automated vehicles (AVs) while traveling is essential for future in-vehicle interface and service design. Since AVs are not yet market-ready, current knowledge about AV use and perception is based on observations in other transportation modes, interviews, or surveys about the hypothetical situation. In this paper, we close this gap by presenting real-world insights into the attitude towards highly automated driving and non-driving-related activities (NDRAs). Using a Wizard of Oz AV, we conducted a real-world driving study (N= 12) with six rides per participant during multiple days. We provide insights into the users’ perceptions and behavior. We found that (1) the users’ trust a human driver more than a system,(2) safety is the main acceptance factor, and (3) the most popular NDRAs were being idle and the use of the smartphone.