Driver decision quality in take-overs is critical for effective human-Autonomous Driving System (ADS) collaboration. However, current research lacks detailed analysis of its variations. This paper introduces two metrics--Actual Achieved Gain (AAG) and Optimal Perceived Gain (OPG)--to assess decision quality, with OPG representing optimal decisions and AAG reflecting actual outcomes. Both are calculated as weighted averages of perceived gains and losses, influenced by ADS accuracy. Study 1 (N=315) used a 21-point Thurstone scale to measure perceived gains and losses—key components of AAG and OPG—across typical tasks: route selection, overtaking, and collision avoidance. Studies 2 (N=54) and 3 (N=54) modeled decision quality under varying ADS accuracy and decision time. Results show with sufficient time (>3.5s), AAG converges towards OPG, indicating rational decision-making, while limited time leads to intuitive and deterministic choices. Study 3 also linked AAG-OPG deviations to irrational behaviors. An intervention study (N=8) and a pilot (N=4) employing voice alarms and multi-modal alarms based on these deviations demonstrated AAG's potential to improve decision quality.
As passengers spend more time in vehicles, the demand for non-driving related tasks (NDRTs) increases. In-car Augmented Reality (AR) has the potential to enhance passenger experiences by enabling interaction with the environment through NDRTs using world-fixed Points of Interest (POIs). However, the effectiveness of existing interaction techniques and visualization methods for in-car AR remains unclear. Based on a survey (N=110) and a pre-study (N=10), we developed an interactive in-car AR system using a video see-through head-mounted display to engage with POIs via eye-gaze and pinch. Users could explore passed and upcoming POIs using three visualization techniques: List, Timeline, and Minimap. We evaluated the system's feasibility in a field study (N=21). Our findings indicate general acceptance of the system, with the List visualization being the preferred method for exploring POIs. Additionally, the study highlights limitations of current AR hardware, particularly the impact of vehicle movement on 3D interaction.
We explore the feasibility of active user-applied locomotion in virtual reality (VR) within in-car environments, diverging from previous in-car VR research that synchronized virtual motion with the car's movement. Through a two-step study, we examined the effects of locomotion methods on user experience in dynamic vehicle environments and evaluated contextual cues designed to mitigate sensory mismatch caused by vehicle motion. The first study evaluated five locomotion methods, identifying joystick-based navigation as the most suitable for in-car use due to its low physical demand and stability. The second study focused on designing and testing contextual cues that translate physical sensations of vehicle motion into virtual effects without limiting the user’s freedom of movement, with results demonstrating their effectiveness in reducing motion sickness and enhancing presence. We conclude with initial insights and design considerations for expanding upon our findings in regards to enabling active locomotion in in-car VR.
The absence of a human operator in automated vehicles (AVs) may require external Human-Machine Interfaces (eHMIs) to facilitate communication with other road users in uncertain scenarios, for example, regarding the right of way.
Given the plethora of adjustable parameters, balancing visual and auditory elements is crucial for effective communication with other road users. With N=37 participants, this study employed multi-objective Bayesian optimization to enhance eHMI designs and improve trust, safety perception, and mental demand. By reporting the Pareto front, we identify optimal design trade-offs. This research contributes to the ongoing standardization efforts of eHMIs, supporting broader adoption.
With the transition to fully autonomous vehicles, non-driving related tasks (NDRTs) become increasingly important, allowing passengers to use their driving time more efficiently. In-car Augmented Reality (AR) gives the possibility to engage in NDRTs while also allowing passengers to engage with their surroundings, for example, by displaying world-fixed points of interest (POIs). This can lead to new discoveries, provide information about the environment, and improve locational awareness. To explore the optimal visualization of POIs using in-car AR, we conducted a field study (N = 38) examining six parameters: positioning, scaling, rotation, render distance, information density, and appearance. We also asked for intention of use, preferred seat positions and preferred automation level for the AR function in a post-study questionnaire. Our findings reveal user preferences and general acceptance of the AR functionality. Based on these results, we derived UX-guidelines for the visual appearance and behavior of location-based POIs in in-car AR.
As vehicle automation technology continues to mature, there is a
necessity for robust remote monitoring and intervention features.
These are essential for intervening during vehicle malfunctions,
challenging road conditions, or in areas that are difficult to navigate.
This evolution in the role of the human operator—from a constant
driver to an intermittent teleoperator—necessitates the development of suitable interaction interfaces. While some interfaces were
suggested, a comparative study is missing. We designed, implemented, and evaluated three interaction concepts (path planning, trajectory guidance, and waypoint guidance) with up to four concurrent requests of automated vehicles in a within-subjects study
with N=23 participants. The results showed a clear preference for
the path planning concept. It also led to the highest usability but
lower satisfaction. With trajectory guidance, the fewest requests
were resolved. The study’s findings contribute to the ongoing development of HMIs focused on the remote assistance of automated vehicles.
Cyclists need interfaces such as on-vehicle displays or augmented-reality (AR) glasses for effective communication with autonomous vehicles (AVs) when human drivers are no longer present. Interfaces must handle complex situations involving multiple AVs around a cyclist. Holistic AV-Cyclist Interfaces (HACIs) are a novel solution; they group interfaces into a multimodal interconnected system to support the rider. However, the best way to present information is uncertain. We explored this in a scenario with three AVs using CycleARcade, a new multi-user AR platform for designing and evaluating HACIs. Cyclists and HCI researchers collaboratively created and tested HACIs within CycleARcade through a novel iterative participatory design method. We synthesised three HACIs from this process and assessed them with riders in CycleARcade. Participants preferred HACIs with AR displays integrated into the environment to avoid road distractions, paired with spatial audio communicating AV proximity. These findings provide crucial input for the real-world deployment of AVs.