CARISSMA Institute of Automated Driving (C-IAD)
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Currently, a significant gap exists between academic and industrial research in automated driving development. Despite this, there is common sense that cooperative control approaches in automated vehicles will surpass the previously favored takeover paradigm in most driving situations due to enhanced driving performance and user experience. Yet, the application of these concepts in real driving situations remains unclear, and a holistic approach to driving cooperation is missing. Existing research has primarily focused on testing specific interaction scenarios and implementations. To address this gap and offer a contemporary perspective on designing human–vehicle cooperation in automated driving, we have developed a three-part taxonomy with the help of an extensive literature review. The taxonomy broadens the notion of driving cooperation towards a holistic and application-oriented view by encompassing (1) the “Cooperation Use Case”, (2) the “Cooperation Frame”, and (3) the “Human–Machine Interface”. We validate the taxonomy by categorizing related literature and providing a detailed analysis of an exemplar paper. The proposed taxonomy offers designers and researchers a concise overview of the current state of driver cooperation and insights for future work. Further, the taxonomy can guide automotive HMI designers in ideation, communication, comparison, and reflection of cooperative driving interfaces.
Partially automated driving functions (SAE Level 2) can control a vehicle’s longitudinal and lateral movements. However, taking over the driving task involves automation risks that the driver must manage. In severe accidents, the driver’s ability to avoid a collision must be assessed, considering their expected reaction behavior. The primary goal of this study is to generate essential data on driver reaction behavior in case of malfunctions in partially automated driving functions for use in legal affairs. A simulator study with two scenarios involving 32 subjects was conducted for this purpose. The first scenario investigated driver reactions to system limitations during cornering. The results show that none of the subjects could avoid leaving their lane and moving into the oncoming lane and, therefore, could not control the situation safely. Due to partial automation, we could also identify a new part of the reaction time, the hands-on time, which leads to increased steering reaction times of 1.18 to 1.74 s. The second scenario examined driver responses to phantom braking caused by AEBS. We found that 25 of the 32 subjects could not override the phantom braking by pressing the accelerator pedal, although 16 subjects were informed about the system analog to the actual vehicle manuals. Overall, the study suggests that the current legal perspective on vehicle control and the expected driver reaction behavior for accident avoidance should be reconsidered.
Partially automated driving functions (SAE Level 2) can control a vehicle's longitudinal and lateral movements. However, taking over the driving task involves automation risks that the driver must manage. In severe accidents, the driver's ability to avoid a collision must be assessed, considering their expected reaction behavior. The primary goal of this study is to generate essential data on driver reaction behavior in case of malfunctions in partially automated driving functions for use in legal affairs. A simulator study with two scenarios involving 32 subjects was conducted for this purpose. The first scenario investigated driver reactions to system limitations during cornering. The second scenario examined driver responses to phantom braking caused by the AEBS. As a result, the first scenario shows that none of the subjects could control the situation safely. Due to partial automation, we could also identify a new part of the reaction time, the hands-on time, which leads to increased steering reaction times of 1.18 to 1.74 seconds. In the second scenario, we found that 25 of the 32 subjects could not override the phantom braking by pressing the accelerator pedal, although 16 subjects were informed about the system analog to the actual vehicle manuals. Overall, the study suggests that the current legal perspective on vehicle control and the expected driver reaction behavior for accident avoidance should be reconsidered.
We're in This Together: Exploring Explanation Needs and Methods in Shared Automated Shuttle Buses
(2023)
Since the onset of the Covid-19 pandemic, a dramatic increase in mHealth application (app) downloads has been documented. However, overall dwell retention for fitness apps is low, so gamification techniques are used within apps with the goal of positively influencing the user experience and ultimately the user’s motivation. The so-called flow, which is related to intrinsic motivation, has been little explored in the context of cycling apps. Therefore, we conducted a quasi-experimental cycling study with 34 cyclists (20 female, 14 male; 19 to 57 years old) who tested the adidas Running by Runtastic (Adidas Runtastic), Komoot, and Strava cycling apps during a 20-minute bike ride. After testing each cycling app, they completed the User Experience Questionnaire (UEQ) and the Flow State Scale-2 Short (FSS-2S). Our results showed no significant differences across the six factors of the UEQ, nor across the total score of the FSS-2S. Thus, we conclude that the three cycling apps Adidas Runta stic, Komoot, and Strava are perceived and rated almost equally by female and male cyclists.
This report documents the program and the outcomes of Dagstuhl Seminar 22222 “Radical Innovation and Design for Connected and Automated Vehicles”.
Automated driving will most likely be the next big change in individual mobility. While research is still primarily concerned with technical challenges of Automated Driving Systems (ADS), HCI researchers and designers already started to develop concepts on how to use privately owned ADS as a space for non-driving-related activities, going beyond what is possible today. There is, however, room to think about creative ways to use automated vehicles (AV) and connected technology towards the public interest beyond incremental changes, which is what we addressed in this seminar. We challenged the current generative/evaluative research approach for automated driving systems against a radical innovations attempt and questioned whether the current incremental research approach is appropriate for the development of future vehicles. As an integral part of the seminar we wanted participants to learn from each other and to disseminate each individual’s experience for boosting subsequent research by trying out different methods that support the “out of the box” thinking (e.g., brainwriting, bodystorming, focus groups, World Café, amongst others).
A mobile application for resolving bicyclist and automated vehicle interactions at intersections
(2022)