“…To maintain safety, the maximum speed was set to 30 km/h, while two experimenters (one on the passenger seat, another outside) and a redundant sensor system (observing the driving robot) could stop the vehicle anytime. The TORs were issued by the USE software framework (Schartmüller et al, 2017), which also synchronized the involved technical components (measurement devices and NDRT prototypes) to provide a reproducible setting. More details on the setup can be found in A.-K. Frison, P. Wintersberger, C. Schartmüller, and A. Riener (2019).Figure 1.Study setup: Driving robot operating the vehicle.…”
Objective Investigating take-over, driving, non-driving related task (NDRT) performance, and trust of conditionally automated vehicles (AVs) in critical transitions on a test track. Background Most experimental results addressing driver take-over were obtained in simulators. The presented experiment aimed at validating relevant findings while uncovering potential effects of motion cues and real risk. Method Twenty-two participants responded to four critical transitions on a test track. Non-driving related task modality (reading on a handheld device vs. auditory) and take-over timing (cognitive load) were varied on two levels. We evaluated take-over and NDRT performance as well as gaze behavior. Further, trust and workload were assessed with scales and interviews. Results Reaction times were significantly faster than in simulator studies. Further, reaction times were only barely affected by varying visual, physical, or cognitive load. Post-take-over control was significantly degraded with the handheld device. Experiencing the system reduced participants’ distrust, and distrusting participants monitored the system longer and more frequently. NDRTs on a handheld device resulted in more safety-critical situations. Conclusion The results confirm that take-over performance is mainly influenced by visual-cognitive load, while physical load did not significantly affect responses. Future take-over request (TOR) studies may investigate situation awareness and post-take-over control rather than reaction times only. Trust and distrust can be considered as different dimensions in AV research. Application Conditionally AVs should offer dedicated interfaces for NDRTs to provide an alternative to using nomadic devices. These interfaces should be designed in a way to maintain drivers’ situation awareness. Précis This paper presents a test track experiment addressing conditionally automated driving systems. Twenty-two participants responded to critical TORs, where we varied NDRT modality and take-over timing. In addition, we assessed trust and workload with standardized scales and interviews.
“…To maintain safety, the maximum speed was set to 30 km/h, while two experimenters (one on the passenger seat, another outside) and a redundant sensor system (observing the driving robot) could stop the vehicle anytime. The TORs were issued by the USE software framework (Schartmüller et al, 2017), which also synchronized the involved technical components (measurement devices and NDRT prototypes) to provide a reproducible setting. More details on the setup can be found in A.-K. Frison, P. Wintersberger, C. Schartmüller, and A. Riener (2019).Figure 1.Study setup: Driving robot operating the vehicle.…”
Objective Investigating take-over, driving, non-driving related task (NDRT) performance, and trust of conditionally automated vehicles (AVs) in critical transitions on a test track. Background Most experimental results addressing driver take-over were obtained in simulators. The presented experiment aimed at validating relevant findings while uncovering potential effects of motion cues and real risk. Method Twenty-two participants responded to four critical transitions on a test track. Non-driving related task modality (reading on a handheld device vs. auditory) and take-over timing (cognitive load) were varied on two levels. We evaluated take-over and NDRT performance as well as gaze behavior. Further, trust and workload were assessed with scales and interviews. Results Reaction times were significantly faster than in simulator studies. Further, reaction times were only barely affected by varying visual, physical, or cognitive load. Post-take-over control was significantly degraded with the handheld device. Experiencing the system reduced participants’ distrust, and distrusting participants monitored the system longer and more frequently. NDRTs on a handheld device resulted in more safety-critical situations. Conclusion The results confirm that take-over performance is mainly influenced by visual-cognitive load, while physical load did not significantly affect responses. Future take-over request (TOR) studies may investigate situation awareness and post-take-over control rather than reaction times only. Trust and distrust can be considered as different dimensions in AV research. Application Conditionally AVs should offer dedicated interfaces for NDRTs to provide an alternative to using nomadic devices. These interfaces should be designed in a way to maintain drivers’ situation awareness. Précis This paper presents a test track experiment addressing conditionally automated driving systems. Twenty-two participants responded to critical TORs, where we varied NDRT modality and take-over timing. In addition, we assessed trust and workload with standardized scales and interviews.
“…To integrate and synchronize prototyping hardware with driving simulation and evaluation systems (e.g., eyetrackers), all components utilize accessible operating systems (i.e., Windows) and/or open and standardized software protocols like MQTT [6]. Additionally, the USE software framework [10] serves as a central backend.…”
Section: Flexible Prototyping and Evaluationmentioning
Figure 1: Left: The Driving Experience Lab at the Landesgartenschau (national garden festival) in Ingolstadt, Germany. Right: A study participant using the multi-function steering wheel and large-format head-up display for interacting the simulated automated vehicle.
“…All involved technical components (smartphone for the DRT, scent delivery device, driving simulation, etc.) were synchronized by in-house software to enable time-critical measurements and repeatability (as suggested by [59]).…”
Section: Reliability Display and Olfactory Devicementioning
Overreliance in technology is safety-critical and it is assumed that this could have been a main cause of severe accidents with automated vehicles. To ease the complex task of permanently monitoring vehicle behavior in the driving environment, researchers have proposed to implement reliability/uncertainty displays. Such displays allow to estimate whether or not an upcoming intervention is likely. However, presenting uncertainty just adds more visual workload on drivers, who might also be engaged in secondary tasks. We suggest to use olfactory displays as a potential solution to communicate system uncertainty and conducted a user study (N=25) in a high-fidelity driving simulator. Results of the experiment (conditions: no reliability display, purely visual reliability display, and visual-olfactory reliability display) comping both objective (task performance) and subjective (technology acceptance model, trust scales, semi-structured interviews) measures suggest that olfactory notifications could become a valuable extension for calibrating trust in automated vehicles.
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