TY - GEN
T1 - Spot'Em
T2 - 15th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, AutomotiveUI 2023
AU - Wintersberger, Philipp
AU - Rathmayr, Michael
AU - Lingler, Alexander
N1 - Publisher Copyright:
© 2023 ACM.
PY - 2023/9/18
Y1 - 2023/9/18
N2 - Appropriate monitoring and successfully intervening when automation fails is one of the most critical issues in level 2 automated driving, since drivers suffer from low situation awareness when using such systems. To counter, we present a gamified in-vehicle interface based on ideas from previous work, where drivers have to support the vehicle by pointing at other traffic objects in the environment. We hypothesized that this system could help drivers in the monitoring task, maintain their situation awareness, and result in lower crash rates. We implemented a prototype of this system and evaluated it in a lab study with N=20 participants. The results indicate that participants were looking more intensively at lead vehicles and performed stronger braking actions. However, there was no measurable benefit on situation awareness and intervention performance in critical situations. We conclude by discussing differences to related experiments and present future ideas.
AB - Appropriate monitoring and successfully intervening when automation fails is one of the most critical issues in level 2 automated driving, since drivers suffer from low situation awareness when using such systems. To counter, we present a gamified in-vehicle interface based on ideas from previous work, where drivers have to support the vehicle by pointing at other traffic objects in the environment. We hypothesized that this system could help drivers in the monitoring task, maintain their situation awareness, and result in lower crash rates. We implemented a prototype of this system and evaluated it in a lab study with N=20 participants. The results indicate that participants were looking more intensively at lead vehicles and performed stronger braking actions. However, there was no measurable benefit on situation awareness and intervention performance in critical situations. We conclude by discussing differences to related experiments and present future ideas.
KW - Automated Driving
KW - Data Labeling
KW - Interactive Machine Learning
KW - Monitoring
KW - Situation Awareness
KW - Supervisory Control
UR - http://www.scopus.com/inward/record.url?scp=85173793013&partnerID=8YFLogxK
U2 - 10.1145/3580585.3607163
DO - 10.1145/3580585.3607163
M3 - Conference contribution
AN - SCOPUS:85173793013
T3 - ACM International Conference Proceeding Series
SP - 72
EP - 80
BT - 15th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, AutomotiveUI 2023 - Main Conference Proceedings
PB - Association for Computing Machinery
Y2 - 18 September 2023 through 21 September 2023
ER -