TY - GEN
T1 - Redefining the Driver's Attention Gauge in Semi-Autonomous Vehicles
AU - Anwar, Raja Hasnain
AU - Anwar, Fatima Muhammad
AU - Haider, Muhammad Kumail
AU - Efrat, Alon
AU - Raza, Muhammad Taqi
N1 - Publisher Copyright:
© 2023 ACM.
PY - 2023/10/30
Y1 - 2023/10/30
N2 - Driver distraction caused by over-reliance on automotive technology is one of the leading causes of accidents in semi-autonomous vehicles. Existing driver's attention-gauging approaches are intrusive and as such emphasize constant driver engagement. In case of an urgent traffic event, they fail to measure the event's criticality and subsequently generate timely alerts. In this paper, we re-position the driver's attention-gauging approach as a way to improve the driver's situational awareness during critical situations. We exploit how a vehicle captures its surroundings information to convert an automotive decision into defining the criticality and timeliness of an alert. For this, we identify the relationship between the traffic event, the type of automotive sensing technologies, and its processing resources to capture that event to design the driver's attention gauge. We evaluate the timeliness of alerts for different traffic scenarios over a prototype built using NVIDIA Jetson Xavier AGX and Carla. Our results show that we can improve the timeliness of an alert by up to 75x as compared to existing state-of-the-art approaches, while also providing feedback on its criticality.
AB - Driver distraction caused by over-reliance on automotive technology is one of the leading causes of accidents in semi-autonomous vehicles. Existing driver's attention-gauging approaches are intrusive and as such emphasize constant driver engagement. In case of an urgent traffic event, they fail to measure the event's criticality and subsequently generate timely alerts. In this paper, we re-position the driver's attention-gauging approach as a way to improve the driver's situational awareness during critical situations. We exploit how a vehicle captures its surroundings information to convert an automotive decision into defining the criticality and timeliness of an alert. For this, we identify the relationship between the traffic event, the type of automotive sensing technologies, and its processing resources to capture that event to design the driver's attention gauge. We evaluate the timeliness of alerts for different traffic scenarios over a prototype built using NVIDIA Jetson Xavier AGX and Carla. Our results show that we can improve the timeliness of an alert by up to 75x as compared to existing state-of-the-art approaches, while also providing feedback on its criticality.
KW - human-computer interaction.
KW - semi-autonomous vehicles
UR - http://www.scopus.com/inward/record.url?scp=85209066681&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85209066681&partnerID=8YFLogxK
U2 - 10.1145/3616388.3617544
DO - 10.1145/3616388.3617544
M3 - Conference contribution
AN - SCOPUS:85209066681
T3 - MSWiM 2023 - Proceedings of the International ACM Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems
SP - 307
EP - 311
BT - MSWiM 2023 - Proceedings of the International ACM Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems
PB - Association for Computing Machinery, Inc
T2 - 26th ACM International Conference on Modelling, Analysis, and Simulation of Wireless and Mobile Systems, MSWiM 2023
Y2 - 30 October 2023 through 3 November 2023
ER -