TY - GEN
T1 - Continuous-time feedback device to enhance situation awareness during take-over requests in automated driving conditions
AU - Gomes, Guilherme Daniel
AU - Flynn, Ronan
AU - Murray, Niall
N1 - Publisher Copyright:
© 2022 ACM.
PY - 2022/6/14
Y1 - 2022/6/14
N2 - Conditional automation may require drivers of Autonomous Vehicles (AVs) to turn their attention away from the roads by taking part, for instance, in Non-Driving Related Tasks (NDRTs). That might cause them a lack of Situation Awareness (SA) when resuming to manual control. Consequently, Take-Over Requests (TORs) are system events intended to inform drivers when the vehicle is unable to handle an upcoming situation that is outside its Operational Design Domain (ODD) and the automated system might get disengaged, requiring driver's attention. The short time frame of time-critical TOR events impacts on the performance of the machine-to-human transition, especially when the driver is engaged with NDRTs. This can lead to dangerous driving conditions. In that context, this work proposes the demonstration of a device called Adaptive Tactile Device (ATD), capable of continuously adjust itself according to the driving conditions and smooth control transition by constantly informing the driver about road and system state based on force haptic feedback. The Continuous-Time (CT) nature of the proposed device is intended to provide adaptive feedback during automated vehicle conditions, promoting a feeling of control and possibly improving driver's Situation Awareness (SA) during TOR events. Future work will consider implementing this device on the steering wheel or driver's seat and collect the user's Quality of Experience (QoE) when using it in Virtual Reality (VR) simulations, to be compared with the user's objective and subjective metrics when receiving Discrete-Time (DT) feedback warnings previously applied in TOR research.
AB - Conditional automation may require drivers of Autonomous Vehicles (AVs) to turn their attention away from the roads by taking part, for instance, in Non-Driving Related Tasks (NDRTs). That might cause them a lack of Situation Awareness (SA) when resuming to manual control. Consequently, Take-Over Requests (TORs) are system events intended to inform drivers when the vehicle is unable to handle an upcoming situation that is outside its Operational Design Domain (ODD) and the automated system might get disengaged, requiring driver's attention. The short time frame of time-critical TOR events impacts on the performance of the machine-to-human transition, especially when the driver is engaged with NDRTs. This can lead to dangerous driving conditions. In that context, this work proposes the demonstration of a device called Adaptive Tactile Device (ATD), capable of continuously adjust itself according to the driving conditions and smooth control transition by constantly informing the driver about road and system state based on force haptic feedback. The Continuous-Time (CT) nature of the proposed device is intended to provide adaptive feedback during automated vehicle conditions, promoting a feeling of control and possibly improving driver's Situation Awareness (SA) during TOR events. Future work will consider implementing this device on the steering wheel or driver's seat and collect the user's Quality of Experience (QoE) when using it in Virtual Reality (VR) simulations, to be compared with the user's objective and subjective metrics when receiving Discrete-Time (DT) feedback warnings previously applied in TOR research.
KW - Autonomous Vehicles
KW - Quality of Experience
KW - Situation Awareness
KW - Take-Over Request
KW - Virtual Reality
UR - http://www.scopus.com/inward/record.url?scp=85137150922&partnerID=8YFLogxK
U2 - 10.1145/3524273.3532905
DO - 10.1145/3524273.3532905
M3 - Conference contribution
AN - SCOPUS:85137150922
T3 - MMSys 2022 - Proceedings of the 13th ACM Multimedia Systems Conference
SP - 319
EP - 323
BT - MMSys 2022 - Proceedings of the 13th ACM Multimedia Systems Conference
PB - Association for Computing Machinery, Inc
T2 - 13th ACM Multimedia Systems Conference, MMSys 2022
Y2 - 14 June 2022 through 17 June 2022
ER -