TY - GEN
T1 - The Impact of Environmental Complexity on Drivers' Situation Awareness
AU - Park, Sami
AU - Xing, Yilun
AU - Akash, Kumar
AU - Misu, Teruhisa
AU - Boyle, Linda Ng
N1 - Publisher Copyright:
© 2022 Owner/Author.
PY - 2022/9/17
Y1 - 2022/9/17
N2 - Computational models embedded in advanced driver assistance systems (ADAS) require insights on drivers' perception and understanding of their environment. This is particularly important as vehicles become increasingly automated and the partnership between the controllers (driver or vehicle) needs to be attentive to each other's future intentions. This study investigates the impact of environmental factors (road type, lighting) on driver situation awareness (SA) using 75 real-world driving scenes viewed within a driving simulator environment. The Situational Awareness Global Assessment Technique (SAGAT) was adopted to compute SA scores from spatially continuous data. A hurdle model showed that visual complexity, which was not considered in previous SA prediction models, significantly impacted driver SA. The number of objects in the visual scene as well as in the peripheral view were also found to significantly affect driver SA. The findings of this study provide insights on environmental factors that may impact SA predictions.
AB - Computational models embedded in advanced driver assistance systems (ADAS) require insights on drivers' perception and understanding of their environment. This is particularly important as vehicles become increasingly automated and the partnership between the controllers (driver or vehicle) needs to be attentive to each other's future intentions. This study investigates the impact of environmental factors (road type, lighting) on driver situation awareness (SA) using 75 real-world driving scenes viewed within a driving simulator environment. The Situational Awareness Global Assessment Technique (SAGAT) was adopted to compute SA scores from spatially continuous data. A hurdle model showed that visual complexity, which was not considered in previous SA prediction models, significantly impacted driver SA. The number of objects in the visual scene as well as in the peripheral view were also found to significantly affect driver SA. The findings of this study provide insights on environmental factors that may impact SA predictions.
KW - Driver situational awareness
KW - Road characteristics
KW - Road density
KW - Simulated driving study
KW - Visual complexity
UR - http://www.scopus.com/inward/record.url?scp=85139461466&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85139461466&partnerID=8YFLogxK
U2 - 10.1145/3543174.3546831
DO - 10.1145/3543174.3546831
M3 - Conference contribution
AN - SCOPUS:85139461466
T3 - Main Proceedings - 14th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications, AutomotiveUI 2022
SP - 131
EP - 138
BT - Main Proceedings - 14th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications, AutomotiveUI 2022
PB - Association for Computing Machinery, Inc
T2 - 14th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications, AutomotiveUI 2022
Y2 - 17 September 2022 through 20 September 2022
ER -