TY - JOUR
T1 - The Impact of Pedestrian Interactions in Intersections on the Three Levels of Drivers’ Situation Awareness
AU - Park, Sami
AU - Xing, Yilun
AU - Akash, Kumar
AU - Misu, Teruhisa
AU - Mehrotra, Shashank
AU - Boyle, Linda Ng
N1 - Publisher Copyright:
© 2024 Elsevier Ltd
PY - 2024/11
Y1 - 2024/11
N2 - Evaluating drivers’ situation awareness (SA) is important in the implementation of alert prioritization. This study investigates the relationship between driving performance measures (speed, acceleration and brake usage, steering wheel and lane deviation), pedestrian interaction (location, direction and motion), and driver SA. To achieve this, a controlled study was conducted with 56 participants using a Balanced Incomplete Block Design, where each participant drove 18 out of 48 possible intersections in a driving simulator environment. The Situational Awareness Global Assessment Technique (SAGAT) method was used to assess drivers’ SA. Mixed effects logit models were developed to examine the different SA Levels (perception, comprehension, projection). The driving performance measures were aggregated across three time windows (1, 3, and 5 s). The findings show significant contributions from both driving performance measures and pedestrian interactions in predicting driver SA. More specifically, a one-second time window was useful for predicting pedestrian direction and a three-second time window was best for predicting pedestrian location and intention to cross. The results indicate the importance of considering different time windows for predicting various levels of driver SA responses. These findings offer insights into factors to be considered in driver SA predictive models.
AB - Evaluating drivers’ situation awareness (SA) is important in the implementation of alert prioritization. This study investigates the relationship between driving performance measures (speed, acceleration and brake usage, steering wheel and lane deviation), pedestrian interaction (location, direction and motion), and driver SA. To achieve this, a controlled study was conducted with 56 participants using a Balanced Incomplete Block Design, where each participant drove 18 out of 48 possible intersections in a driving simulator environment. The Situational Awareness Global Assessment Technique (SAGAT) method was used to assess drivers’ SA. Mixed effects logit models were developed to examine the different SA Levels (perception, comprehension, projection). The driving performance measures were aggregated across three time windows (1, 3, and 5 s). The findings show significant contributions from both driving performance measures and pedestrian interactions in predicting driver SA. More specifically, a one-second time window was useful for predicting pedestrian direction and a three-second time window was best for predicting pedestrian location and intention to cross. The results indicate the importance of considering different time windows for predicting various levels of driver SA responses. These findings offer insights into factors to be considered in driver SA predictive models.
KW - Driver Behavior
KW - Driving Performance
KW - Driving Simulator
KW - Situational Awareness
KW - Vehicle-Pedestrian Interaction
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U2 - 10.1016/j.trf.2024.08.023
DO - 10.1016/j.trf.2024.08.023
M3 - Article
AN - SCOPUS:85203287415
SN - 1369-8478
VL - 107
SP - 167
EP - 180
JO - Transportation Research Part F: Traffic Psychology and Behaviour
JF - Transportation Research Part F: Traffic Psychology and Behaviour
ER -