Citation

Choudhary P, Velaga NR. IEEE Trans. Intel. Transp. Syst. 2019; 20(11): 4055-4065.

Copyright

(Copyright © 2019, IEEE (Institute of Electrical and Electronics Engineers))

DOI

10.1109/TITS.2018.2879968

PMID

unavailable

Abstract

The present study attempts to statistically model and quantify the contribution of two sets of potential factors to accident risk: 1) real-time driving behavioral characteristics and 2) distraction conditions. The effects of phone and music player usage on reaction times and accident probabilities were modeled for two events: 1) a pedestrian crossing (started in peripheral vision) and 2) sudden braking (started in central vision) in an urban scenario. In total, 90 subjects drove for baseline and conversation conditions. In all, 70 and 78 of the 90 subjects drove for texting and music player tasks, respectively. Reaction times and accident probabilities were modeled with the help of generalized linear mixed model approach. The results showed that the reaction time increased by 42%, 113%, and 62% due to the presence of conversation, texting, and music player operations, respectively, for the pedestrian crossing event. Whereas, the increments were lower for the sudden braking event. Drivers with higher approach speed detected the events faster. The accident probability increased due to the texting tasks and by operating the player during driving. An increase in reaction time also led to higher risk for the pedestrian crossing event. For the sudden braking event, the effect of music-listening was not significant, but the accident risk increased if the driver approached the event at high speed. Overall, the results suggest that the algorithm for designing a collision warning system should consider the real-time driving characteristics based on event type and quantify the effects of distraction based on the type of distraction.

Language: en

Keywords

accident probabilities; accident probability; accident risk; Accidents; braking; central vision; comparative analysis; conversation conditions; Degradation; Driver distraction; driver information systems; driving behavioral characteristics; generalized linear mixed model approach; higher approach speed; music; Music; music player operations; music player tasks; music player usage; music-listening; music-player; pedestrian crossing event; performance degradation; peripheral vision; phone-use; probability; Probability; reaction time; reaction times; real-time behavioral characteristics; real-time driving characteristics; Real-time systems; road safety; road traffic; road vehicles; statistical analysis; sudden braking event; sudden hazardous events; Task analysis; texting tasks; traffic engineering computing; vehicle dynamics; Vehicles