Latest revision as of 19:41, 3 February 2021

Abstract

Vehicle safety is the study and practice for min- imizing the occurrences and consequences of traffic accidents. It is found that driver behaviors such as drowsiness, impaired driving and distraction are contributing factors to traffic acci- dents. In complex road surroundings, comprehensive analysis is more robust than separate evaluations which are broadly proceeded with. In this paper, we propose a vision-based nonintrusive system involving lane and driver's eye features to analyze driver behaviors. In the framework of evidence theory, evaluations of driver drowsiness and distracted and impaired driving performance are integrated to evaluate vehicle safety in real time. The system was validated in real world scenarios, and experimental results demonstrate that it is promising to improve the robustness and temporal response of vehicle safety vigilance.

Original document

The different versions of the original document can be found in:

• https://hal.archives-ouvertes.fr/hal-01667775

• http://xplorestaging.ieee.org/ielx7/6848889/6856370/06856435.pdf?arnumber=6856435,

http://dx.doi.org/10.1109/ivs.2014.6856435

• https://dblp.uni-trier.de/db/conf/ivs/ivs2014.html#LiSLL14,

https://www.researchgate.net/profile/Xuanpeng_Li/publication/263414901_Vehicle_Safety_Evaluation_based_on_Driver_Drowsiness_and_Distracted_and_Impaired_Driving_Performance_Using_Evidence_Theory/links/53fedb9b0cf23bb019be6492.pdf,

http://ieeexplore.ieee.org/document/6856435,

https://doi.org/10.1109/IVS.2014.6856435,

http://doi.org/10.1109/IVS.2014.6856435,

https://hal.archives-ouvertes.fr/hal-01667775,

https://academic.microsoft.com/#/detail/2163929212