Multi-sensor driver monitoring for drowsiness prediction

Schwarz, Chris; Gaspar, John; Yousefian, Reza

doi:10.1080/15389588.2023.2164839

Cited by 5 publications

(1 citation statement)

References 14 publications

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“…An investigation [22] focused on elderly individuals in near-side impact crashes revealed the need for further analysis in establishing injury thresholds. A study [23] on drowsy-driving detection models incorporated multiple data sources and achieved good accuracy in predicting drowsiness. A study [24] evaluated the crash reductions achieved in cars equipped with automatic emergency braking (AEB) systems with pedestrian and bicyclist detection.…”

Section: Ongoing Funded Research Projectsmentioning

confidence: 99%

Safety in Traffic Management Systems: A Comprehensive Survey

Dash

et al. 2023

Designs

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Traffic management systems play a vital role in ensuring safe and efficient transportation on roads. However, the use of advanced technologies in traffic management systems has introduced new safety challenges. Therefore, it is important to ensure the safety of these systems to prevent accidents and minimize their impact on road users. In this survey, we provide a comprehensive review of the literature on safety in traffic management systems. Specifically, we discuss the different safety issues that arise in traffic management systems, the current state of research on safety in these systems, and the techniques and methods proposed to ensure the safety of these systems. We also identify the limitations of the existing research and suggest future research directions.

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Section: Ongoing Funded Research Projectsmentioning

confidence: 99%