standards, and approaches for transportation safety data and driver state prediction A review of best practices

This systematic review documents current best practices, standards, and approaches for transportation safety data analytics. While standards exist for defining measures, there are few available standards or guides for processing driving and driver data. Standards are crucial for ensuring repeatability and appropriate cost-benefit decisions. The review identified 36 relevant studies describing behavioral and physiological measures. Most studies do not comprehensively report data processing steps. Of the studies that did report data processing steps, few analyzed the impact of decisions made during data processing on algorithm performance. Most studies were conducted in a controlled simulator environment and may not generalize to naturalistic settings. The findings show that driver behavior and physiological data show efficacy for detecting fatigue, distraction, stress, and driver errors. The results of these studies may necessitate additional data processing standards and future work should focus on measuring the effects of data decisions on model performance.

Nartey, D., Alambeigi, H., McDonald, A. D., Shipp, E., Manser, M., Christensen, S., Lenneman, J. K., & Pulver, E.

Nartey, David||Alambeigi, Hananeh||McDonald, Anthony D||Shipp, Eva||Manser, Michael||Christensen, Scott||Lenneman, John K||Pulver, Elizabeth

A review of best practices, standards, and approaches for transportation safety data and driver state prediction Academic Article