Driver monitoring systems (DMS): The future of impaired driving management?

Abstract

Objective

Driver monitoring systems (DMS) are the next generation of vehicle safety technology. Broadly, these refer to the embedded, aftermarket wearable or vehicle-mounted devices that collect observable information about the operator to make real-time assessment of their capacity to perform the driving task. Integrating biobehavioral monitoring (primarily ocular metrics) with driving performance assessments, these systems function to infer driver state in real time to identify operator conditions that negatively affect driving (such as fatigue, inattention, or distraction).

Method

We review available methods used to infer driver state, as referenced against accepted models for optimal performance. Modeling our observations on deviation from predetermined performance thresholds used to trigger graded safety alerts, we suggest that many psychoactive substances produce alterations to biobehavioral processes including attentional and motor control, which affect performance indices in a manner already arguably captured by these technologies.

Results

Using these existing frameworks, there is considerable potential to similarly catalogue the effect of many common intoxicants known to negatively affect driving ability. This will provide safety-relevant and practical biological models for the development of next-generation multimodal DMS that integrate ocular and physiological variables sensitive to the effects of common and emergent psychoactive substances.

Conclusion

These devices have tangible potential application across all areas of transportation, including aviation, rail, and all commercial and private vehicle systems.

Keywords:

• Driver monitoring system (DMS)
• driving
• impairment
• psychoactive
• alcohol
• drug

Acknowledgments

This article is submitted on behalf of the International Council of Alcohol, Drugs and Traffic Safety working group for Driver State Monitoring Systems.

Disclosure statement

Dr. Amie Hayley is supported by a Rebecca L. Cooper Al and Val Rosenstrauss Fellowship (No. F2021894) and a Jack Brockhoff and Edwin Flack Early Career Medical Research Grant (No. 4338-2017). Blair Aitken, Frederick Vinckenbosch, Dr. Timothy Brown, and Professor Luke Downey have no conflicts of interest to declare. Dr. Shiferaw is employed by Seeing Machines; however, there are no conflicts of interest associated with this publication and no financial support was provided for this work that could have influenced its outcome.

Additional information

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.