The Virginia Tech Transportation Institute (VTTI) research team conducted a comprehensive literature review to identify the types of fatigue detection technologies that are commercially available and detail their respective features for application in CMV operations. The literature review discusses technologies that (i) detect fatigue using physiological sensors from the driver; (ii) use computer vision to monitor driver behavior and positioning; (iii) record and monitor driver performance metrics, and (iv) combine multiple measures in a hybrid approach to monitoring and detection. The literature review does not discuss technologies that predict fatigue using neurobiological predictive modeling or scheduling algorithms. The literature review also does not discuss crash-mitigation technologies such as automatic braking systems, lane departure warning systems, and forward collision warning systems that are designed to cover a broad spectrum of crash types in addition to fatigue-related crashes. Informed by the technologies discussed in the literature review, the VTTI research team developed an inventory of fatigue detection technologies that are currently commercially available for implementation in CMV operations. The inventory details their specific features, capabilities, limitations, applications, and efficacy (where available). Availability, cost, practicality for the industry, and stage of development for testing or implementation are also detailed for each technology. Finally, each technology is categorized, taking into consideration factors such as empirical validation, effectiveness, practicality, and availability. Development of this central and thorough inventory of fatigue detection technologies and their specific capabilities and applications for CMV operations is a helpful resource for the industry and may inform future research and evaluations using the most promising fatigue detection technologies. (Author/publisher)
Abstract