Dynamic Driver Risk Management: Integrating AFDD and FMEA

Abstract

Driving accidents continue to be a major global concern, with many fatalities linked to human-factor limitations such as cognitive strain, fatigue, distraction, and other behavioral or physiological impairments. Although tools like Intelligent Speed Adaptation (ISA), GPS alerts, and driver-behavior programs help manage external road risks, they often do not address the internal driver-state conditions that trigger unsafe behaviors, including speeding, drifting out of lane, delayed braking, or reduced hazard awareness. This paper introduces an integrated, intelligence-based safety framework that combines Automatic Fatigue and Distraction Detection (AFDD) with a Dynamic Failure Mode and Effects Analysis (FMEA) model to more effectively reduce accident risk. The AFDD system continuously monitors physiological, behavioral, and environmental cues to identify early signs of driver impairment. These real-time observations are then fed into the dynamic FMEA, which updates the Occurrence and Detection ratings and produces a more accurate, continuously refreshed Risk Priority Number (RPN). Experimental results show that this combined approach can lower RPN values by 42–62% across several driver-related failure modes, including those associated with fatigue, distraction, visibility challenges, cognitive workload, and unsafe driving behaviors. By replacing static risk assessments with a real-time predictive approach, the framework enables safety decisions to be made more rapidly and with improved accuracy. This approach not only enhances human-centered transportation safety but also sets the stage for future improvements, including integration with V2X networks and fleet-wide risk management.

Keywords: Driving Safety, Human Factors, Driver Fatigue, Driver Distraction, Automatic Fatigue And Distraction Detection (AFDD), Dynamic FMEA.

DOI: 10.54941/ahfe1007789