Driver Age and Its Effect on Key Driving Metrics: Insights from Dynamic Vehicle Data

TL;DR

This study uses naturalistic driving data to analyze how age affects driving behaviors like speed adherence and deceleration, aiming to improve safety interventions for older drivers with potential applications in ADAS.

Contribution

It introduces age-specific benchmarks for driving behaviors using NDD, facilitating anomaly detection and tailored safety interventions, which is novel in real-world driving analysis.

Findings

Significant differences in speed adherence at 75mph between age groups

Established benchmarks for senior and young drivers' behaviors

Potential for ADAS to detect anomalies and improve safety

Abstract

By 2030, the senior population aged 65 and older is expected to increase by over 50%, significantly raising the number of older drivers on the road. Drivers over 70 face higher crash death rates compared to those in their forties and fifties, underscoring the importance of developing more effective safety interventions for this demographic. Although the impact of aging on driving behavior has been studied, there is limited research on how these behaviors translate into real-world driving scenarios. This study addresses this need by leveraging Naturalistic Driving Data (NDD) to analyze driving performance measures - specifically, speed limit adherence on interstates and deceleration at stop intersections, both of which may be influenced by age-related declines. Using NDD, we developed Cumulative Distribution Functions (CDFs) to establish benchmarks for key driving behaviors among senior and young drivers. Our analysis, which included anomaly detection, benchmark comparisons, and accuracy evaluations, revealed significant differences in driving patterns primarily related to speed limit adherence at 75mph. While our approach shows promising potential for enhancing Advanced Driver Assistance Systems (ADAS) by providing tailored interventions based on age-specific adherence to speed limit driving patterns, we recognize the need for additional data to refine and validate metrics for other driving behaviors. By establishing precise benchmarks for various driving performance metrics, ADAS can effectively identify anomalies, such as abrupt deceleration, which may indicate impaired driving or other safety concerns. This study lays a strong foundation for future research aimed at improving safety interventions through detailed driving behavior analysis.