Integrating Naturalistic Insights in Objective Multi-Vehicle Safety Framework

TL;DR

This paper introduces a comprehensive framework for assessing traffic safety in multi-vehicle scenarios, combining objective safety metrics with subjective perception adjustments, validated on naturalistic highway driving data.

Contribution

It presents a novel framework that integrates diverse safety metrics and subjective perception modeling for more accurate traffic safety assessment in complex environments.

Findings

Enhanced safety evaluation when combining multiple objective metrics

Significant performance improvement with all surrounding vehicles considered

Effective application to naturalistic highway driving data

Abstract

As autonomous vehicle technology advances, the precise assessment of safety in complex traffic scenarios becomes crucial, especially in mixed-vehicle environments where human perception of safety must be taken into account. This paper presents a framework designed for assessing traffic safety in multi-vehicle situations, facilitating the simultaneous utilization of diverse objective safety metrics. Additionally, it allows the integration of subjective perception of safety by adjusting model parameters. The framework was applied to evaluate various model configurations in car-following scenarios on a highway, utilizing naturalistic driving datasets. The evaluation of the model showed an outstanding performance, particularly when integrating multiple objective safety measures. Furthermore, the performance was significantly enhanced when considering all surrounding vehicles.