A Data-Driven Analysis of Vulnerable Road User Safety in Interaction with Connected Automated Vehicles

TL;DR

This paper introduces a new risk metric to evaluate VRU safety in connected automated vehicle interactions, showing that high V2X communication penetration can significantly reduce risk but also highlighting limitations in perception-based safety assessments.

Contribution

The paper proposes the Risk Factor (RF), a novel metric for assessing VRU safety, and evaluates its effectiveness in quantifying risk mitigation in connected vehicle scenarios.

Findings

High V2X penetration reduces mean RF by up to 44%.

Median risk decreases significantly, indicating overall safety improvement.

Risk distribution analysis reveals limitations of perception-based safety metrics.

Abstract

According to the World Health Organization, the involvement of Vulnerable Road Users (VRUs) in traffic accidents remains a significant concern, with VRUs accounting for over half of traffic fatalities. The increase of automation and connectivity levels of vehicles has still an uncertain impact on VRU safety. By deploying the Collective Perception Service (CPS), vehicles can include information about VRUs in Vehicle-to-Everything (V2X) messages, thus raising the general perception of the environment. Although an increased awareness is considered positive, one could argue that the awareness ratio, the metric used to measure perception, is only implicitly connected to the VRUs' safety. This paper introduces a tailored metric, the Risk Factor (RF), to measure the risk level for the interactions between Connected Automated Vehicles (CAVs) and VRUs. By evaluating the RF, we assess the impact of V2X communication on VRU risk mitigation. Our results show that high V2X penetration rates can reduce mean risk, quantified by our proposed metric, by up to 44%. Although the median risk value shows a significant decrease, suggesting a reduction in overall risk, the distribution of risk values reveals that CPS's mitigation effectiveness is overestimated, which is indicated by the divergence between RF and awareness ratio. Additionally, by analyzing a real-world traffic dataset, we pinpoint high-risk locations within a scenario, identifying areas near intersections and behind parked cars as especially dangerous. Our methodology can be ported and applied to other scenarios in order to identify high-risk areas. We value the proposed RF as an insightful metric for quantifying VRU safety in a highly automated and connected environment.