An Empirical Analysis of Cooperative Perception for Occlusion Risk Mitigation
Aihong Wang, Tenghui Xie, Fuxi Wen, Jun Li

TL;DR
This paper introduces a new risk metric for occlusion-related hazards in automated driving, validates it with real-world data, and proposes a communication strategy that enhances safety at lower V2X penetration levels.
Contribution
It proposes the Risk of Tracking Loss (RTL) metric, validates it with diverse datasets, and introduces an asymmetric communication framework to improve risk mitigation at lower V2X penetration.
Findings
RTL effectively captures cumulative occlusion risk.
High V2X penetration (75-90%) is needed for significant risk reduction.
Asymmetric communication improves safety at 25% penetration.
Abstract
Occlusions present a significant challenge for connected and automated vehicles, as they can obscure critical road users from perception systems. Traditional risk metrics often fail to capture the cumulative nature of these threats over time adequately. In this paper, we propose a novel and universal risk assessment metric, the Risk of Tracking Loss (RTL), which aggregates instantaneous risk intensity throughout occluded periods. This provides a holistic risk profile that encompasses both high-intensity, short-term threats and prolonged exposure. Utilizing diverse and high-fidelity real-world datasets, a large-scale statistical analysis is conducted to characterize occlusion risk and validate the effectiveness of the proposed metric. The metric is applied to evaluate different vehicle-to-everything (V2X) deployment strategies. Our study shows that full V2X penetration theoretically…
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Taxonomy
TopicsVehicular Ad Hoc Networks (VANETs) · Autonomous Vehicle Technology and Safety · Human-Automation Interaction and Safety
