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This paper introduces the Risk of Tracking Loss (RTL) metric to quantify the cumulative risk posed by occlusions to connected and automated vehicles, addressing the limitations of instantaneous risk metrics. Through large-scale statistical analysis on real-world datasets, the authors characterize occlusion risk and validate RTL's effectiveness in evaluating V2X deployment strategies. They further propose and demonstrate the efficacy of an asymmetric communication framework that significantly reduces occlusion risk even with low V2X penetration rates, outperforming traditional symmetric models.
A novel asymmetric V2X communication strategy allows even non-connected vehicles to benefit from occlusion risk mitigation, outperforming traditional symmetric models with significantly lower penetration rates.
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 eliminates this risk, the reduction is highly nonlinear; a substantial statistical benefit requires a high penetration threshold of 75-90%. To overcome this limitation, we propose a novel asymmetric communication framework that allows even non-connected vehicles to receive warnings. Experimental results demonstrate that this paradigm achieves better risk mitigation performance. We found that our approach at 25% penetration outperforms the traditional symmetric model at 75%, and benefits saturate at only 50% penetration. This work provides a crucial risk assessment metric and a cost-effective, strategic roadmap for accelerating the safety benefits of V2X deployment.
Tsinghua AI