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An Empirical Analysis of Cooperative Perception for Occlusion Risk Mitigation

Aihong Wang, Tenghui Xie, Fuxi Wen, Jun Li

TL;DR

A novel and universal risk assessment metric, the Risk of Tracking Loss (RTL), which aggregates instantaneous risk intensity throughout occluded periods provides a holistic risk profile that encompasses both high-intensity, short-term threats and prolonged exposure.

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 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.

An Empirical Analysis of Cooperative Perception for Occlusion Risk Mitigation

TL;DR

A novel and universal risk assessment metric, the Risk of Tracking Loss (RTL), which aggregates instantaneous risk intensity throughout occluded periods provides a holistic risk profile that encompasses both high-intensity, short-term threats and prolonged exposure.

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 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.
Paper Structure (14 sections, 11 equations, 9 figures, 4 tables)

This paper contains 14 sections, 11 equations, 9 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Related work
  3. Occlusion-Induced Risk Quantification
  4. Cooperative Perception Safety Benefits
  5. Method
  6. Methodology Overview
  7. Metric Definition
  8. Risk Weight Parameter Design
  9. Experiments
  10. Experimental Setup
  11. Risk Analysis
  12. Impact of V2X Penetration on Risk Reduction
  13. Impact of Communication Strategies in Mixed Traffic
  14. Conclusions

Figures (9)

  • Figure 1: Illustrative examples of critical sensory blind spots for a vehicle, with the resulting occluded regions circled in red. (a) A static occlusion caused by the A-pillar, which obscures a child about to retrieve a ball. (b) A dynamic occlusion caused by a large truck, which conceals a pedestrian running across the road from its front.
  • Figure 2: The RTL computational pipeline illustrating the hierarchical flow from trajectory inputs to system-level safety evaluation.
  • Figure 3: Demonstration of two risk profiles captured by the proposed metric.
  • Figure 4: Decision logic for the coefficient $k_{\rm dynamic}$ in dynamic scenarios.
  • Figure 5: Visualization of spatial risk aggregation heatmaps for scenarios from the SIND and Waymo datasets. The heatmaps are generated by accumulating RTL values of high-risk events at their peak-risk locations. Gray areas denote roads, and the color gradient indicates risk intensity, with warmer colors indicating higher accumulated RTL. The first row (a-d) displays intersection scenarios from the SIND dataset, comparing Veh-Veh and Veh-VRU at the Tianjin and Changchun intersections. The second row (e-h) presents various Veh-Veh interaction scenarios from the Waymo dataset, including a T-junction, a multi-fork road, a merging lane, and an ordinary road.
  • ...and 4 more figures