An Empirical Analysis of Cooperative Perception for Occlusion Risk Mitigation

• URL: http://arxiv.org/abs/2602.23051v1
• Date: Thu, 26 Feb 2026 14:38:38 GMT
• Title: An Empirical Analysis of Cooperative Perception for Occlusion Risk Mitigation
• Abstract summary: Occlusions present a significant challenge for connected and automated vehicles.<n>We propose a novel and universal risk assessment metric, the Risk of Tracking Loss (RTL), which aggregates instantaneous risk intensity throughout occluded periods.
• Score: 5.46089529597726
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Fusing Driver Perceived and Physical Risk for Safety Critical Scenario Screening in Autonomous Driving [20.663269212781426]
  We propose a driver risk fusion based hazardous scenario screening method for autonomous driving.<n>During training, the method combines an improved Driver Risk Field with a dynamic cost model to generate high quality risk supervision signals.<n>During inference it directly predicts scenario level risk scores through fast forward passes, avoiding per frame risk computation.
  arXiv  Detail & Related papers  (2026-03-07T13:24:23Z)
• SUPER -- A Framework for Sensitivity-based Uncertainty-aware Performance and Risk Assessment in Visual Inertial Odometry [1.376408511310322]
  SUPER (Sensitivity-based Uncertainty-aware PErformance and Risk assessment) is a generic and explainable framework that propagates uncertainties via sensitivities for real-time risk assessment in VIO.<n>Our framework estimates risks on the basis of the residual magnitudes, geometric conditioning, and short horizon temporal trends without requiring ground truth knowledge.
  arXiv  Detail & Related papers  (2025-12-16T08:35:47Z)
• RADAR: A Risk-Aware Dynamic Multi-Agent Framework for LLM Safety Evaluation via Role-Specialized Collaboration [81.38705556267917]
  Existing safety evaluation methods for large language models (LLMs) suffer from inherent limitations.<n>We introduce a theoretical framework that reconstructs the underlying risk concept space.<n>We propose RADAR, a multi-agent collaborative evaluation framework.
  arXiv  Detail & Related papers  (2025-09-28T09:35:32Z)
• From Mean to Extreme: Formal Differential Privacy Bounds on the Success of Real-World Data Reconstruction Attacks [54.25638567385662]
  Differential Privacy in machine learning is often interpreted as guarantees against membership inference.<n> translating DP budgets into quantitative protection against the more damaging threat of data reconstruction remains a challenging open problem.<n>This paper bridges the critical gap by deriving the first formal privacy bounds tailored to the mechanics of demonstrated "from-scratch" attacks.
  arXiv  Detail & Related papers  (2024-02-20T09:52:30Z)
• Domain Generalization without Excess Empirical Risk [83.26052467843725]
  A common approach is designing a data-driven surrogate penalty to capture generalization and minimize the empirical risk jointly with the penalty. We argue that a significant failure mode of this recipe is an excess risk due to an erroneous penalty or hardness in joint optimization. We present an approach that eliminates this problem. Instead of jointly minimizing empirical risk with the penalty, we minimize the penalty under the constraint of optimality of the empirical risk.
  arXiv  Detail & Related papers  (2023-08-30T08:46:46Z)
• Extreme Risk Mitigation in Reinforcement Learning using Extreme Value Theory [10.288413564829579]
  A critical aspect of risk awareness involves modeling highly rare risk events (rewards) that could potentially lead to catastrophic outcomes. While risk-aware RL techniques do exist, their level of risk aversion heavily relies on the precision of the state-action value function estimation. Our work proposes to enhance the resilience of RL agents when faced with very rare and risky events by focusing on refining the predictions of the extreme values predicted by the state-action value function distribution.
  arXiv  Detail & Related papers  (2023-08-24T18:23:59Z)
• A Counterfactual Safety Margin Perspective on the Scoring of Autonomous Vehicles' Riskiness [52.27309191283943]
  This paper presents a data-driven framework for assessing the risk of different AVs' behaviors. We propose the notion of counterfactual safety margin, which represents the minimum deviation from nominal behavior that could cause a collision.
  arXiv  Detail & Related papers  (2023-08-02T09:48:08Z)
• Efficient Risk-Averse Reinforcement Learning [79.61412643761034]
  In risk-averse reinforcement learning (RL), the goal is to optimize some risk measure of the returns. We prove that under certain conditions this inevitably leads to a local-optimum barrier, and propose a soft risk mechanism to bypass it. We demonstrate improved risk aversion in maze navigation, autonomous driving, and resource allocation benchmarks.
  arXiv  Detail & Related papers  (2022-05-10T19:40:52Z)
• Two steps to risk sensitivity [4.974890682815778]
  conditional value-at-risk (CVaR) is a risk measure for modeling human and animal planning. We adopt a conventional distributional approach to CVaR in a sequential setting and reanalyze the choices of human decision-makers. We then consider a further critical property of risk sensitivity, namely time consistency, showing alternatives to this form of CVaR.
  arXiv  Detail & Related papers  (2021-11-12T16:27:47Z)
• Automatic Risk Adaptation in Distributional Reinforcement Learning [26.113528145137497]
  The use of Reinforcement Learning (RL) agents in practical applications requires the consideration of suboptimal outcomes. This is especially important in safety-critical environments, where errors can lead to high costs or damage. We show reduced failure rates by up to a factor of 7 and improved generalization performance by up to 14% compared to both risk-aware and risk-agnostic agents.
  arXiv  Detail & Related papers  (2021-06-11T11:31:04Z)
• Learning Bounds for Risk-sensitive Learning [86.50262971918276]
  In risk-sensitive learning, one aims to find a hypothesis that minimizes a risk-averse (or risk-seeking) measure of loss. We study the generalization properties of risk-sensitive learning schemes whose optimand is described via optimized certainty equivalents.
  arXiv  Detail & Related papers  (2020-06-15T05:25:02Z)
• Deep Survival Machines: Fully Parametric Survival Regression and Representation Learning for Censored Data with Competing Risks [14.928328404160299]
  We describe a new approach to estimating relative risks in time-to-event prediction problems with censored data. Our approach does not require making strong assumptions of constant proportional hazard of the underlying survival distribution. This is the first work involving fully parametric estimation of survival times with competing risks in the presence of censoring.
  arXiv  Detail & Related papers  (2020-03-02T20:21:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.