通过融合多车感知数据，扩展自动驾驶车辆的视野范围。
Beyond Line of Sight: Hybrid Validation of V2X Collective Perception in Complex Scenarios
- 用贝叶斯融合算法整合多车异构传感器数据，生成共享概率占据图。
- 在环岛场景中，感知覆盖范围提升260%，占用单元召回率从0.82升至0.94。
- 结合虚拟仿真与实车测试，支持可复现、可解释的协同感知验证。
本文提出一种概率框架与混合验证方法，用于复杂交通场景下车联网协同感知（V2X-enabled Collective Perception, CP）的评估。所提出的贝叶斯融合算法通过整合多智能体的异构传感器观测数据，构建共享的概率占据网格，每个网格单元包含占据概率与不确定性信息，实现对本车视距外场景的可解释、可信态势感知。为弥合仿真与真实评估之间的差距，开发了结合CARLA虚拟环境与车在回路实验的混合测试框架。在环岛场景中的实验表明，在正常定位条件下，感知视场覆盖范围提升260%，占据单元召回率从单车的0.82提升至六车协同下的0.94。整体方法为协同感知系统的验证提供了可复现、可解释的基础，支持合作式自动驾驶车辆的安全与可认证部署。
原文摘要 · Abstract (English)
This paper introduces a probabilistic framework and hybrid validation methodology for V2X-enabled Collective Perception (CP) in complex traffic scenarios. The proposed Bayesian fusion algorithm extends the perceptual horizon of connected and autonomous vehicles by integrating heterogeneous sensor observations from multiple agents into a shared probabilistic occupancy grid. Each cell of this grid encapsulates both occupancy likelihood and uncertainty, enabling explainable and trustworthy situational awareness beyond the ego vehicle's field of view. To bridge the gap between simulation and real-world evaluation, a hybrid testing framework is developed, combining CARLA-based virtual environments with vehicle-in-the-loop experimentation. Experimental results in a roundabout scenario demonstrate a 260 percent increase in field-of-view coverage and a rise in occupied-cell recall from 0.82 (ego-only) to 0.94 (six-agent CP) under nominal localization conditions. Overall, the proposed approach provides a reproducible and interpretable foundation for validating CP systems, supporting the safe and certifiable deployment of cooperative autonomous vehicles.
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