发表机构
Western University; University of Sherbrooke; Lakehead University（韦仕敦大学; 舍布鲁克大学; 湖首大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文审计三个V2X基准，发现评估选择显著影响协同感知收益测量，模拟中收益有限，真实数据中优势随分割变化，协同价值7至15个准确率点。
AI 中文摘要
协同感知，即联网车辆与路侧基础设施共享传感器信息，是自动驾驶的潜在推动因素，而基准准确率是考虑路侧部署时引用的证据。本文对证据基础进行了审计，涵盖一个模拟和两个真实世界的车联网（V2X）基准。在模拟中，两个广泛研究的鲁棒性轴几乎没有留下可恢复的余量：基于基础设施的位姿修正，在每个误差水平下仅能带来约一个准确率点的提升，而破坏合作伙伴则造成0.7个点的损失。在真实数据上，测量选择决定了结论。在合作伙伴稀缺的帧中，基础设施的明显十七倍优势，在分割范围扩大后降至四倍以下，而临时定义的类别所测得的收益远小于官方协议报告的结果。协同感知的价值为7至15个准确率点，但一个分割中27%的帧没有合作伙伴，而另一个分割中几乎没有这样的帧，因此每个基于条件的声明都必须指明其分割。
英文摘要
Cooperative perception, in which connected vehicles and roadside infrastructure share sensor information, is a candidate enabler of automated mobility, and benchmark accuracy is the evidence cited when roadside deployment is considered. This paper audits that evidence base across one simulated and two real-world vehicle-to-everything (V2X) benchmarks. In simulation, two widely studied robustness axes leave almost no recoverable headroom: an infrastructure-anchored pose correction returns about one accuracy point at every error level, and corrupting a partner costs 0.7 points. On real data, measurement choices govern the conclusion. An apparent seventeen-fold advantage of infrastructure in partner-poor frames falls below four-fold once the split is broadened, and an ad-hoc class definition measures a far smaller benefit than the official protocol reports. Cooperation is worth 7 to 15 accuracy points, yet 27% of frames on one split offer no partner and almost none do on another, so every regime-conditioned claim must name its split.
CommentsInternational Conference of Hong Kong Society for Transportation Studies (HKSTS) 2026
