让汽车通过协作感知提前预判危险，提升避障能力。
CooperDrive: Enhancing Driving Decisions Through Cooperative Perception
- 各车保留原有感知系统，仅共享物体级信息实现轻量融合
- 在非视距场景下反应时间提前，最小碰撞时间提升37%
- 适合高风险交叉路口的自动驾驶系统部署
配备强大车载感知、定位和规划系统的自动驾驶车辆，在遮挡和非视距（NLOS）场景中仍面临反应延迟带来的碰撞风险。我们提出CooperDrive，一种协作感知框架，可增强态势感知并实现更早、更安全的驾驶决策。该框架具备两大优势：(i) 每辆车保留其原有感知、定位与规划系统；(ii) 采用轻量级的物体级信息共享与融合策略，连接感知与规划模块。具体而言，CooperDrive复用检测器的鸟瞰图（BEV）特征，无需额外重型编码器即可精确估计车辆位姿，从而重建BEV表示，并以低延迟向规划器提供信息。在规划层面，借助扩展的物体集合，CooperDrive能更早识别潜在冲突并主动调整速度与轨迹，将被动应对转化为前瞻性的安全决策。真实世界闭环测试在遮挡严重的非视距交叉口进行，结果显示CooperDrive显著提升了反应提前时间、最小时间到碰撞（TTC）和制动裕度，仅需90 kbps带宽，平均端到端延迟保持在89 ms。
原文摘要 · Abstract (English)
Autonomous vehicles equipped with robust onboard perception, localization, and planning still face limitations in occlusion and non-line-of-sight (NLOS) scenarios, where delayed reactions can increase collision risk. We propose CooperDrive, a cooperative perception framework that augments situational awareness and enables earlier, safer driving decisions. CooperDrive offers two key advantages: (i) each vehicle retains its native perception, localization, and planning stack, and (ii) a lightweight object-level sharing and fusion strategy bridges perception and planning. Specifically, CooperDrive reuses detector Bird's-Eye View (BEV) features to estimate accurate vehicle poses without additional heavy encoders, thereby reconstructing BEV representations and feeding the planner with low latency. On the planning side, CooperDrive leverages the expanded object set to anticipate potential conflicts earlier and adjust speed and trajectory proactively, thereby transforming reactive behaviors into predictive and safer driving decisions. Real-world closed-loop tests at occlusion-heavy NLOS intersections demonstrate that CooperDrive increases reaction lead time, minimum time-to-collision (TTC), and stopping margin, while requiring only 90 kbps bandwidth and maintaining an average end-to-end latency of 89 ms.
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