CooperDrive：通过协作感知增强驾驶决策
CooperDrive: Enhancing Driving Decisions Through Cooperative Perception
AI总结：
本文提出CooperDrive协作感知框架，通过轻量级对象级共享与融合策略提升感知与规划能力，实现在遮挡和非视线场景下的更早安全驾驶决策。
摘要：
配备强大 onboard 感知、定位和规划的自动驾驶车辆仍面临遮挡和非视线（NLOS）场景的限制，延迟反应会增加碰撞风险。我们提出CooperDrive，一种协作感知框架，通过保留每个车辆的原生感知、定位和规划堆栈，并采用轻量级对象级共享与融合策略连接感知与规划。具体而言，CooperDrive重用检测器鸟瞰图（BEV）特征以估计准确的车辆姿态，无需额外重型编码器，从而重建BEV表示并以低延迟馈入规划器。在规划侧，CooperDrive利用扩展的对象集提前预测潜在冲突并主动调整速度和轨迹，从而将反应行为转变为预测性和更安全的驾驶决策。现实世界闭环测试显示，在遮挡重的NLOS交叉口，CooperDrive增加了反应提前时间、最小时间到碰撞（TTC）和停车边距，仅需90 kbps带宽并保持平均端到端延迟为89 ms。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
