Occlusion is a major challenge for LiDAR-based object detection methods. This challenge becomes safety-critical in urban traffic where the ego vehicle must have reliable object detection to avoid collision while its field of view is severely reduced due to the obstruction posed by a large number of road users. Collaborative perception via Vehicle-to-Everything (V2X) communication, which leverages the diverse perspective thanks to the presence at multiple locations of connected agents to form a complete scene representation, is an appealing solution. State-of-the-art V2X methods resolve the performance-bandwidth tradeoff using a mid-collaboration approach where the Bird-Eye View images of point clouds are exchanged so that the bandwidth consumption is lower than communicating point clouds as in early collaboration, and the detection performance is higher than late collaboration, which fuses agents' output, thanks to a deeper interaction among connected agents. While achieving strong performance, the real-world deployment of most mid-collaboration approaches is hindered by their overly complicated architectures, involving learnable collaboration graphs and autoencoder-based compressor/ decompressor, and unrealistic assumptions about inter-agent synchronization. In this work, we devise a simple yet effective collaboration method that achieves a better bandwidth-performance tradeoff than prior state-of-the-art methods while minimizing changes made to the single-vehicle detection models and relaxing unrealistic assumptions on inter-agent synchronization. Experiments on the V2X-Sim dataset show that our collaboration method achieves 98\% of the performance of an early-collaboration method, while only consuming the equivalent bandwidth of a late-collaboration method.

翻译：遮挡是基于激光雷达的目标检测方法面临的主要挑战。在城市交通场景中，自车的视野因大量道路使用者的遮挡而严重受限，此时必须依靠可靠的目标检测以避免碰撞，这使得遮挡问题成为安全关键。通过车联万物（V2X）通信实现协同感知是一种颇具吸引力的解决方案。该方法利用多个位置的互联智能体提供的多样化视角，形成完整的场景表征。现有最先进的V2X方法采用中间协同方式解决性能与带宽的权衡问题：通过交换点云的鸟瞰图（BEV）图像，在带宽消耗上低于早期协同中的原始点云通信，同时通过智能体间更深层次的交互，其检测性能优于融合各智能体输出的后期协同。尽管性能优异，但大多数中间协同方法在真实场景部署中面临障碍：其架构过于复杂（包含可学习协同图和基于自编码器的压缩/解压器），且对智能体间同步存在不切实际的假设。本研究提出一种简洁高效的协同方法，在保持单车辆检测模型最小改动的同时，放松了智能体间同步的不合理假设，实现了比现有最优方法更优的带宽-性能权衡。在V2X-Sim数据集上的实验表明，所提协同方法在仅消耗相当于后期协同方法带宽的情况下，达到了早期协同方法98%的性能。