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，由于大量道路使用者造成的障碍，自车视野严重受限，此时确保可靠的目标检测以避免碰撞成为安全关键问题。通过车联万物通信实现的协同感知——利用多个位置分布的联网智能体的多样化视角构建完整场景表征——是一种颇具吸引力的解决方案。现有先进的V2X方法采用中间协同方式解决性能-带宽权衡问题，其通过交换点云鸟瞰图实现：相较于早期协同中直接传输点云，该方法降低了带宽消耗；而由于联网智能体间更深层次的交互，其检测性能又优于融合各智能体输出的晚期协同。尽管取得了优异性能，但多数中间协同方法因架构过于复杂（涉及可学习协同图与基于自编码器的压缩/解压缩模块）以及对智能体间同步的不切实际假设，阻碍了实际部署。本文提出一种简洁高效的协同方法，在最小化对单车检测模型改动、放宽智能体间同步假设的同时，实现了比现有先进方法更优的带宽-性能权衡。在V2X-Sim数据集上的实验表明，本协同方法可达到早期协同方法98%的性能，而带宽消耗仅相当于晚期协同方法。