Multi-agent cooperative perception is an increasingly popular topic in the field of autonomous driving, where roadside LiDARs play an essential role. However, how to optimize the placement of roadside LiDARs is a crucial but often overlooked problem. This paper proposes an approach to optimize the placement of roadside LiDARs by selecting optimized positions within the scene for better perception performance. To efficiently obtain the best combination of locations, a greedy algorithm based on perceptual gain is proposed, which selects the location that can maximize the perceptual gain sequentially. We define perceptual gain as the increased perceptual capability when a new LiDAR is placed. To obtain the perception capability, we propose a perception predictor that learns to evaluate LiDAR placement using only a single point cloud frame. A dataset named Roadside-Opt is created using the CARLA simulator to facilitate research on the roadside LiDAR placement problem.

翻译：多智能体协同感知是自动驾驶领域日益热门的研究方向，其中路侧激光雷达发挥着关键作用。然而，如何优化路侧激光雷达的布设位置是一个关键但常被忽视的问题。本文提出了一种通过选取场景内优化位置来提升路侧激光雷达感知性能的布设优化方法。为高效获取最佳位置组合，我们提出了一种基于感知增益的贪心算法，该算法依次选取能最大化感知增益的位置。我们将感知增益定义为新增激光雷达所能提升的感知能力。为量化感知能力，我们提出了一个感知预测器——该模型仅需单帧点云数据即可评估激光雷达布设效果。借助CARLA仿真器构建了Roadside-Opt数据集，以促进路侧激光雷达布设问题的相关研究。