Optimizing the Placement of Roadside LiDARs for Autonomous Driving
This addresses a crucial but often overlooked problem in multi-agent cooperative perception for autonomous driving, though it appears incremental as it builds on existing methods with a new dataset.
This paper tackles the problem of optimizing roadside LiDAR placement for autonomous driving by proposing a greedy algorithm based on perceptual gain, resulting in improved perception performance as demonstrated on a dataset created using the CARLA simulator.
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.