Optimizing the Placement of Roadside LiDARs for Autonomous Driving
Wentao Jiang Hao Xiang Xinyu Cai Runsheng Xu Jiaqi Ma Yikang Li Gim Hee Lee Si Liu
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.