Cooper: Cooperative Perception for Connected Autonomous Vehicles based on 3D Point Clouds
This addresses the challenge of limited bandwidth for raw sensor data exchange in vehicular networks, enabling safer autonomous driving through enhanced perception.
The paper tackles the problem of inaccurate detection in autonomous vehicles by proposing a cooperative perception system that fuses LiDAR point clouds from multiple connected vehicles, demonstrating improved detection accuracy and extended sensing area on KITTI and a custom dataset.
Autonomous vehicles may make wrong decisions due to inaccurate detection and recognition. Therefore, an intelligent vehicle can combine its own data with that of other vehicles to enhance perceptive ability, and thus improve detection accuracy and driving safety. However, multi-vehicle cooperative perception requires the integration of real world scenes and the traffic of raw sensor data exchange far exceeds the bandwidth of existing vehicular networks. To the best our knowledge, we are the first to conduct a study on raw-data level cooperative perception for enhancing the detection ability of self-driving systems. In this work, relying on LiDAR 3D point clouds, we fuse the sensor data collected from different positions and angles of connected vehicles. A point cloud based 3D object detection method is proposed to work on a diversity of aligned point clouds. Experimental results on KITTI and our collected dataset show that the proposed system outperforms perception by extending sensing area, improving detection accuracy and promoting augmented results. Most importantly, we demonstrate it is possible to transmit point clouds data for cooperative perception via existing vehicular network technologies.