Collaborative 3D Object Detection for Autonomous Vehicles via Learnable Communications

3D object detection from LiDAR point cloud is a challenging task in autonomous driving systems. Collaborative perception can incorporate information from spatially diverse sensors and provide significant benefits for accurate 3D object detection from point clouds. In this work, we consider that the autonomous vehicle uses local point cloud data and combines information from neighboring infrastructures through wireless links for cooperative 3D object detection. However, information sharing among vehicles and infrastructures in predefined communication schemes may result in communication congestion and/or bring limited performance improvement. To this end, we propose a novel collaborative 3D object detection framework using an encoder-decoder network architecture and an attention-based learnable communications scheme. It consists of three components: a feature encoder network that maps point clouds into feature maps; an attention-based communication module that propagates compact and fine-grained query feature maps from the vehicle to support infrastructures, and optimizes attention weights between query and key to refine support feature maps; a region proposal network that fuses local feature maps and weighted support feature maps for 3D object detection. We evaluate the performance of the proposed framework on CARLA-3D, a new dataset that we synthesized using CARLA for 3D cooperative object detection. Experimental results and bandwidth consumption analysis show that the proposed collaborative 3D object detection framework achieves a better detection performance and communication bandwidth trade-off than five baseline 3D object detection models under different detection difficulties.