Autonomous driving paper index
CoBEVFusion Cooperative Perception with LiDAR-Camera Bird's Eye View Fusion
One-line summary
We propose the Dual Window-based Cross-Attention (DWCA) model which extracts and fuses selected camera and LiDAR data features and projects that onto a Bird's-Eye View (BEV) representation on a single ego vehicle.
Engineering notes
We demonstrate that using multimodal camera and LiDAR data, our DWCA model exceeds the performance of state-of-the-art (SOTA) models using unimodal data in vehicular object detection and segmentation tasks on a single vehicle. The CoBEVFusion model outperforms SOTA models in object detection tasks when using unimodal data or multimodal camera-LiDAR data.
Chinese explanation / 中文解读
中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。
Original abstract
Autonomous Vehicles (AVs) use multiple sensors to gather information about their surroundings. Connected Autonomous Vehicles (CAVs) share sensor data for increased safety and reliability through cooperative perception. However, most recent approaches in cooperative perception share unimodal information perceived using a single sensor such as only camera video data or only LiDAR point cloud data or perform multi-modal data fusion at the early or late stage. In this research, we explore vehicular perception utilizing intermediate fusion of mul-timodal camera video and LiDAR point cloud data. We propose the Dual Window-based Cross-Attention (DWCA) model which extracts and fuses selected camera and LiDAR data features and projects that onto a Bird's-Eye View (BEV) representation on a single ego vehicle. We demonstrate that using multimodal camera and LiDAR data, our DWCA model exceeds the performance of state-of-the-art (SOTA) models using unimodal data in vehicular object detection and segmentation tasks on a single vehicle. Next, we propose a model for Cooperative Perception, CoBEVFusion, which aggregates the fused BEV representations obtained from surrounding CAVs using a 3D Convolutional Neural Network. We validate our CoBEVFusion framework on the cooperative perception dataset, OPV2V, for two perception tasks: 3D object detection and BEV semantic segmentation. The CoBEVFusion model outperforms SOTA models in object detection tasks when using unimodal data or multimodal camera-LiDAR data.
Links and sources
Need this topic turned into a technical roadmap?
Full Self Driving can prepare a custom autonomous driving literature review, code map, dataset map, and B2B technology assessment.
Request B2B research
Comments