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We present an end-to-end method for object detection and trajectoryprediction utilizing multi-view representations of LiDAR returns and cameraimages. In this work, we recognize the strengths and weaknesses of differentview representations, and we propose an efficient and generic fusing methodthat aggregates benefits from all views. Our model builds on a state-of-the-artBird's-Eye View (BEV) network that fuses voxelized features from a sequence ofhistorical LiDAR data as well as rasterized high-definition map to performdetection and prediction tasks. We extend this model with additional LiDARRange-View (RV) features that use the raw LiDAR information in its native,non-quantized representation. The RV feature map is projected into BEV andfused with the BEV features computed from LiDAR and high-definition map. Thefused features are then further processed to output the final detections andtrajectories, within a single end-to-end trainable network. In addition, the RVfusion of LiDAR and camera is performed in a straightforward andcomputationally efficient manner using this framework. The proposed multi-viewfusion approach improves the state-of-the-art on proprietary large-scalereal-world data collected by a fleet of self-driving vehicles, as well as onthe public nuScenes data set with minimal increases on the computational cost.

Multi-View Fusion of Sensor Data for Improved Perception and Prediction in Autonomous Driving