Grid-Centric Traffic Scenario Perception for Autonomous Driving: A Comprehensive Review

Grid-centric perception is a crucial field for mobile robot perception and navigation. Nonetheless, grid-centric perception is less prevalent than object-centric perception for autonomous driving as autonomous vehicles need to accurately perceive highly dynamic, large-scale outdoor traffic scenarios and the complexity and computational costs of grid-centric perception are high. The rapid development of deep learning techniques and hardware gives fresh insights into the evolution of grid-centric perception and enables the deployment of many real-time algorithms. Current industrial and academic research demonstrates the great advantages of grid-centric perception, such as comprehensive fine-grained environmental representation, greater robustness to occlusion, more efficient sensor fusion, and safer planning policies. Given the lack of current surveys for this rapidly expanding field, we present a hierarchically-structured review of grid-centric perception for autonomous vehicles. We organize previous and current knowledge of occupancy grid techniques and provide a systematic in-depth analysis of algorithms in terms of three aspects: feature representation, data utility, and applications in autonomous driving systems. Lastly, we present a summary of the current research trend and provide some probable future outlooks.

FusionMotion: Multi-Sensor Asynchronous Fusion for Continuous Occupancy Prediction via Neural-ODE

Occupancy maps are widely recognized as an efficient method for facilitating robot motion planning in static environments. However, for intelligent vehicles, occupancy of both the present and future moments is required to ensure safe driving. In the automotive industry, the accurate and continuous prediction of future occupancy maps in traffic scenarios remains a formidable challenge. This paper investigates multi-sensor spatio-temporal fusion strategies for continuous occupancy prediction in a systematic manner. This paper presents FusionMotion, a novel bird's eye view (BEV) occupancy predictor which is capable of achieving the fusion of asynchronous multi-sensor data and predicting the future occupancy map with variable time intervals and temporal horizons. Remarkably, FusionMotion features the adoption of neural ordinary differential equations on recurrent neural networks for occupancy prediction. FusionMotion learns derivatives of BEV features over temporal horizons, updates the implicit sensor's BEV feature measurements and propagates future states for each ODE step. Extensive experiments on large-scale nuScenes and Lyft L5 datasets demonstrate that FusionMotion significantly outperforms previous methods. In addition, it outperforms the BEVFusion-style fusion strategy on the Lyft L5 dataset while reducing synchronization requirements. Codes and models will be made available.