CsoOcc: An Occupancy Network for Train Forward Perception Based on Cascade Sparsity Octree

The ability to perceive and reconstruct the surrounding environment in three dimensions is crucial for autonomous driving trains. With the advent of occupancy networks, it has become essential for autonomous train driving to use these networks for 3-D reconstruction in railway transportation. This study proposes the first railway occupancy network (CsoOcc) that considers the characteristics of railway transportation. Specifically, a cascaded optimization framework was devised to cascade the occupancy results of adjacent layers, thereby facilitating the incremental refinement of the occupancy features. A binocular attention mechanism was designed to establish correlations between binocular cameras, thereby enabling 3-D reconstruction based on binocular correlations. A sparse octree optimization method was proposed to reduce the computational load on the network. Experiments based on the TWL-Occ dataset verified the CsoOcc performance, with a mean intersection over union (mIoU) of 0.632 and an intersection over union (IoU) of 0.765, outperforming the benchmarks. The field experiments were conducted along Hong Kong Metro Tsuen Wan Line. All the results demonstrate that the proposed occupancy network provides accurate forward 3-D environmental information, thereby enhancing the safety of autonomous train navigation.