Accelerated Pattern-Coupled Sparse Bayesian Learning for Automotive Occupancy Mapping

An occupancy grid map (OGM) is used in automotive driving applications to model the vehicle surroundings using data from sensors on vehicles like LiDAR, radar, or their fusion. In state-of-the-art work, pattern-coupled sparse Bayesian learning (PCSBL) was used to estimate the OGM by leveraging spatial dependencies in the map when either a single sensor modality was used, or when fusion of multiple sensor modalities is employed. The PCSBL method, however, has high computational complexity, making real-time implementation challenging for large-sized grid maps. To address this limitation, we propose several methods to improve the computational efficiency of PCSBL while maintaining mapping accuracy. First, we utilize a precomputed lookup table to accelerate selection matrix construction. Second, we implement adaptive resolution reduction based on sensor measurements, lowering problem dimensionality where coarse resolution suffices. Third, we develop two novel methods that exploit the narrow angular interactions between measurements and the map regions to enhance computational efficiency. The first method partitions measurements into spatially disjoint submaps that enable parallel processing. The second method exploits the angular structure to impose a block structure on the selection matrix, reducing the computational overhead of matrix operations. Experiments on the nuScenes and RADIATE public datasets show that the presented methods reduce runtime by at least an order of magnitude compared to the benchmark PCSBL and fusion-based PCSBL methods while preserving detection accuracy.