Computationally-Efficient Sparsity-Aware Occupancy Grid Mapping for Automotive Driving

Abstract

Occupancy maps are used in automotive driving applications to understand the scene around the vehicle using data from sensors like LiDAR and/or radar on vehicles. In state-of-the-art work, pattern-coupled sparse Bayesian learning (PCSBL) was used to estimate the occupancy map by leveraging spatial dependencies across grids in the map for both single modalities and the fusion of multiple modalities. The PCSBL method, however, has high computational complexity, making real-time implementation challenging for large-scale 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. 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 computational overhead. Experiments on the nuScenes and RADIATE public datasets show that the presented methods reduce computational costs compared to the benchmark PCSBL and fusionbased PCSBL methods while preserving detection accuracy.