Occupancy grid mapping is a common approach to support automotive driving perception capabilities. We present an occupancy grid estimation algorithm using sensor point-cloud measurements aided by side information from other sensing modalities like cameras. This prior side information is in the form of an erroneous occupancy map estimate, referred to as prior support information. Specifically, we extract a prior map using you only look once (YOLO) object detection on camera images. A sparse Bayesian learning-based mapping algorithm is designed with a modified hierarchical model to incorporate this prior. Experiments done on public real-world driving datasets, nuScenes and RADIATE, demonstrate that our approach achieves better target detection and scatter noise reduction than the state-of-the-art methods. Furthermore, our method seamlessly works on the two datasets although we train YOLO only using camera images from nuScenes.

Occupancy maps are used in automotive driving applications to understand the scene around the vehicle using sensor data like LiDAR measurements. State-of-the-art work relies on pattern-coupled sparse Bayesian learning (PCSBL) to estimate the occupancy map from LiDAR point cloud data by leveraging spatial dependencies across grids in the map. However, PCSBL has high computational complexity, posing challenges for real-time implementation on large-sized grid maps. In this work, we propose two methods to improve the computational efficiency of PCSBL for occupancy grid mapping by exploiting the narrow angular interactions of sensor measurements with the map. The first method partitions the measurements into spatially disjoint submaps that can be processed in parallel. The second method exploits the angular structure to impose a block structure on the measurement matrix, allowing more efficient matrix computations to accelerate the algorithm. Experiments on the nuScenes public dataset show that the presented methods reduce computational runtime compared to the benchmark PCSBL method while preserving detection accuracy.

An occupancy grid map (OGM) is used in automotive driving applications to model the vehicle surroundings using data from sensors on vehicles like light detection and ranging (LiDAR), radar, or their fusion. In stateof- 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 was 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 with the benchmark PCSBL and fusion-based PCSBL methods while preserving detection accuracy.

We tackle the problem of estimating a binary occupancy grid map by fusing point cloud data from LiDAR and radar sensors for automotive driving perception. To this end, we introduce two sparsity measurement models for fusion, formulating occupancy mapping as a sparse binary vector reconstruction problem. The first model jointly estimates a common map from all measurements, while the second assumes a shared map and an innovation component for each modality's measurements. We use the pattern-coupled sparse Bayesian learning algorithm to recover maps, leveraging the inherent sparsity and spatial dependencies in automotive occupancy maps. Numerical experiments on the RADIATE public dataset show that our fusion-based approach improves mapping accuracy compared to single-modality and high-level fusion mapping algorithms.