Print Email Facebook Twitter Occupancy Grid Mapping for Automotive Driving Exploiting Clustered Sparsity Title Occupancy Grid Mapping for Automotive Driving Exploiting Clustered Sparsity Author Önen, Çağan (Student TU Delft; NXP Semiconductors) Pandharipande, Ashish (NXP Semiconductors) Joseph, G. (TU Delft Signal Processing Systems) Myers, N.J. (TU Delft Team Nitin Myers) Date 2024 Abstract Occupancy grid maps provide information about obstacles and available free space in the environment and are crucial in automotive driving applications. An occupancy map is constructed using point cloud data from sensor modalities such as light detection and ranging (LiDAR) and radar used for automotive perception. In this article, we formulate the problem of estimating the occupancy grid map using sensor point cloud data as a sparse binary occupancy value reconstruction problem. Our proposed occupancy grid estimation method, based on pattern-coupled sparse Bayesian learning (PC-SBL), leverages the sparsity and spatial dependencies inherent in occupancy maps typically encountered in automotive scenarios. The proposed method shows enhanced detection capabilities compared to two benchmark methods based on performance evaluation with scenes from the nuScenes and RADIal public datasets. Subject Sensor point cloudsLiDARRadarnuScenes datasetRADIal datasetoccupancy gridspattern-coupled priorspatial correlation To reference this document use: http://resolver.tudelft.nl/uuid:cdc1dfc4-04ad-424c-ab04-8da914ad27c0 DOI https://doi.org/10.1109/JSEN.2023.3342463 Embargo date 2024-08-19 ISSN 1558-1748 Source IEEE Sensors Journal, 24 (7), 9240-9250 Bibliographical note Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public. Part of collection Institutional Repository Document type journal article Rights © 2024 Çağan Önen, Ashish Pandharipande, G. Joseph, N.J. Myers Files file embargo until 2024-08-19