Print Email Facebook Twitter Sensing and Machine Learning for Automotive Perception Title Sensing and Machine Learning for Automotive Perception: A Review Author Pandharipande, Ashish (NXP Semiconductors) Cheng, Chih Hong (Fraunhofer Iks) Dauwels, J.H.G. (TU Delft Signal Processing Systems) Gurbuz, Sevgi Z. (University of South Alabama) Ibanez-Guzman, Javier (Group Renault) Li, Guofa (Chongqing University) Piazzoni, Andrea (Nanyang Technological University) Wang, Pu (Mitsubishi Electric Research Laboratories) Santra, Avik (Infineon Technologies, North America) Date 2023 Abstract Automotive perception involves understanding the external driving environment and the internal state of the vehicle cabin and occupants using sensor data. It is critical to achieving high levels of safety and autonomy in driving. This article provides an overview of different sensor modalities, such as cameras, radars, and light detection and ranging (LiDAR) used commonly for perception, along with the associated data processing techniques. Critical aspects of perception are considered, such as architectures for processing data from single or multiple sensor modalities, sensor data processing algorithms and the role of machine learning techniques, methodologies for validating the performance of perception systems, and safety. The technical challenges for each aspect are analyzed, emphasizing machine learning approaches, given their potential impact on improving perception. Finally, future research opportunities in automotive perception for their wider deployment are outlined. Subject Advanced driver assistance system (ADAS)automotive perceptionautonomous drivingcameraslight detection and ranging (LiDAR)radarssafetysensor data processing To reference this document use: http://resolver.tudelft.nl/uuid:835f1e55-6dcb-4e0c-8223-aefe1d26d368 DOI https://doi.org/10.1109/JSEN.2023.3262134 Embargo date 2023-09-25 ISSN 1530-437X Source IEEE Sensors Journal, 23 (11), 11097-11115 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 © 2023 Ashish Pandharipande, Chih Hong Cheng, J.H.G. Dauwels, Sevgi Z. Gurbuz, Javier Ibanez-Guzman, Guofa Li, Andrea Piazzoni, Pu Wang, Avik Santra Files PDF Sensing_and_Machine_Learn ... Review.pdf 19.67 MB Close viewer /islandora/object/uuid:835f1e55-6dcb-4e0c-8223-aefe1d26d368/datastream/OBJ/view