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  4. Sensing and Machine Learning for Automotive Perception: A Review
 
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2023
Journal Article
Title

Sensing and Machine Learning for Automotive Perception: A Review

Abstract
Automotive perception involves understanding the external driving environment as well as 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 paper provides an overview of different sensor modalities like cameras, radars, and LiDARs used commonly for perception, along with the associated data processing techniques. Critical aspects in perception are considered, like 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.
Author(s)
Pandharipande, Ashish
NXP Semiconductors
Cheng, Chih-Hong  
Fraunhofer-Institut für Kognitive Systeme IKS  
Dauwels, Justin
TU Delft  
Gurbuz, Sevgi Z.
The University of Alabama, Department of Electrical and Computer Engineering
Ibanez-Guzman, Javier
Renault  
Li, Guofa
Chongqing University, College of Mechanical and Vehicle Engineering
Piazzoni, Andrea
Nanyang Technological University  
Wang, Pu
Mitsubishi Electric Research Laboratories
Santra, Avik
Infineon Technologies, München  
Journal
IEEE Sensors Journal  
Project(s)
IKS-Ausbauprojekt  
Funder
Bayerisches Staatsministerium für Wirtschaft, Landesentwicklung und Energie  
Open Access
DOI
10.1109/JSEN.2023.3262134
Language
English
Fraunhofer-Institut für Kognitive Systeme IKS  
Fraunhofer Group
Fraunhofer-Verbund IuK-Technologie
Keyword(s)
  • automotive perception

  • radar

  • camera

  • light detection and ranging

  • LiDAR

  • sensor data processing

  • advanced driver assistance system

  • autonomous driving

  • safety

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