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AIMS Electronics and Electrical Engineering

2023, Volume 7, Issue 4: 271-321. doi: 10.3934/electreng.2023016
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Review

Exploring the challenges and opportunities of image processing and sensor fusion in autonomous vehicles: A comprehensive review

  • 1.
    Shady Side Academy, Pittsburgh, Pennsylvania, United States of America
  • 2.
    Civil Engineering Department, University of Toronto, Toronto, Canada, Canada
  • 3.
    Public Works Department, Faculty of Engineering, Cairo University, Giza, Egypt
  • Received: 22 June 2023 Revised: 26 September 2023 Accepted: 07 October 2023 Published: 18 October 2023

  • Autonomous vehicles are at the forefront of future transportation solutions, but their success hinges on reliable perception. This review paper surveys image processing and sensor fusion techniques vital for ensuring vehicle safety and efficiency. The paper focuses on object detection, recognition, tracking, and scene comprehension via computer vision and machine learning methodologies. In addition, the paper explores challenges within the field, such as robustness in adverse weather conditions, the demand for real-time processing, and the integration of complex sensor data. Furthermore, we examine localization techniques specific to autonomous vehicles. The results show that while substantial progress has been made in each subfield, there are persistent limitations. These include a shortage of comprehensive large-scale testing, the absence of diverse and robust datasets, and occasional inaccuracies in certain studies. These issues impede the seamless deployment of this technology in real-world scenarios. This comprehensive literature review contributes to a deeper understanding of the current state and future directions of image processing and sensor fusion in autonomous vehicles, aiding researchers and practitioners in advancing the development of reliable autonomous driving systems.

    Citation: Deven Nahata, Kareem Othman. Exploring the challenges and opportunities of image processing and sensor fusion in autonomous vehicles: A comprehensive review[J]. AIMS Electronics and Electrical Engineering, 2023, 7(4): 271-321. doi: 10.3934/electreng.2023016

    Related Papers:

  • Autonomous vehicles are at the forefront of future transportation solutions, but their success hinges on reliable perception. This review paper surveys image processing and sensor fusion techniques vital for ensuring vehicle safety and efficiency. The paper focuses on object detection, recognition, tracking, and scene comprehension via computer vision and machine learning methodologies. In addition, the paper explores challenges within the field, such as robustness in adverse weather conditions, the demand for real-time processing, and the integration of complex sensor data. Furthermore, we examine localization techniques specific to autonomous vehicles. The results show that while substantial progress has been made in each subfield, there are persistent limitations. These include a shortage of comprehensive large-scale testing, the absence of diverse and robust datasets, and occasional inaccuracies in certain studies. These issues impede the seamless deployment of this technology in real-world scenarios. This comprehensive literature review contributes to a deeper understanding of the current state and future directions of image processing and sensor fusion in autonomous vehicles, aiding researchers and practitioners in advancing the development of reliable autonomous driving systems.


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