Pedestrian and Vehicle Detection in Autonomous Vehicle Perception Systems—A Review

Luiz G. Galvao, Maysam Abbod, Tatiana Kalganova, Vasile Palade, Md Nazmul Huda

    Research output: Contribution to journalReview articlepeer-review

    22 Citations (Scopus)
    211 Downloads (Pure)


    Autonomous Vehicles (AVs) have the potential to solve many traffic problems, such as accidents, congestion and pollution. However, there are still challenges to overcome, for instance, AVs need to accurately perceive their environment to safely navigate in busy urban scenarios. The aim of this paper is to review recent articles on computer vision techniques that can be used to build an AV perception system. AV perception systems need to accurately detect non-static objects and predict their behaviour, as well as to detect static objects and recognise the information they are providing. This paper, in particular, focuses on the computer vision techniques used to detect pedestrians and vehicles. There have been many papers and reviews on pedestrians and vehicles detection so far. However, most of the past papers only reviewed pedestrian or vehicle detection separately. This review aims to present an overview of the AV systems in general, and then review and investigate several detection computer vision techniques for pedestrians and vehicles. The review concludes that both traditional and Deep Learning (DL) techniques have been used for pedestrian and vehicle detection; however, DL techniques have shown the best results. Although good detection results have been achieved for pedestrians and vehicles, the current algorithms still struggle to detect small, occluded, and truncated objects. In addition, there is limited research on how to improve detection performance in difficult light and weather conditions. Most of the algorithms have been tested on well-recognised datasets such as Caltech and KITTI; however, these datasets have their own limitations. Therefore, this paper recommends that future works should be implemented on more new challenging datasets, such as PIE and BDD100K.
    Original languageEnglish
    Article number7267
    Issue number21
    Early online date31 Oct 2021
    Publication statusPublished - Nov 2021

    Bibliographical note

    This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

    Publisher Copyright:
    © 2021 by the authors. Licensee MDPI, Basel, Switzerland.


    Funding Information:
    This research was funded by EPSRC DTP PhD studentship at Brunel University London.


    • Autonomous vehicle
    • Deep learning
    • Generic object detection
    • Pedestrian detection
    • Traditional technique
    • Vehicle detection

    ASJC Scopus subject areas

    • Analytical Chemistry
    • Information Systems
    • Atomic and Molecular Physics, and Optics
    • Biochemistry
    • Instrumentation
    • Electrical and Electronic Engineering


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