Main Article Content
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 project 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 movement , as well as to detect static objects and detect the information they are providing. This project , focuses on the computer vision techniques used to detect pedestrians and vehicles. There have been many projects and reviews on pedestrians and vehicles detection so far. However, most of the past projects 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.
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WHO. Global Status Report on Road Safety 2018: Summary; Technical Report; World Health Organization: Geneva,
Switzerland, 2018. 2. Maddox, J. Improving Driving Safety through Automation, Presentation at the Congressional Robotics
Caucus; National Highway Traffic Safety Administration: Washington, DC, USA, 2012. 3. IIHS-HLDI. Advanced Driver
Assistance. 2019. Available online: https://www.iihs.org/topics/advanced-driver-assistance (accessed on 6 October 2020). 4.
Colonna, M. Urbanisation Worldwide. 2018. Available online: https://ec.europa.eu/knowledge4policy/foresight/topic/
continuing-urbanisation/urbanisation-worldwide_en (accessed on 6 October 2020). 5. Hart, A.; Cox, C. How Autonomous
Vehicles Could Relive or Worsen Traffic Congestion; Technical Report; SBD HERE: Berlin, Germany, 2017. Available
could_relieve_or_worsen_traffic_congestion_white_paper.pdf (accessed on 5 October 2020). 6. Benenson, R.; Omran, M.;
Hosang, J.; Schiele, B. Ten years of pedestrian detection, what have we learned? In European Conference on Computer Vision;
Springer: Zurich, Switzerland, 2014; pp. 613–627. 7. Nguyen, D.T.; Li, W.; Ogunbona, P.O. Human detection from images
and videos: A survey. Pattern Recognit. 2016, 51, 148–175. [CrossRef] 8. Antonio, J.A.; Romero, M. Pedestrians’ Detection
Methods in Video Images: A Literature Review. In Proceedings of the 2018 International Conference on Computational
Science and Computational Intelligence (CSCI), Las Vegas, NV, USA, 13–15 December 2018; pp. 354–360. 9. Ragesh, N.K.;
Rajesh, R. Pedestrian detection in automotive safety: Understanding state-of-the-art. IEEE Access 2019, 7, 47864–47890.