Extraction of Vehicle Turning Trajectories at Signalized Intersections Using Convolutional Neural Networks
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RESEARCH ARTICLE-CIVIL ENGINEERING
Extraction of Vehicle Turning Trajectories at Signalized Intersections Using Convolutional Neural Networks Osama Abdeljaber1 · Adel Younis2 · Wael Alhajyaseen3 Received: 22 April 2019 / Accepted: 16 April 2020 © The Author(s) 2020
Abstract This paper aims at developing a convolutional neural network (CNN)-based tool that can automatically detect the left-turning vehicles (right-hand traffic rule) at signalized intersections and extract their trajectories from a recorded video. The proposed tool uses a region-based CNN trained over a limited number of video frames to detect moving vehicles. Kalman filters are then used to track the detected vehicles and extract their trajectories. The proposed tool achieved an acceptable accuracy level when verified against the manually extracted trajectories, with an average error of 16.5 cm. Furthermore, the trajectories extracted using the proposed vehicle tracking method were used to demonstrate the applicability of the minimum-jerk principle to reproduce variations in the vehicles’ paths. The effort presented in this paper can be regarded as a way forward toward maximizing the potential use of deep learning in traffic safety applications. Keywords Traffic safety · Signalized intersections · Turning vehicle trajectories · Convolutional neural networks · Minimum-jerk principle
1 Introduction Road traffic safety is increasingly an issue of global concern. Recently, it has been estimated that 1.4 million people die and 73.25 million get disabled annually as a result of road traffic injuries worldwide [1]. Globally, the annual cost estimation for deaths, injuries, and disabilities due to road crashes is approximately 518 billion dollars, which makes up around 1.5% of the gross national product for middle-income countries [1]. Intersections are recognized as the most complex locations within a highway system, in which conflicts are easily generated, and thus traffic crashes are more likely to occur [2]. Despite them constituting a small part of the highway systems, intersection-related crashes share over * Osama Abdeljaber [email protected] 1
Department of Building Technology, Faculty of Technology, Linnaeus University, P.O. Box 35195, Växjö, Sweden
2
Department of Civil and Architectural Engineering, College of Engineering, Qatar University, P.O. Box 2713, Doha, Qatar
3
Qatar Transportation and Traffic Safety Center, College of Engineering, Qatar University, P.O. Box 2713, Doha, Qatar
50% of all crashes in urban areas and 30% of those in rural regions [2]. Therefore, intersections are deemed crash-prone locations due to the large number of conflict points between traffic streams moving in different direction. Turning traffic has a major role in the safety performance of intersections due to the nature of their maneuvers which are usually characterized with significant variations in paths and speeds depending on drivers’ targeted exit lane, their instinctive judgment, intersection geometry, and other factors [3]. As left-tur
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