Special Traffic Event Detection: Framework, Dataset Generation, and Deep Neural Network Perspectives

• Published in: Sensors (Basel, Switzerland) Vol. 23; no. 19; p. 8129
• Main Authors: Lee, Soomok, Lee, Sanghyun, Noh, Jongmin, Kim, Jinyoung, Jeong, Harim
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 28.09.2023; MDPI
• Subjects: Accident prevention; Accidents; Accuracy; Algorithms; Artificial intelligence; Cameras; Classification; Data mining; Datasets; Deep learning; Freeways; Infrastructure; Methods; Neural networks; Public transportation; road event recognition; Roads & highways; scene classification; Special events; special traffic accident detection; Surveillance; Telematics; Traffic accidents & safety; Transportation planning; Vehicles; South Korea
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s23198129

Identifying early special traffic events is crucial for efficient traffic control management. If there are a sufficient number of vehicles equipped with automatic event detection and report gadgets, this enables a more rapid response to special events, including road debris, unexpected pedestrians, accidents, and malfunctioning vehicles. To address the needs of such a system and service, we propose a framework for an in-vehicle module-based special traffic event and emergency detection and safe driving monitoring service, which utilizes the modified ResNet classification algorithm to improve the efficiency of traffic management on highways. Due to the fact that this type of classification problem has scarcely been proposed, we have adapted various classification algorithms and corresponding datasets specifically designed for detecting special traffic events. By utilizing datasets containing data on road debris and malfunctioning or crashed vehicles obtained from Korean highways, we demonstrate the feasibility of our algorithms. Our main contributions encompass a thorough adaptation of various deep-learning algorithms and class definitions aimed at detecting actual emergencies on highways. We have also developed a dataset and detection algorithm specifically tailored for this task. Furthermore, our final end-to-end algorithm showcases a notable 9.2% improvement in performance compared to the object accident detection-based algorithm.