Cooperative Forward Collision Avoidance System Based on Deep Learning

• Main Authors: Farhat, Wajdi, Rhaiem, Olfa Ben, Faiedh, Hassene, Souani, Chokri
• Format: Conference Proceeding
• Language: English
• Subjects: Autonomous Driving; Collision Avoidance; Deep learning; Internet of Vehicles; Road safety; Sensors; Spread spectrum communication; Vehicle-to-infrastructure; Vehicular ad hoc networks
• ISSN: 2161-1351
• DOI: 10.1109/DeSE54285.2021.9719448

Self-driving vehicles can move autonomously without involving a human pilot by sensing the surrounding environment. Having a forward collision avoidance system will help improve road safety and prevent car accidents. However, this system has drawbacks in terms of crash avoidance (i.e., lack of warning messages, complexity of driving situations and weather conditions). Recently, deep learning algorithms become more suitable to overcome this issue, which have better accuracy and adaptive capability to different environments. In this paper, we propose a Cooperative Forward Collision Avoidance System (CFCA) based on deep learning method. Particularly, this system alerts the driver and broadcast a multi-hop warning messages using vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication based ITS-G5. The experimental results show that the proposed system performs better than existing systems and can efficiently help drivers avoid collisions. In fact, we have considered two databases (KITTI and a private database). Our model achieved 94.04% accuracy with approximately 5% loss rate using KITTI database. While performance accuracy of approximately 92.42% was achieved using a private database.