Vehicle Forward Collision Warning Based on Improved Deep Neural Network

Forward Collision Warning System is an important part of vehicle active safety system, it can reduce the occurrence of rear-end collision accidents with high fatality rate and improve the safety of driving. At present, there are still some outstanding issues to be addressed among the existing forward collision warning systems, such as the high cost of information acquisition based on LiDAR and other high-definition sensors, and the poor real-time performance of target detection based on vision. In view of the aforementioned issues and in order to improve the detection accuracy and real-time requirements of the target detection function of the early warning system, this paper proposes an enhanced deep learning model-based vehicle target detection method, and improves the key techniques of target detection, ranging and speed measurement and early warning strategy in the warning system. Then, a target positioning scheme by visual fusion method is employed to improve the accuracy of distance detection, followed by an improved multi-target tracking algorithm.to realize the speed estimation of the front vehicle. Finally, the proposed forward collision warning strategy is demonstrated through real world case studies and results are given in the end.