Research On Monocular Visual Perception Method For Forward Collision Warning

With the rapid development of domestic automobile industry and the continuous improvement of vehicle intelligence,vehicle active safety system has become one of the hot research fields.Forward collision warning system(FCW)is an important part of vehicle active safety system.The system 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 problems in the research of forward collision warning system,such as the high cost of collecting information based on radar and other sensors,and the poor real-time performance of object detection based on vision,which makes it difficult to improve the overall performance of collision warning system.To solve the above problems,the monocular visual perception method for forward collision warning has carried out the following research works:1.According to the requirements of detection accuracy and real-time of object detection function of warning system,a vehicle object detection method based on improved deep learning model is proposed.Firstly,combined with the lightweight feature extraction network,the object detection model is built,and the model is improved to improve the detection accuracy and reduce the computational overhead.Then,the selfmade vehicle data set is used for model training and prediction.Compared with the original model,the final object detection model improves the detection accuracy by about1.2% and reduces the number of parameters by about 2 / 3.2.Aiming at the scene limitation and low accuracy of warning system based on vision in ranging and speed measurement function,firstly,an accurate positioning method based on vision fusion is proposed to reduce the relative error of vehicle distance detection by about 1.3%;Then,the stable tracking of object trajectory is realized by improving the multi-object tracking algorithm,and the real-time and accurate speed estimation is realized combined with the vehicle distance detection results.3.Aiming at the problem of false alarm in different lanes,the forward collision warning strategy based on the same lane judgment is optimized.Firstly,the lane line detection is realized by the algorithm based on improved Hough transform and semantic segmentation,and the algorithm is selected according to the real-time and detection performance to realize the high-precision real-time detection of the lane line of the vehicle;Then,according to the detection results,the front vehicle object is judged in the same lane,and the warning results are output combined with the safe distance model to effectively reduce the false alarm rate.4.Build an experimental scenario to test the performance of relevant modules of the warning system.Firstly,the accuracy of vehicle distance detection is verified by taking vehicle images at a fixed distance;Then,the accuracy of vehicle speed estimation is tested by synchronously collecting the information of camera and odometer;Finally,the effectiveness and feasibility of the warning system are tested through different traffic scenarios.The experimental results show that the monocular visual perception method for forward collision warning in this paper has obvious performance and speed improvement in object detection function,speed measurement and ranging function,and reduces the occurrence of false alarm through the warning strategy based on the same lane judgment.Finally,the feasibility and effectiveness of the warning function are verified.The research on the warning method in this paper is expected to realize the real-time and accurate forward collision warning function based on low-cost sensors,which is of great significance for realizing the intelligence of low-cost vehicles.