高速公路场景的车路视觉协同行车安全预警算法

目的 基于视觉的车辆行驶安全性预警分析技术是目前车辆辅助驾驶的一个重要研究方向，对前方多车道快速行驶的车辆进行精准的跟踪定位并建立稳定可靠的安全距离预警模型是当前研究难点。为此，提出面向高速公路场景的车路视觉协同行车安全预警算法。方法 首先提出一种深度卷积神经网络SF_YOLOv4（single feature you look only once v4）对前方车辆进行精准的检测跟踪；然后提出一种安全距离模型对车辆刹车距离进行计算，并根据单目视觉原理计算车辆间距离；最后提出多车道预警模型对自车行驶过程的安全性进行分析，并对司机给予相应安全提示。结果 实验结果表明，提出的SF_YOLOv4算法对车辆检测的准确率为93.55%，检测速度（25帧/s）领先对比算法，有效降低了算法的时间和空间复杂度；提出的安全距离模型计算的不同类型车辆的刹车距离误差小于0.1 m，与交通法建议的距离相比，本文方法计算的安全距离精确度明显提升；提出的多车道安全预警模型与马自达6（ATENZA）自带的前方碰撞系统相比，能对相邻车道车辆进行预警，并提前0.7 s对前方变道车辆发出预警。结论 提出的多车道预警模型充分考虑高速公路上相邻车道中的车辆位置变化发生的碰撞事故；本文方法与传统方法相比，具有较高实用性，其预警效果更加客观，预警范围更广，可以有效提高高速公路上的行车安全。

Vehicle-road visual cooperative driving safety early warning algorithm for expressway scenes

Objective Vehicles motion are prone to traffic accidents on the expressway due to their high speed, which mainly include rear-end collisions, punctures, scratches, side collisions, etc. Among them, high-speed rear-end collisions, overtaking and lane changing accounted for t the most losses of them. Therefore, it is essential to analyze the driving safety and reduce the occurrence of accidents. Thanks to the development of deep learning, vision-based vehicle driving safety early warning analysis technology is currently an important research direction for vehicle aided driving.We propose an early warning algorithm for vehicle-road visual collaborative driving safety in expressway scenes. Method The vehicles motion safety early warning algorithm in synchronized vehicles-road visual expressway scenarios is facilitated. First, we illustrated a vehicle motion recorder to monitor and combine vehicle target recognition and positioning, a safe distance model, and analyzes driving safety based on a multi-lane early warning algorithm. It is composed of three parts like vehicle target recognition and positioning technology, safety distance model and multi-lanes warning algorithm. The image processing technology is as the input to detect the distance between the vehicle ahead and the vehicle body. A safe distance model early warning fusion algorithm is, performed to safety analysis on the motion of the vehicle. Our deep convolutional neural network of single feature you look only once v4(SF_YOLOv4) detects and tracks the vehicle ahead accurately. Then, the range of the vehicles is calculated in terms of the perspective transformation principle combined with the vehicle position information. Finally, a safe distance model and fusion algorithm are proposed to analyze the vehicle safety. In the target detection part, our method is improved on the basis of YOLOv4. The backbone network is replaced by CSPDarknet53 with a smaller layer of cross stage paritial Darknet17(CSPDarknet17) network, which reduces the number of model parameters and calculations, improves the speed of target detection, and the accuracy of target detection for a single scene less affected. Our four-feature pyramid network(F-FPN) is illustrated to construct a feature pyramid, and the 104×104 scale feature map is added to the feature network. It can improve the detection effect of small targets effectively. In the distance calculation part, the monocular vision calculation principle is used to perform perspective transformation on the selected area. The corresponding equations are fitted in the horizontal and vertical directions to calculate the distance via the referenced lane and lane line data. In the part of the safety distance, the braking loss of the vehicle motion is ignored according to the energy change of the vehicle during braking. During the braking process, the kinetic energy is converted into work to overcome friction, work to overcome wind resistance, and work to overcome inertia. The safety distance model calculates the safety zone in the multiple lanes ahead. In the part of the safety warning model, a multi-lanes forward safety warning scheme is proposed. The corresponding warning is given in according with the corresponding position of the vehicle in the adjacent lane ahead, which can effectively avoid collision accidents caused by rear-end collisions and abnormal lane changes. Result We use mean average precision(mAP), frames per second(FPS), recall, model parameters, model calculations to evaluate the target detection network. At the same time, different algorithms are used to carry out comparative experiments on self-built data sets. The safety distance is verified by selecting different vehicle data. a real video data set of vehicles motion on express way is established by simulating the actual high-speed vehicle motion environment. According to the experimental analysis, the main potentials of the algorithm are as follows:1) a single-stage feature neural network (SF_YOLOv4) is proposed to detect vehicle targets in a single scene quickly. The detection speed is greatly improved through achieving precise positioning of the vehicle in front, improving the detection effect of small targets, and ensuring the accuracy in the case of unclear change. Our experiments show that the SF_YOLOv4 can detect the vehicle ahead in real time with 93.55% accuracy, and the detection speed is 25 frames per second; 2) Our safe distance model is melted the momentum and energy conservation of the vehicle in motion, and the mechanical features of the vehicle are in consistency under actual driving conditions. Our verified safety distance model calculates the braking distance error of different types of vehicles is less than 0.1 m; 3) A multi-lanes safety warning algorithm is illustrated to construct a safety warning area for adjacent vehicles in front of the vehicle. Conclusion Our SF_YOLOv4 target detection model can achieve a perfect match between detection speed and accuracy. The proposed early warning model of safety fusion realize the changes of vehicle positions on adjacent lanes of expressways, and can predict the impact of rear-end collisions, overtaking and lane changes. Once the vehicle is outside the multi-lane safety warning zone ahead, the safety distance will be displayed in real time, and the target vehicle will be marked, and the driver will be reminded to drive safely; when the vehicle enters the warning zone, in addition to the real-time display of the safety distance, the mark will also be calculated. The distance between the vehicle and the workshop is displayed to the driver in real time, and the driver is reminded to avoid a collision once a vehicle in the corresponding lane, and a reminder is issued through voice and other means. Compared to the traditional method, the early warning effect is more objective, the warning range is wider, and the safety of highway driving can be improvedeffectively.