Objective

2

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

2

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

2

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

2

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.