Objective

2

The unceasing progress in China's economy and urbanization has caused the increase in internal expressway mileage and automobiles

and this situation leads to concern on traffic jam among Chinese people. In view of the increasing requirements for safe driving by operators

autonomous driving techniques have attracted widespread attention from domestic and foreign scholars. However

automotive active safety techniques remain in the stage of R & D and testing in China. Many problems

such as few practical products

low-precision safety distance model (SDM)

difficulty in obtaining key parameters

and disregarding drivers' characteristics

exist. Corresponding research is accordingly necessary. The vehicle-road visual collaboration technology

which is the core technology for the field of automotive active safety

is applied reasonably to anti-collision early warning systems. Compared with ultrasound

laser

and radar

the technology has the following advantages:the collected vehicle-road parameter information is more abundant

no perceived blind zone exists

the state of dangerous vehicles is dynamically predicted

it is not restricted by view block

and it is more affordable and suitable for the human eye's information capture habits. The rapid and accurate detection of preceding vehicles and the establishment of a stable and reliable SDM are two difficulties. To overcome these problems

a freeway anti-collision-warning algorithm based on vehicle-road visual collaboration was proposed.

Method

2

The environment was sensed through the driving recorder. The road lane line

the vehicle speed

and the position of the vehicle in front were combined to realize the anti-collision warning. The algorithm mainly includes three parts

namely

the construction of an SDM

the lane detection based on you only look once v3(YOLOv3)

and the anti-collision-warning algorithm based on the visual collaboration of the road. A real-time calculation method for the safety zone was proposed to construct an SDM between the current and preceding vehicles. The lateral distance of the safety zone was obtained by detecting and fitting the mathematical model of the lane line. A speed detection algorithm was proposed to calculate the current vehicle's driving speed based on video images. The longitudinal distance of the safety zone was obtained through the current vehicle's driving speed and the driver's behavioral and perceptual response characteristics. The early warning area of the safety zone was constituted through the horizontal and vertical distances between lane lines. SDM was built using the safe area real-time calculation algorithm to form an early warning safety zone in front of the current vehicle. The deep neural network YOLOv3 was used to detect preceding vehicles in real time for obtaining the location information of the vehicle. After the input image was detected with YOLOv3

the category where the object belongs to and the coordinate information of the object were predicted. The lower-right (or lower-left) coordinates of the bounding box of the vehicle was used to improve the accuracy of the early warning. The real-time calculation technology of the safety zone and the results of lane-line tracking were combined through the vehicle-road visual collaborative algorithm to build a safety early warning area on the road that changes with the vehicle speed in real time. The coordinate information of the vehicle in a video was obtained using YOLOv3. Different tips depending on where the vehicle was in the video were given through the anti-collision-warning algorithm based on vehicle-road visual collaboration.

Result

2

The proposed method was tested and evaluated on the video captured by a driving recorder

which was acquired from a passenger transportation company in Huai'an. The sequence of the test video was Test_1 to Test_20

and these video sequences contained different highway driving environments that can effectively verify the pros and cons of the algorithm. Vehicle detection experiments were performed on a desktop computer with an NVIDIA GeForce GTX 1070TI GPU. The collision-warning experiment was performed on a Mazda CX-4 with millimeter-wave radar and a laser rangefinder. The test section was Changshen Expressway. The experiments showed that the vehicle detection accuracy of the retrained YOLOv3 algorithm is up to 98.04%. Compared with the forward obstruction warning system of Mazda CX-4

the proposed algorithm can achieve side and forward warning

and an alarm is issued 0.8 s in advance. Although the proposed method demonstrates a good result in terms of detection speed

accuracy

and collision early warning

the construction of SDM can still be further improved compared with other algorithms because the research on the lane-line detection is limited on a good weather. Further research will be conducted in the case of poor visibility

such as rain

fog

and night.

Conclusion

2

Compared with the traditional anti-collision-warning method based on vehicle ultrasound

radar

or laser ranging

the proposed method can effectively improve the accuracy of early warning and better guarantee the driving safety on expressways. The algorithm has four advantages. First

progressive probabilistic Hough transform combined with morphological filtering

which had good robustness to interference noise and high detection accuracy

was proposed to detect lane lines. Second

real-time computing technology of safety zone

which considered the behavioral and intuitive response characteristics of drivers and was in line with the real driving situation

was proposed to construct SDM. Third

theYOLOv3 target detection algorithm was used to realize single-category detection

which further improved the video frames to reach 43 frame per second with guaranteed accuracy. Finally

the vehicle position information and road SDM were considered

and an anti-collision warning algorithm based on vehicle-road vision coordination was proposed. This advantage effectively improved the accuracy of vehicle warning and ensured the driving safety of drivers.