# Vehicle Distance Measurement Method of Two-Way Two-Lane Roads Based on Monocular Vision
## 4 Conclusions  
This paper proposed a vehicle distance measurement method for two-way two-lane roads based on monocular vision. Firstly, the traffic data set of two-way two-lane roads was established; the vehicle detection model of two-way two-lane roads was trained using the YOLOv5s network. Secondly, in order to improve the ranging accuracy of the vehicle in front, a rotation model of the camera's imaging plane was established, and the simulation of ranging results was designed; it was found that the roll angle of the camera would affect the longitudinal distance measurement of vehicles using the traditional geometric ranging  
Figure 10: Analysis of the ranging results. (**a**) Comparison of ranging results; (**b**) Comparison of ranging errors.  
method. When the roll angle existed, the ranging result would deviate from the normal value. The degree of deviation would vary with the vehicle position. The larger the roll angle, the greater the deviation. The improved geometric ranging method considering the roll angle of the camera was proposed to solve this problem, which corrected the imaging position of the feature point of the vehicle changed due to the roll angle of the camera. Finally, the experiment was carried out on a real two-way two-lane road to verify the proposed method. The experimental results showed that the method proposed in this paper was effective, and the improved geometric ranging method had higher ranging accuracy than the other two methods when measuring the longitudinal distance between the vehicle and the forward vehicle, as well as the longitudinal distance between the vehicle and the opposite vehicle on two-way two-lane roads. In addition, the ranging accuracy was improved more significantly at long distances. The problem of low-ranging accuracy caused by the roll angle of the camera was improved. The proposed method in this paper could improve the problem of low-ranging accuracy caused by the roll angle of the camera, provide a reference for vision-based vehicle ranging technology in multi-lane scenes, and lay a foundation for the subsequent development of vision-based vehicle driving assistance technology in this scene. However, due to the small imaging area of the vehicle at a long distance, which leads to inaccurate recognition or jumping of the object detection frame, there was still a large error in the distance measurement of the proposed method at a long distance. In the future, it will be considered to integrate vision technology with sensors such as radar to achieve a higher precision distance measurement.  
Conceptualization, R.Y.; methodology, R.Y.; software, S.Y.; experiment, S.Y. and Q.Y.; validation, S.Y. and Q.Y.; formal analysis, R.Y. and S.Y.; data curation, S.Y., and Q.Y.; writing--original draft preparation, R.Y., S.Y. and J.H.; writing--review and editing, S.Y. and F.Y.; visualization, J.H. and F.Y.; supervision, J.H.; project administration, R.Y.; funding acquisition, J.H. All authors have read and agreed to the published version of the manuscript. This research was funded by Guangxi Innovation Driven Development Project, grant number AA22068061; Guangxi Innovation Driven Development Project, grant number AA22068063. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Not applicable.  
Thanks to [PERSON]'s guidance, [PERSON]'s contribution to the experiment and the help of the research team members. Without their guidance and help, the study could not be completed. Conflicts of Interest: The authors declare no conflict of interest.