# Vehicle Distance Measurement Method of Two-Way Two-Lane Roads Based on Monocular Vision
## 1 Introduction  
As one of the most common highway forms in China's highway network, two-way two-lane roads account for more than 95% of the total highway mileage in the western region and play an important role in the highway network [1]. Due to the particularity of two-way two-lane roads, vehicles need to borrow the opposite lane when overtaking, which is common in many developing countries in Asia, such as Vietnam and China [2]. However, a serious traffic accident may occur because of the false temporal and distance estimations regarding the uncoming vehicles made by drivers when the vehicle is overtaking [3; 4; 5; 6]. Therefore, improving the driving safety of vehicles on two-way two-lane roads is an important research content of advanced driving assistance systems. Moreover, automatic driving technology of two-way two-lane roads will be an important research direction in the future. Research shows that the longitudinal distance between the vehicle and the forward vehicle, as well as the longitudinal distance between the vehicle and the opposite vehicle, is the main risk factor of overtaking behavior on two-way two-lane roads [7]. Therefore, measuring these distances is the basis and key to the automatic driving technology of two-way two-lane roads. The primary task of vehicle distance measurement is vehicle detection. With the rapid development of computer vision, vehicle detection and distance measurement technology based on on-board cameras has been improved and promoted. Among them, the monocular vision system has attracted the attention of the industry because of its simple structure, small amount of calculation, and high real-time performance [8]. In terms of vehicle detection technology based on computer vision, the traditional vehicle detection method uses prior knowledge, such as vehicle color and edge, to detect the vehicles in front. However, due to its dependence on prior knowledge, the method has insufficient adaptability and generalization capabilities [9]. The vehicle detection method based on deep learning extracts vehicle features through the convolutional neural network, which has stronger adaptability, greatly improves detection accuracy and speed, and is more widely used [10]. In terms of vehicle distance measurement technology, at present, vision-based vehicle ranging methods include: the geometric relationship method, imaging model method, data regression modeling method, inverse perspective mapping method, and image depth estimation based on deep learning. [PERSON] [11] used the relationship between the distance and height of the target in the image to establish a mathematical regression model and then used the quadratic curve to measure the distance of the regression model. This method has a large workload and lacks the generalization ability and portability of the model. [PERSON] [12] used the vehicle width ratio to measure the distance. This method is easily affected by the diversity of vehicle size and thus affects the ranging accuracy. [PERSON] and [PERSON] [13; 14] converted the original image into an aerial view to restore the plane information of the road ahead. The IPM image obtained through the conversion is used to calculate the distance of the target vehicle. This method is simple and feasible but ignores the influence of the camera attitude angle. [PERSON] [15] used a deep learning network to train a large number of depth maps, then combined the estimated depth map with the results of instance segmentation to measure the distance between the subject vehicle and the target vehicle. This method has high-ranging accuracy, but the data contain more redundant information, which is not easy to be processed. The geometric relationship method establishes the ranging model through the geometric relationship between the camera and the target in a three-dimensional coordinate system; it has a small workload, a simple model, and portability compared with other methods.