# Vision-Based Distance Measurement in Advanced Driving Assistance Systems  
[PERSON]  
1 College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China; [EMAIL_ADDRESS] (Z.Z. ); [EMAIL_ADDRESS] (X.J.) 1 College of Civil Aviation Key Laboratory of Aircraft Health Monitoring and Intelligent Maintenance, Civil Aviation Administration of China, Nanjing 210016, China College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China; [EMAIL_ADDRESS]  
11 September 20202014 October 20202017  
ADASs [2; 3]. Generally speaking, existing measurement methods can be placed into two categories [4]: active sensor-based methods and passive vision-based methods. Active sensor-methods, such as LiDAR and ultrasonic sensors, use echo signals to measure the distance to targets. The main advantage of active sensors is that they can be used in different visibility conditions. However, the high cost of the LiDAR system greatly restricts their scope of application [5]. As a result of the lack of shape, texture, and color information, it is difficult to use the data from ultrasonic sensors to collect further useful information from the driving environment. In recent years, because passive vision sensors are relatively cheap and able to capture forward-looking images from the vehicle, containing the rich information required for safe driving, they have become the basic equipment used in existing ADASs [6; 7]. The vision-based driving assistance system (V-DAS), a type of ADAS, has been applied in several specific functions of ADAS, such as evasive pedestrian protection, lane-keeping support, and traffic sign warning. The existing passive vision-based methods used in V-DAS can be further divided into two subcategories [8]: stereo vision-based approaches and monocular vision-based approaches. The former uses multiple view geometry and stereo image pairs to rebuild a 3D space and generate the depth information of the target. However, errors and computational complexities from the calibration and matching of stereo image pairs reduce the measurement accuracy and efficiency to a certain extent in actual road scenarios [9]. Recently, monocular vision-based ADAS has become a research hotspot in the field of intelligent vehicles [10]. Monocular vision methods have certain advantages, such as being cheap, having a simple hardware structure, and a wide field of application. However, because of the lack of a distance scale, traditional monocular vision-based measurement methods cannot complete the absolute distance estimation using only a single image. With the rapid development of deep learning technologies, and using the powerful data mining capabilities of deep learning, it is entirely possible to complete the distance estimation of different targets online based on monocular vision by constructing a deep learning network. Consequently, the main outcome of this paper is to present a system that only uses a single image to complete vehicle-vehicle and pedestrian-vehicle absolute distance measurements online using ADAS. In addition to monocular vision, the proposed system does not need extra sensors as inputs for online applications. Specifically, in this study, we first constructed and trained a semi-supervised deep learning network which uses the forward-looking visible light image pairs captured by the vehicle-mounted CCD camera and sparse depth labels obtained by LiDAR for offline training. It was then able to estimate the absolute depth value of each pixel of a single image in the camera body coordinate system online. Compared with unsupervised depth estimation methods, the depth estimation results of this semi-supervised network are more accurate. On the basis of the depth map of the input image, focusing on the two main participants in road traffic activities, i.e., pedestrians and vehicles, this paper further proposes a pedestrian-vehicle and a vehicle-vehicle distance measurement method.