Robust Inter-Vehicle Distance Estimation Method Based on Monocular Vision

Abstract

Advanced driver assistance systems (ADAS) based on monocular vision are rapidly becoming a popular research subject. In ADAS, inter-vehicle distance estimation from an in-car camera based on monocular vision is critical. At present, related methods based on a monocular vision for measuring the absolute distance of vehicles ahead experience accuracy problems in terms of the ranging result, which is low, and the deviation of the ranging result between different types of vehicles, which is large and easily affected by a change in the attitude angle. To improve the robustness of a distance estimation system, an improved method for estimating the distance of a monocular vision vehicle based on the detection and segmentation of the target vehicle is proposed in this paper to address the vehicle attitude angle problem. The angle regression model (ARN) is used to obtain the attitude angle information of the target vehicle. The dimension estimation network determines the actual dimensions of the target vehicle. Then, a 2D base vector geometric model is designed in accordance with the image analytic geometric principle to accurately recover the back area of the target vehicle. Lastly, area-distance modeling based on the principle of camera projection is performed to estimate distance. The experimental results on the real-world computer vision benchmark, KITTI, indicate that our approach achieves superior performance compared with other existing published methods for different types of vehicles (including front and sideway vehicles).

Camera-Based System for Drafting Detection While Cycling

“…To perform sensitivity analysis of the distance estimation formulae from Section 2.3, we partially derive the expressions Equations (4) and (5) to the most relevant parameters. To simplify the resulting expressions, we generally assume the tilt angle θ is small and h 1 ≈ h 2 .…”

“…These techniques typically combine object detection networks with geometric techniques but also employ extra information to accurately estimate the distances. These features include the presence of lane markings [4], or the specific geometry of cars [5]. However, in the application of detecting bicycles in triathlon races, neither of these additional features can be used.…”

Vision-Based Distance Measurement in Advanced Driving Assistance Systems

“…One of the earliest applications of instance segmentation for distance estimation was performed by Huang et al They proposed a method that combines instance segmentation and a projection geometry model for distance estimation [27]. In the latest work of Huang et al, they obtained the vehicle attitude angle using an angle regression model and a segmentation algorithm, and then estimated the distance to the vehicle ahead by constructing an "area-distance" geometric model [4].…”

“…Measuring the vehicle-vehicle and vehicle-pedestrian distance is one of the main tasks of 2 of 19 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.…”

High-Accuracy Calibration of On-Site Multi-Vision Sensors Based on Flexible and Optimal 3D Field

“…On-site multi-vision sensors with the characteristics of noncontact, high accuracy and large measurement space have been widely utilized in 3D precision measurement and defect detection fields [1]- [5]. Where the global calibration accuracy of on-site multi-vision sensors determines the measurement accuracy directly.…”