## 1 Introduction  
With the continuous development of autonomous driving technology and the wide application of robot navigation systems, vehicle pose estimation has emerged as a prominent research area in the realms of computer vision and machine perception. The objective of pose estimation is to precisely ascertain the position and orientation of a vehicle or robot within a three\(-\)dimensional space. This capability is paramount for achieving tasks such as autonomous navigation, environmental sensing, and safe driving. Vehicle pose estimation holds significant importance across a range of applications, although it has received comparatively less attention compared to human pose estimation. Recent technological advancements in the automotive industry have heightened the demand for precise vehicle pose estimation. This surge in interest is attributed to its applications in autonomous driving, traffic regulation, and scenario analysis. Vehicle pose estimation entails the identification of specific key points on a given vehicle. This task presents challenges due to the diverse array of vehicles characterized by variations in color, shape, and size.  
Unlike human pose estimation, which is challenging due to the high degree of freedom of human joints and substantial occlusion, vehicle pose involves a more rigid structure with diverse types of occlusions. The expansion of the automotive industry has resulted in significant variability within each vehicle class, posing challenges in developing a robust method applicable to different types of vehicles. Moreover, camera viewpoints exhibit considerable variability in terms of vehicle height. As of now, vehicle datasets are primarily annotated for purposes other than defining poses, leading to a lack of conventions in this regard. This absence of standardized pose definitions poses challenges in identifying representative key points for training and evaluating deep learning models.  
Taking into account the motion constraints inherent in sequences of edge feature points in driving videos, a strategy emerges. This involves refining the initial structural parameters encompassing height, width, and length during vehicle tracking. Subsequently, the entire rigid structure of the vehicle serves as a foundational template. This template is utilized to compute the anticipated distances of components and the relative yaw angle. The results of this process are visually depicted in Figure 1. The primary contribution of this paper lies in the novel approach to determining the frontal vehicle's distance and yaw angle. This is achieved through the utilization of pixel coordinates associated with edge feature points. Given the foundational understanding of the height, width, and length of target structures, the collection of edge points can be harnessed to gauge the distance of multiple structures. This is accomplished via the utilization of two distinct ranging models. Notably, the hybrid ranging model deftly sidesteps errors stemming from blurred regions of interest (ROIs) or alterations in the camera's pitch angle. The subsequent innovation lies in the amalgamation of several elements: the generation of a tracking search area and the optimization of point similarity. This fusion serves the purpose of effectively aligning with the target vehicle. Lastly, a crucial advancement emerges in the form of the Gaussian distribution model related to changes in the distance change ratio. This model plays a pivotal role in selecting valid sequences of edge points, ultimately leading to the accurate correction of structural parameters.  
Figure 1: System outputs. Top: 2D vehicle bounding boxes, multi–layer edge point locations. In this example, red dots correspond to bottom edges, green dots to middle beam edges, and blue dots to top edges. Middle: 2D vehicle bounding boxes, multi–layer edge point locations. Bottom: vehicle overlooking orientation estimation and the lane lines. The host vehicle’s camera is represented by a blue triangle.