Automatic Parking Based on a Bird's Eye View Vision System

Abstract

This paper aims at realizing an automatic parking method through a bird's eye view vision system. With this method, vehicles can make robust and real-time detection and recognition of parking spaces. During parking process, the omnidirectional information of the environment can be obtained by using four on-board fisheye cameras around the vehicle, which are the main part of the bird's eye view vision system. In order to achieve this purpose, a polynomial fisheye distortion model is firstly used for camera calibration. An image mosaicking method based on the Levenberg-Marquardt algorithm is used to combine four individual images from fisheye cameras into one omnidirectional bird's eye view image. Secondly, features of the parking spaces are extracted with a Radon transform based method. Finally, double circular trajectory planning and a preview control strategy are utilized to realize autonomous parking. Through experimental analysis, we can see that the proposed method can get effective and robust real-time results in both parking space recognition and automatic parking.

Vision-Based Parking-Slot Detection: A Benchmark and A Learning-Based Approach

“…Jung et al [34] 98.70% 59.13% Wang et al [36] 98.59% 61.76% Hamada et al [38] 98.61% 63.82% Suhr&Jung [45] 98.70% 76.22% PSD_L 98.87% 92.38%…”

“…Separating marking-line segments were at last recognized by a T-shaped template matching. Following the similar idea as Jung et al's work [34], Wang et al [36] proposed to detect marking-line segments in Radon space [37] as they considered that Radon transform has a better noise-tolerance ability and is more robust than Hough transform. One potential drawback of the methods in [34,36] is their sensitiveness to the marking-line width.…”

“…Following the similar idea as Jung et al's work [34], Wang et al [36] proposed to detect marking-line segments in Radon space [37] as they considered that Radon transform has a better noise-tolerance ability and is more robust than Hough transform. One potential drawback of the methods in [34,36] is their sensitiveness to the marking-line width. In [38], after obtaining the edge map of the surround-view image, Hamada et al made use of the probabilistic Hough transform [39] to extract all line segments and then they inferred the valid parking-slots based on some geometric constraints.…”

“…Besides, the performance of several state-of-the-art methods in this field, including Jung et al's method [34], Wang et al's method [36], Hamda et al's method [38], and Suhr&Jung's method [45], was also evaluated for comparison. We use the precision-recall rates as the performance measure, which are defined as, Each labeled parking-slot is represented as PS i = P i 1 , P i 2 , o i , where P i 1 and P i 2 are the coordinates of the two marking-points forming the entrance-line and o i represents the parking-slot's orientation.…”

Autonomous reverse parking system - vision approach through ridge detector and Kalman filter

“…Way points concept were adopted by other researchers, such as Wada et al (2003) and Li and Chang (2003). Wang et al (2014) proposed double circular trajectory algorithm. It consists of two arcs only, the first is based on the minimum turning radius arc and the second one is free to adjust based on the available space.…”