Map-Enhanced Ego-Lane Detection in the Missing Feature Scenarios

Abstract

As one of the most important tasks in autonomous driving systems, ego-lane detection has been extensively studied and has achieved impressive results in many scenarios. However, ego-lane detection in the missing feature scenarios is still an unsolved problem. To address this problem, previous methods have been devoted to proposing more complicated feature extraction algorithms, but they are very time-consuming and cannot deal with extreme scenarios. Different from others, this paper exploits prior knowledge contained in digital maps, which has a strong capability to enhance the performance of detection algorithms. Specifically, we employ the road shape extracted from OpenStreetMap as lane model, which is highly consistent with the real lane shape and irrelevant to lane features. In this way, only a few lane features are needed to eliminate the position error between the road shape and the real lane, and a search-based optimization algorithm is proposed. Experiments show that the proposed method can be applied to various scenarios and can run in real-time at a frequency of 20 Hz. At the same time, we evaluated the proposed method on the public KITTI Lane dataset where it achieves state-of-the-art performance. Moreover, our code will be open source after publication. INDEX TERMS Ego-lane detection, missing feature, OpenStreetMap, parameter estimation.

A Deep Learning-Based Benchmarking Framework for Lane Segmentation in the Complex and Dynamic Road Scenes

“…Moreover, the region of interest (ROI) is determined using several techniques to segment a part of the road containing the needed lane detection information. Selecting ROI can be done by conventionally choosing the lower two-thirds of the image area as done in [12] or the bottom side as in [13], or a subset from the frame as in [14] and [15]. However, these methods are inefficient with urban road scenes.…”

“…However, we can compare our results with other related work by focusing on just the common concerns for lane detection. Wang et al [15] proposed a framework that utilizes range and camera images along with OpenStreetMap for ego-lane detection in challenging scenarios with dynamic features. Chen and Chen [35] introduced RBNet to simultaneously detect road lanes, while a deep learning methodology for lane segmentation using up-convolutional networks was presented in [68].…”

“…Chen and Chen [35] introduced RBNet to simultaneously detect road lanes, while a deep learning methodology for lane segmentation using up-convolutional networks was presented in [68]. In terms of dice coefficient (which is equivalent to F1-measure in the binary segmentation context), our method shows maximum dice coefficient of 98.9% while the maximum F1-measure (MaxF) was 93.56%, 90.54%, and 89.88% in [15], [35] and [68], respectively. In [16], authors considered the host lane detection based on the normal map in harsh road conditions.…”

A Perception Centered Self-Driving System without HD Maps

“…Many researchers, like Kim [12], used Convolutional Neural Network (CNN) to reduce noise and get the segmentation of the markings of those lines. Wang [13] used shape extracted from OpenStreetMap (OSM) as prior knowledge to help detect the lanes. Some problems remain for the CNN supported approaches.…”

“…As a result, CNN was not used for lines detection in this paper. The proposed lines detector leverages the lines information from a topology map, similar to what Wang did in [13] from the OSM, as prior knowledge to help. The proposed lines detector separates different line types to boost the performance even more by using different lines detector for each type of lines (solid or dashed lines, straight or curved lines).…”