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Book ChapterDOI

Vision-Based Lane Detection for Advanced Driver Assistance Systems

01 Jan 2021-Vol. 688, pp 537-546

TL;DR: Results obtained show that the proposed system for lane detection, self-calibration and vehicle offset estimation is effective, accurate for both straight and curved lanes and robust to challenging environments.
Abstract: Lane lines play a key role in indicating traffic flow and directing vehicles; lane detection serves as a core component in most of the modern-day advanced driver assistance systems (ADASs). Computer vision-based lane detection is an essential technology for self-driving cars. This paper proposes a lane detection system to detect lane lines in urban streets and highway roads under complex background. In order to nullify the distortions caused by the camera lenses, we generate a distortion model by calibrating images against a known object, and apply a generalized filtering approach using Sobel operator (Canny edge detection) in HLS color space. A bird eye view of image is generated using perspective transformation. A special search strategy using sliding window algorithm is used to detect lane lines, and later, curve fitting is done using polynomial regression. Thus, the obtained lane detector is overlaid upon a video to fill the detected portion of the lane. Then, it is applied to the video to detect lane lines. The image processing pipeline is written in Python using OpenCV libraries, and video processing is done using MoviePy. In this paper, the system developed is tested by applying it on a video taken from a camera mounted over the car. The environment used to implement the system is Anaconda. The results obtained show that the proposed system for lane detection, self-calibration and vehicle offset estimation is effective, accurate for both straight and curved lanes and robust to challenging environments.
Topics: Video processing (54%), Canny edge detector (54%), Advanced driver assistance systems (54%), Sobel operator (54%), Image processing (53%)
References
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Journal ArticleDOI
Yang Xing1, Chen Lv1, Long Chen2, Huaji Wang1  +4 moreInstitutions (4)
TL;DR: In this paper, previous vision-based lane detection studies are reviewed in terms of three aspects, which are lane detection algorithms, integration, and evaluation methods, and a Computational experiment-based parallel lane detection framework is proposed.
Abstract: Lane detection is a fundamental aspect of most current advanced driver assistance systems U+0028 ADASs U+0029. A large number of existing results focus on the study of vision-based lane detection methods due to the extensive knowledge background and the low-cost of camera devices. In this paper, previous vision-based lane detection studies are reviewed in terms of three aspects, which are lane detection algorithms, integration, and evaluation methods. Next, considering the inevitable limitations that exist in the camera-based lane detection system, the system integration methodologies for constructing more robust detection systems are reviewed and analyzed. The integration methods are further divided into three levels, namely, algorithm, system, and sensor. Algorithm level combines different lane detection algorithms while system level integrates other object detection systems to comprehensively detect lane positions. Sensor level uses multi-modal sensors to build a robust lane recognition system. In view of the complexity of evaluating the detection system, and the lack of common evaluation procedure and uniform metrics in past studies, the existing evaluation methods and metrics are analyzed and classified to propose a better evaluation of the lane detection system. Next, a comparison of representative studies is performed. Finally, a discussion on the limitations of current lane detection systems and the future developing trends toward an Artificial Society, Computational experiment-based parallel lane detection framework is proposed.

70 citations


Journal ArticleDOI
TL;DR: A Bayesian classifier based on mixtures of Gaussians is applied to classify the lane markings present at each frame of a video sequence as dashed, solid, dashed solid, solid dashed, or double solid.
Abstract: This paper presents a new approach for road lane classification using an onboard camera. Initially, lane boundaries are detected using a linear–parabolic lane model, and an automatic on-the-fly camera calibration procedure is applied. Then, an adaptive smoothing scheme is applied to reduce noise while keeping close edges separated, and pairs of local maxima–minima of the gradient are used as cues to identify lane markings. Finally, a Bayesian classifier based on mixtures of Gaussians is applied to classify the lane markings present at each frame of a video sequence as dashed, solid, dashed solid, solid dashed, or double solid. Experimental results indicate an overall accuracy of over 96% using a variety of video sequences acquired with different devices and resolutions.

43 citations


Journal ArticleDOI
Paolo Bosetti1, Mauro Da Lio1, Andrea Saroldi2Institutions (2)
TL;DR: A curve negotiation “behavior” that can be used-within subsumption architectures - to produce artificial agents with the ability to negotiate curves in a humanlike way may be used to implement functions spanning different levels of automation.
Abstract: This paper describes a curve negotiation “behavior” that can be used—within subsumption architectures—to produce artificial agents with the ability to negotiate curves in a humanlike way. This may be used to implement functions spanning different levels of automation, from assistance (curve warning) to automated (curve speed control). This paper gives the following: 1) a summary of related works and of the subsumption architecture conceptual framework; 2) a detailed description of the function within this framework; 3) experimental data for validation and tuning derived from user tests; 4) guidelines on integration of the function within advanced driver assistance systems with different automation levels, with examples; and 5) a comparison with experimental data of the human curve speed choice models in the state of the art.

37 citations


Journal ArticleDOI
Chang Yuan1, Hui Chen1, Ju Liu1, Di Zhu1  +1 moreInstitutions (2)
TL;DR: This work improves an adaptive threshold segmentation method and denoising operations to enhance the lane markings and generates a normal map by using the depth information and extracting a segmented road pavement without vehicles and buildings based on the normal map.
Abstract: Detection of road or lane is indispensable for the environmental perception of advanced driver assistance systems. It has been an active field of research with a wide application prospect. However, due to the complex illumination and interferences, such as vehicles and shadows in the real driving environment, lane detection is still a challenging task today. To address these issues, a robust method for road segmentation and lane detection based on a normal map is proposed. The first step of this approach is to generate the normal map by using the depth information and then extract a segmented road pavement without vehicles and buildings based on the normal map. Second, we improve an adaptive threshold segmentation method and denoising operations to enhance the lane markings. Third, the combination of Hough transform and vanishing point makes it more accurate to determine the starting points of host lanes, and then the lanes in the following image sequence can be detected in the adaptive region of interest. Compared with the state-of-the-art methods, the experimental results on the data sets in two countries demonstrate that our approach produces more credible performance under various light conditions or dense traffic.

36 citations


Journal ArticleDOI
Huifeng Wang1, Yun-Fei Wang1, Xiang-Mo Zhao, Wang Guiping1  +2 moreInstitutions (1)
TL;DR: Experiments show that this curve detection algorithm can accurately identify the curve lane-line, provide effective traffic information, make early warning, and it also has certain universality.
Abstract: Curve is the traffic accident-prone area in the traffic system of the structural road. How to effectively detect the lane-line and timely give the traffic information ahead for drivers is a difficult point for the assisted safe driving. The traditional lane detection technology is not very applicable in the curved road conditions. Thus, a curve detection algorithm which is based on straight-curve model is proposed in this paper and this method has good applicability for most curve road conditions. First, the method divides the road image into the region of interest and the road background region by analyzing the basic characteristics of the road image. The region of interest is further divided into the straight region and the curve region. At the same time, the straight-curve mathematical model is established. The mathematical equation of the straight model is obtained by using the improved Hough transform. The polynomial curve model is established according to the continuity of the road lane-line and the tangent relationship between the straight model and the curve model. Then, the parameters of the curve model equation are solved by the curve fitting method. Finally, the detection and identification of the straight and the curve are realized respectively and the road lane-line is reconstructed. Experiments show that this method can accurately identify the curve lane-line, provide effective traffic information, make early warning, and it also has certain universality.

30 citations


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