Stereovision-based 3D lane detection system: a model driven approach

Benmansour, N.; Labayrade, Raphaël; Didier, A.; Glaser, Sébastien

doi:10.1109/itsc.2008.4732566

Cited by 13 publications

(11 citation statements)

References 11 publications

(18 reference statements)

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“…The lane detection and tracking is performed using single camera while the obstacle detection is carried out based on stereo vision. In [35], a 3D lane detection method is given based on stereo vision, where the parameters of the 3D lane, such as width, horizontal and vertical curvature, roll, pitch, and yaw angles, are estimated. Similarly, Nedevschi et al propose a stereo vision based lane detection and tracking scheme, where the lane is modeled as a 3D surface, defined by the vertical and horizontal clothoid curves, the lane width and the roll angle.…”

Section: Related Workmentioning

confidence: 99%

When Dijkstra Meets Vanishing Point: A Stereo Vision Approach for Road Detection

Zhang

Yang

et al. 2018

IEEE Trans. on Image Process.

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In this paper, we propose a vanishing-point constrained Dijkstra road model for road detection in a stereo-vision paradigm. First, the stereo-camera is used to generate the u- and v-disparity maps of road image, from which the horizon can be extracted. With the horizon and ground region constraints, we can robustly locate the vanishing point of road region. Second, a weighted graph is constructed using all pixels of the image, and the detected vanishing point is treated as the source node of the graph. By computing a vanishing-point constrained Dijkstra minimum-cost map, where both disparity and gradient of gray image are used to calculate cost between two neighbor pixels, the problem of detecting road borders in image is transformed into that of finding two shortest paths that originate from the vanishing point to two pixels in the last row of image. The proposed approach has been implemented and tested over 2600 grayscale images of different road scenes in the KITTI data set. The experimental results demonstrate that this training-free approach can detect horizon, vanishing point, and road regions very accurately and robustly. It can achieve promising performance.

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Section: Related Workmentioning

confidence: 99%

When Dijkstra Meets Vanishing Point: A Stereo Vision Approach for Road Detection

Zhang

Yang

et al. 2018

IEEE Trans. on Image Process.

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“…In this chapter, we only concentrate on the 2D models which assume a flat road plane. Detailed studies of 3D models can be found in [2,8,23].…”

Section: Lane Modellingmentioning

confidence: 99%

Computational Intelligence in Automotive Applications

Wang

Dahnoun

Achim

2013

Advances in Intelligent Signal Processing and Data Mining

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Abstract. In this chapter, we discuss one of the most popular machine vision applications in the automotive industry: lane detection and tracking. Model-based lane detection algorithms can be separated into lane modeling, feature extraction and model parameter estimation. Each of these steps is discussed in detail with examples and results. A recently proposed lane feature extraction approach, which is called the Global Lane Feature Refinement Algorithm (GLFRA), is also introduced. It provides a generalized framework to significantly improve various types of gradient-based lane feature maps by utilizing the global shape information and subsequently improves the parameter estimation and the tracking performance. Another important aspect of this application lies in the tracking stage. We compare the performances of three different types of particle filters (the sampling importance resampling particle filter, the Gaussian particle filter and the Gaussian sum particles filter) quantitatively and provide insightful result analysis and suggestions. Furthermore, the influence of feature maps on the tracking performance is also investigated.

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“…A lane detection component that is based on contextual cues was developed in [12]. There are also stereovision based approaches that extend the 2D road model to the 3D world [13].…”

Section: Introductionmentioning

confidence: 99%

A stereovision based approach for detecting and tracking lane and forward obstacles on mobile devices

Petrovai

Danescu

Nedevschi

2015

2015 IEEE Intelligent Vehicles Symposium (IV)

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This paper presents SmartCoDrive, an Android application which performs driving assistance functions: 3D lane detection and tracking, forward obstacle detection, obstacle tracking. With this mobile application we wish to increase the adoption rate of driving assistance systems and to provide a viable and cheap solution for every driver, that will be able to use his own tablet or smartphone as a personal driving assistant. The mobile application is deployed on a tablet equipped with dual back-facing cameras. The visual information from the two cameras, along with the data received from the Controller Area Network bus of the vehicle enable a thorough understanding of the 3D environment. First, we develop the sparse 3D reconstruction algorithm. Then, using monocular vision we perform lane markings detection. Obstacle detection is done by combining the superpixel segmentation with 3D information and the tracking algorithm is based on the Kalman Filter. Since the processing capabilities of the mobile platforms are limited, different optimizations are carried out in order to obtain a real-time implementation. The Android application may be used in urban traffic that is characterized by low-speed and short-medium distances to obstacles.

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Stereovision-based 3D lane detection system: a model driven approach

Cited by 13 publications

References 11 publications

When Dijkstra Meets Vanishing Point: A Stereo Vision Approach for Road Detection

When Dijkstra Meets Vanishing Point: A Stereo Vision Approach for Road Detection

Computational Intelligence in Automotive Applications

A stereovision based approach for detecting and tracking lane and forward obstacles on mobile devices

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