A Road Environment Prediction System for Intelligent Vehicle

The road environment prediction is an essential task for intelligent vehicle. In this study, we provide a flexible system that focuses on freespace detection and road environment prediction to host vehicle. The hardware of this system includes two parts: a binocular camera and a low-power mobile platform, which is flexible and portable for a variety of intelligent vehicle. We put forward a multiscale stereo matching algorithm to reduce the computing cost of the hardware unit. Based on disparity space and points cloud, we propose a weighted probability grid map to detect freespace region and a state model to describe the road environment. The experiments show that the proposed system is accurate and robust, which indicates that this technique is fully competent for road environment prediction for intelligent vehicle.

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Research on Stereo Matching Algorithm in Intelligent Vehicle Application

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.678.35 ◽

2014 ◽

Vol 678 ◽

pp. 35-38 ◽

Cited By ~ 1

Author(s):

Peng He ◽

Feng Gao

Keyword(s):

Stereo Vision ◽

Stereo Matching ◽

Driver Assistance System ◽

Experimental Results ◽

Assistance System ◽

Intelligent Vehicle ◽

Driver Assistance ◽

Matching Algorithm ◽

Environment Perception ◽

Vehicle Technologies

Perception of environment in front of driving vehicle is a core investigation theme of intelligent vehicle technologies aiming to increase safety, convenience and efficiency of driving. Using stereo vision for environment perception is a hot technology. This paper developed an algorithm for stereo matching in intelligent vehicle application. The experimental results indicate that this algorithm is effective. Furthermore, this algorithm paves the way for the implementation of automotive driver assistance system.

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Dense stereo matching by region growing based on disparity space sampling

Journal of Computer Applications ◽

10.3724/sp.j.1087.2009.02690 ◽

2009 ◽

Vol 29 (10) ◽

pp. 2690-2692

Author(s):

Bao-hai YANG ◽

Xiao-li LIU ◽

Dai-feng ZHA

Keyword(s):

Stereo Matching ◽

Region Growing ◽

Dense Stereo Matching ◽

Disparity Space

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Towards an Embedded Stereo Matching Algorithm Based on Multiple Correlation Windows

Proceedings of the 13th International Conference on Distributed Smart Cameras ◽

10.1145/3349801.3357128 ◽

2019 ◽

Author(s):

M. A. Palacios-Ramos ◽

H. D. Vázquez-Delgado ◽

Abiel Aguilar-González ◽

M. Pérez-Patricio

Keyword(s):

Stereo Matching ◽

Multiple Correlation ◽

Matching Algorithm

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Matching Large Baseline Oblique Stereo Images Using an End-to-End Convolutional Neural Network

Remote Sensing ◽

10.3390/rs13020274 ◽

2021 ◽

Vol 13 (2) ◽

pp. 274

Author(s):

Guobiao Yao ◽

Alper Yilmaz ◽

Li Zhang ◽

Fei Meng ◽

Haibin Ai ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Stereo Matching ◽

Least Square ◽

Affine Invariant ◽

Stereo Images ◽

Distance Ratio ◽

Matching Algorithm ◽

End To End

The available stereo matching algorithms produce large number of false positive matches or only produce a few true-positives across oblique stereo images with large baseline. This undesired result happens due to the complex perspective deformation and radiometric distortion across the images. To address this problem, we propose a novel affine invariant feature matching algorithm with subpixel accuracy based on an end-to-end convolutional neural network (CNN). In our method, we adopt and modify a Hessian affine network, which we refer to as IHesAffNet, to obtain affine invariant Hessian regions using deep learning framework. To improve the correlation between corresponding features, we introduce an empirical weighted loss function (EWLF) based on the negative samples using K nearest neighbors, and then generate deep learning-based descriptors with high discrimination that is realized with our multiple hard network structure (MTHardNets). Following this step, the conjugate features are produced by using the Euclidean distance ratio as the matching metric, and the accuracy of matches are optimized through the deep learning transform based least square matching (DLT-LSM). Finally, experiments on Large baseline oblique stereo images acquired by ground close-range and unmanned aerial vehicle (UAV) verify the effectiveness of the proposed approach, and comprehensive comparisons demonstrate that our matching algorithm outperforms the state-of-art methods in terms of accuracy, distribution and correct ratio. The main contributions of this article are: (i) our proposed MTHardNets can generate high quality descriptors; and (ii) the IHesAffNet can produce substantial affine invariant corresponding features with reliable transform parameters.

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