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Author

Hong Wang

Bio: Hong Wang is an academic researcher from Tsinghua University. The author has contributed to research in topics: RANSAC & Edge detection. The author has an hindex of 10, co-authored 16 publications receiving 697 citations.

Papers
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Journal ArticleDOI
TL;DR: This paper presents a road-area detection algorithm based on color images that can overcome basic problems due to inaccuracies in edge detection based on the intensity image alone and due to the computational complexity of segmentation algorithms based oncolor images.
Abstract: Road detection is a key issue for autonomous driving in urban traffic. In this paper, after a brief overview of existing methods, we present a road-area detection algorithm based on color images. This algorithm is composed of two modules: boundaries are first estimated based on the intensity image and road areas are subsequently detected based on the full color image. In the first module, an edge image of the scene is analyzed to obtain the candidates for the left and right road borders and to delimit the area that will subsequently be used to compute the mean and variance of the Gaussian distribution, assumed to be obeyed by the color components of road surfaces. The second module effectively extracts the road area and reinforces boundaries that most appropriately fit the road-extraction result. The combination of these modules can overcome basic problems due to inaccuracies in edge detection based on the intensity image alone and due to the computational complexity of segmentation algorithms based on color images. Experimental results on real road scenes have substantiated the effectiveness of the proposed method.

363 citations

Proceedings ArticleDOI
11 Sep 2006
TL;DR: A real-time lane detection algorithm based on a hyperbola-pair lane boundary model that is able to make full use of road boundaries with existence of partial occlusion.
Abstract: In this paper; we propose a real-time lane detection algorithm based on a hyperbola-pair lane boundary model In stead of modeling each road boundary separately, we propose a model to describe the road boundary as two parallel hyperbolas on ground plane By fitting points on pair road boundaries into this model, our method is able to make full use of road boundaries with existence of partial occlusion Experiment in many different conditions, including various weather and road, demonstrates its high performance and accuracy

78 citations

Proceedings ArticleDOI
Dong An1, Hong Wang1
15 Jun 2004
TL;DR: A new radar-based obstacle avoidance method for mobile robots, based on the combination and improvement of PFM and VFH+ method, is presented, which permits the detection of unknown obstacles and avoids collisions in real time while simultaneously steering the mobile robot towards the target.
Abstract: Real-time obstacle avoidance is one of the key issues in successful application of mobile robot systems. Some new real-time obstacle avoidance methods for mobile robots have already been developed and implemented. This paper introduces the developments in this area, and summarizes Potential Field method (PFM) and the Enhanced Vector Field Histogram method (VFH+), which are widely used for autonomous mobile robot obstacle avoidance. After a brief introduction of laser measurement system (LMS), a new radar-based obstacle avoidance method for mobile robots, based on the combination and improvement of PFM and VFH+ method, is presented. This method, named the Vector Polar Histogram method (VPH), uses laser radar to detect obstacles and takes the physical meaning of the vector polar into account, so as to get the best avoidance choice. The new method permits the detection of unknown obstacles and avoids collisions in real time while simultaneously steering the mobile robot towards the target.

56 citations

Proceedings ArticleDOI
09 Oct 2006
TL;DR: A real-time lane detection algorithm based on a hyperbola-pair lane boundary model and an improved RANSAC paradigm to improve the accuracy and robustness of fitting the points on the boundaries into the model is proposed.
Abstract: In this paper, we propose a real-time lane detection algorithm based on a hyperbola-pair lane boundary model and an improved RANSAC paradigm. Instead of modeling each road boundary separately, we propose a model to describe the road boundary as a pair of parallel hyperbolas on the ground plane. A fuzzy measurement is introduced into the RANSAC paradigm to improve the accuracy and robustness of fitting the points on the boundaries into the model. Our method is able to deal with existence of partial occlusion, other traffic participants and markings, etc. Experiment in many different conditions, including various conditions of illumination, weather and road, demonstrates its high performance and accuracy

47 citations

Journal ArticleDOI
TL;DR: This paper presents an efficient method to integrate spatial and temporal constraints for detecting and tracking obstacles in urban environments, which does not consider the urban roads as rigid planes, but as quasi-planes, whose normal vectors have orientation constraints.
Abstract: Obstacle detection is an essential capability for the safe guidance of autonomous vehicles, especially in urban environments. This paper presents an efficient method to integrate spatial and temporal constraints for detecting and tracking obstacles in urban environments. In order to enhance the reliability of the obstacle detection task, we do not consider the urban roads as rigid planes, but as quasi-planes, whose normal vectors have orientation constraints. Under this flexible road model, we propose a fast, robust stereovision based obstacle detection method. A watershed transformation is employed for obstacle segmentation in dense traffic conditions, even with partial occlusions, in urban environments. Finally a UKF (Unscented Kalman filter) is applied to estimate the obstacles parameters under a nonlinear observation model. To avoid the difficulty of the computation in metric space, the whole detection process is performed in the disparity image. Various experimental results are presented, showing the advantages of this method.

39 citations


Cited by
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Proceedings ArticleDOI
04 Jun 2008
TL;DR: In this paper, a robust and real-time approach to lane marker detection in urban streets is presented, which is based on generating a top view of the road, filtering using selective oriented Gaussian filters, using RANSAC line fitting to give initial guesses to a new and fast RANAC algorithm for fitting Bezier Splines, which was then followed by a post-processing step.
Abstract: We present a robust and real time approach to lane marker detection in urban streets. It is based on generating a top view of the road, filtering using selective oriented Gaussian filters, using RANSAC line fitting to give initial guesses to a new and fast RANSAC algorithm for fitting Bezier Splines, which is then followed by a post-processing step. Our algorithm can detect all lanes in still images of the street in various conditions, while operating at a rate of 50 Hz and achieving comparable results to previous techniques.

672 citations

Proceedings ArticleDOI
TL;DR: A robust and real time approach to lane marker detection in urban streets based on generating a top view of the road, filtering using selective oriented Gaussian filters, using RANSAC line fitting to give initial guesses to a new and fast RansAC algorithm for fitting Bezier Splines, which is then followed by a post-processing step.
Abstract: We present a robust and real time approach to lane marker detection in urban streets. It is based on generating a top view of the road, filtering using selective oriented Gaussian filters, using RANSAC line fitting to give initial guesses to a new and fast RANSAC algorithm for fitting Bezier Splines, which is then followed by a post-processing step. Our algorithm can detect all lanes in still images of the street in various conditions, while operating at a rate of 50 Hz and achieving comparable results to previous techniques.

638 citations

Journal ArticleDOI
TL;DR: This paper decomposes the road detection process into two steps: the estimation of the vanishing point associated with the main (straight) part of the road, followed by the segmentation of the corresponding road area based upon the detected vanishing point.
Abstract: Given a single image of an arbitrary road, that may not be well-paved, or have clearly delineated edges, or some a priori known color or texture distribution, is it possible for a computer to find this road? This paper addresses this question by decomposing the road detection process into two steps: the estimation of the vanishing point associated with the main (straight) part of the road, followed by the segmentation of the corresponding road area based upon the detected vanishing point. The main technical contributions of the proposed approach are a novel adaptive soft voting scheme based upon a local voting region using high-confidence voters, whose texture orientations are computed using Gabor filters, and a new vanishing-point-constrained edge detection technique for detecting road boundaries. The proposed method has been implemented, and experiments with 1003 general road images demonstrate that it is effective at detecting road regions in challenging conditions.

401 citations

Journal ArticleDOI
TL;DR: In this article, a shadow-invariant feature space combined with a model-based classifier is used to detect the free road surface ahead of the ego-vehicle.
Abstract: By using an onboard camera, it is possible to detect the free road surface ahead of the ego-vehicle. Road detection is of high relevance for autonomous driving, road departure warning, and supporting driver-assistance systems such as vehicle and pedestrian detection. The key for vision-based road detection is the ability to classify image pixels as belonging or not to the road surface. Identifying road pixels is a major challenge due to the intraclass variability caused by lighting conditions. A particularly difficult scenario appears when the road surface has both shadowed and nonshadowed areas. Accordingly, we propose a novel approach to vision-based road detection that is robust to shadows. The novelty of our approach relies on using a shadow-invariant feature space combined with a model-based classifier. The model is built online to improve the adaptability of the algorithm to the current lighting and the presence of other vehicles in the scene. The proposed algorithm works in still images and does not depend on either road shape or temporal restrictions. Quantitative and qualitative experiments on real-world road sequences with heavy traffic and shadows show that the method is robust to shadows and lighting variations. Moreover, the proposed method provides the highest performance when compared with hue-saturation-intensity (HSI)-based algorithms.

327 citations

Journal ArticleDOI
TL;DR: A novel real-time optimal-drivable-region and lane detection system for autonomous driving based on the fusion of light detection and ranging (LIDAR) and vision data and an optimal selection strategy for detecting the best drivable region is presented.
Abstract: Autonomous vehicle navigation is challenging since various types of road scenarios in real urban environments have to be considered, particularly when only perception sensors are used, without position information. This paper presents a novel real-time optimal-drivable-region and lane detection system for autonomous driving based on the fusion of light detection and ranging (LIDAR) and vision data. Our system uses a multisensory scheme to cover the most drivable areas in front of a vehicle. We propose a feature-level fusion method for the LIDAR and vision data and an optimal selection strategy for detecting the best drivable region. Then, a conditional lane detection algorithm is selectively executed depending on the automatic classification of the optimal drivable region. Our system successfully handles both structured and unstructured roads. The results of several experiments are provided to demonstrate the reliability, effectiveness, and robustness of the system.

318 citations