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This study describes a method for using a camera to automatically recognize the speed limits on speed-limit signs. This method consists of the following three processes: first (1) a method of detecting the speed-limit signs with a machine learning method utilizing the local binary pattern (LBP) feature quantities as information helpful for identification, then (2) an image processing method using...
Vehicle detection is the core function in any Driver Assistant System. Besides the challenge in various environmental conditions, the limitation in execution time and computing power is also critical. This paper proposes a shadow detection step that aims at recognizing the shadow part of the train in various environments (including very tough cases) to accelerate the detection process. We propose...
In this paper, we propose an intelligent auto-dipping system that will be placed on the dashboard of the vehicle. The moment it detects headlights of an oncoming vehicle, the high beam of host vehicle will be dipped automatically, with a flash of dipper to signal the oncoming vehicle. The system will be most effective when every vehicle has this auto-dipper installed. This system would help prevent...
Advanced driver assistance systems rely on the availability of robust information on the driving situation and the driver's needs and intentions to operate reasonably and safely. For this, they have to be enabled to identify and assess both the driving situation and the driver's intentions on the basis of features that can be measured by the vehicle. In case of the prediction of lane change maneuvers...
For a safety critical task like driving, it is very important for the driver to be vigilant at all times. In this study, we explore a driver drowsiness monitoring and early warning system, which uses machine learning techniques based on vehicle telemetry data. The proposed system can ensure safe driving by real time monitoring of driving pattern. This proves to be a very cost effective technique over...
The traffic sign detection and recognition is an integral part of Advanced Driver Assistance System (ADAS). Traffic signs provide information about the traffic rules, road conditions and route directions and assist the drivers for better and safe driving. Traffic sign detection and recognition system has two main stages: The first stage involves the traffic sign localization and the second stage classifies...
Lane departure and forward collision detection plays an important role in autonomous driving and commercial driver-assistance systems. This paper presents an integrative approach to vision-based lane departure detection which aims to be as simple as possible to enable the real-time computation while being able to adapt to a variety of highway and urban scenarios on different weather conditions. In...
Several vehicle detection methods in urban traffic scenes, such as vehicle detection method based on symmetrical features, vehicle detection method based on license plate, vehicle detection method based on Gabor features and Support Vector Machines (SVM), and vehicle detection method based on Haar-like features and AdaBoost classifier, are comparatively used in this paper. The theoretical analysis...
Localization is considered as a key capability for autonomous vehicles act in urban environments. Though have been proved to be able to perform convictive results, localization methods using neither laser scanners nor vision sensors could achieve the goal about balancing between accuracy and cost. In this paper, an occupancy grid based localization framework is presented in order to obtain a precise...
In recent years, there has been an interest in detailed monitoring of road traffic, particularly in intersections, in order to obtain a statistical model of the flow of vehicles through them. These models aid in the optimization of traffic management and allow for smarter transportation systems. While conventional methods sensors at each of the intersections entrances/exits allow for counting, are...
Although many algorithms have been proposed for the camera-based detection of road features (such as road markings, curbstones and road borders), truly contextual or relational information between the detections is rarely used. This is all the more surprising, since a lot of potential remains unused, regarding outlier rejection or compensating detection failures, multiple detections, misclassification...
Existing approaches to object detection address the generation of object hypotheses by extracting several cues in natural and automotive images, relying on objects with sufficiently high resolution. Very little to almost no approaches, however, address the generation of hypothesis of very small or distant objects in images such as on motorways. Here, we propose a simple yet effective approach to generating...
The Highly Automated Driving Map (HAD Map), is an essential and significant research topic in automated driving. In the meanwhile, the corrugated beam guardrail, known as one kind of the traffic crash barrier, is one of the most important elements in the HAD Map. Our novel contribution in this paper is proposing a method to detect corrugated beam guardrail automatically from mobile laser scanning...
In this paper we present a new lane markers detection and estimation algorithm aiming to improve lane detection methods. We first estimate the area of lane marking using the profile of the lane estimation in a confidence map. After that a fitting method is applied to improve the lane marker detection accuracy. To track our lane markers over time and make the association between two iteration, we use...
An extensive, precise and robust recognition and modeling of the environment is a key factor for next generations of Advanced Driver Assistance Systems and development of autonomous vehicles. In this paper, a real-time approach for the perception of multiple lanes on highways is proposed. Lane markings detected by camera systems and observations of other traffic participants provide the input data...
This paper presents a new approach for pedestrian detection in the context of Driver Assistance Systems (DAS). Given a camera with known intrinsic parameters, a flexible online calibration scheme that explores the expected road geometry is used to obtain the extrinsic parameters. With the full camera parameters, the expected geometry and size of a standing person is used to customize a baseline pedestrian...
Accurate detection and localization of vehicles in aerial images has a wide range of applications including urban planning, military reconnaissance, visual surveillance, and realtime traffic management. Automated detection of vehicles in aerial imagery is a challenging task, due to the density of vehicles on the road, the complexity of the surrounding environment in urban areas, and low spatial resolution...
We extract 3D curb from video sequence, using a single camera equipped with fish-eye lens and located at the front/rear of the vehicle. The challenge in extracting curbs from images lies in their small size and their lack of texture. We show that by appropriately exploiting appearance features, 3D geometry, and temporal information, one can reliably detect and localize the curbs in the 3D scene. The...
In this paper, we propose the dense disparity map-based pedestrian detection method for intelligent vehicle. The dense disparity map is utilized to improve the pedestrian detection performance. Our method consists of several steps namely, obstacle area detection using road feature information and column detection, pedestrian area detection using dense disparity map-based segmentation, and pedestrian...
We present an efficient approach to lane and pedestrian detection by processing sequential images from a camera attached to a moving vehicle. The left and right lines of the current lane are detected by finding high intensity pixels along multiple horizontal scan lines and connecting the detected pixel points. Line positions are predicted by tracking in order to increase detection credibility while...
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