• Journal of the Institute of Convergence Signal Processing
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• v.22 no.2
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• pp.85-90
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• 2021

A ADAS(Advanced Driver Assistance System) for the safe driving is an important area in autonumous car. Specially, a ADAS software using an image sensors attached in previous car is low in building cost, and utilizes for various purpose. A algorithm for detecting the break-lamp from the tail-lamp of preceding vehicle is proposed in this paper. This method can perceive the driving condition of preceding vehicle. Proposed method uses the YOLO techinicque that has a excellent performance in object tracing from real scene, and extracts the intensity variable region of break-lamp from HSV image of detected vehicle ROI(Region Of Interest). After detecting the candidate region of break-lamp, each isolated region is labeled. The break-lamp region is detected finally by using the proposed selective-attention model that percieves the shape-similarity of labeled candidate region. In order to evaluate the performance of the preceding vehicle break-lamp detection system implemented in this paper, we applied our system to the various driving images. As a results, implemented system showed successful results.

• Journal of Korea Multimedia Society
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• v.19 no.6
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• pp.1003-1013
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• 2016

This paper proposes a framework of superpixel-based vehicle detection method using plane normal vector in disparity space. We utilize two common factors for detecting vehicles: Hypothesis Generation (HG) and Hypothesis Verification (HV). At the stage of HG, we set the regions of interest (ROI) by estimating the lane, and track them to reduce computational cost of the overall processes. The image is then divided into compact superpixels, each of which is viewed as a plane composed of the normal vector in disparity space. After that, the representative normal vector is computed at a superpixel-level, which alleviates the well-known problems of conventional color-based and depth-based approaches. Based on the assumption that the central-bottom of the input image is always on the navigable region, the road and obstacle candidates are simultaneously extracted by the plane normal vectors obtained from K-means algorithm. At the stage of HV, the separated obstacle candidates are verified by employing HOG and SVM as for a feature and classifying function, respectively. To achieve this, we trained SVM classifier by HOG features of KITTI training dataset. The experimental results demonstrate that the proposed vehicle detection system outperforms the conventional HOG-based methods qualitatively and quantitatively.

• Journal of Auto-vehicle Safety Association
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• v.12 no.2
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• pp.33-39
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• 2020

This paper presents an Around View Monitoring (AVM) stop-line detection based longitudinal position correction algorithm for automated driving on urban roads. Poor positioning accuracy of low-cost GPS has many problems for precise path tracking. Therefore, this study aims to improve the longitudinal positioning accuracy of low-cost GPS. The algorithm has three main processes. The first process is a stop-line detection. In this process, the stop-line is detected using Hough Transform from the AVM camera. The second process is a map matching. In the map matching process, to find the corrected vehicle position, the detected line is matched to the stop-line of the HD map using the Iterative Closest Point (ICP) method. Third, longitudinal position of low-cost GPS is updated using a corrected vehicle position with Kalman Filter. The proposed algorithm is implemented in the Robot Operating System (ROS) environment and verified on the actual urban road driving data. Compared to low-cost GPS only, Test results show the longitudinal localization performance was improved.

• 전자공학회논문지 IE
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• v.49 no.2
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• pp.30-39
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• 2012

An effective drowsy driver detection system is needed, because the probability of accident is high for drowsy driving and its severity is high at the time of accident. However, the drowsy driver detection system that uses bio-signals or vision is difficult to be utilized due to high cost. Thus, this paper proposes a drowsy driver detection algorithm by using steering angle sensor, which is attached to the most of vehicles at no additional cost, and vehicle information such as brake switch, throttle position signal, and vehicle speed. The proposed algorithm is based on jerk criterion, which is one of drowsy driver's steering patterns. In this paper, threshold value of each variable is presented and the proposed algorithm is evaluated by using acquired vehicle data from hardware in the loop simulation (HILS) through CAN communication and MATLAB program.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.2
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• pp.155-162
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• 2009

In walking guide robot, a guide vehicle detects an obstacle distribution in the walking space using range sensors, and generates a 3D grid map to map the obstacle information and the tactile display. And the obstacle information is transferred to a blind pedestrian using tactile feedback. Based on the obstacle information a user plans a walking route and controls the guide vehicle. The algorithm for 3D detection of an obstacle distribution and the method of mapping the generated obstacle map and the tactile display device are proposed in this paper. The experiment for the 3D detection of an obstacle distribution using ultrasonic sensors is performed and estimated. The experimental system consisted of ultrasonic sensors and control system. In the experiment, the detection of fixed obstacles on the ground, the moving obstacle, and the detection of down-step are performed. The performance for the 3D detection of an obstacle distribution and space mapping is verified through the experiment.

• Transactions of the Korean Society of Automotive Engineers
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• v.19 no.3
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• pp.16-22
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• 2011

This paper presents the potential of in-vehicle data recorder system for monitoring aggressive driving patterns and providing feedback to drivers on their on road behaviour. This system can detect 10 risky types of drivers' driving patterns such as aggressive lane change, sudden brakes and turns with acceleration etc. Vehicle dynamics simulation and vehicle road test have been performed in order to develop driving pattern recognition algorithms. Recorder systems are installed to 50 buses in a single company. Drivers' driving behaviour are monitored for 1 month. The drivers' risky driving data collected by the system are analyzed. Aggressive lane change in 50km/h below is a cause in overwhelming majority of risky driving pattern.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.120-122
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• 2021

Currently, as demand for cars at home and abroad increases, the number of vehicles is decreasing and the number of vehicles is increasing. This is the main cause of the traffic jam. To solve this problem, it operates a high-ocompancy vehicle (HOV) lane, a multi-passenger vehicle, but many people ignore the conditions of use and use it illegally. Since the police visually judge and crack down on such illegal activities, the accuracy of the crackdown is low and inefficient. In this paper, we propose a system design that enables more efficient detection using imaging techniques using computer vision to solve such problems. By improving the existing vehicle detection method that was studied, the trigger was set in the image so that the detection object can be selected and the image analysis can be conducted intensively on the target. Using the YOLO model, a deep learning object recognition model, we propose a method to utilize the shift amount of the center point rather than judging by the bounding box in the image to obtain real-time object detection and accurate signals.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.8 no.6
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• pp.149-161
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• 2009

Electronic control systems of vehicle are rapidly developed to keep balance of a driver`s safety and the legal, social needs. The driver assistance systems are putted into practical use according to the cost drop in hardware and highly efficient sensor, etc. This study has developed a lane and vehicle detection program using CCD camera. The Risky Driving Analysis Program based on vision systems is developed by combining a risky driving detection algorithm formed in previous study with lane and vehicle detection program suggested in this study. Risky driving detection programs developed in this study with information coming from the vehicle moving data and lane data are useful in efficiently analyzing the cause and effect of risky driving behavior.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.8
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• pp.1026-1031
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• 2021

With the development of global automotive technology and the expansion of market size, demand for vehicles is increasing, which is leading to a decrease in the number of passengers on the road and an increase in the number of vehicles on the road. This causes traffic jams, and in order to solve these problems, the number of illegal vehicles continues to increase. Various technologies are being studied to crack down on these illegal activities. Previously developed systems use trigger equipment to recognize vehicles and photograph vehicles using infrared cameras to detect the number of passengers on board. In this paper, we propose a vehicle occupant detection system with deep learning model techniques without exploiting existing system-applied trigger equipment. The proposed technique proposes a system to detect vehicles by establishing triggers within images and to apply deep learning object recognition models to detect real-time boarding personnel.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1432-1435
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• 1997

This paper proposes a fault detection and isolation metho by combining the parameter estimation method[4] with the observer method[2] to use merits of both methods. To verify the performance of the method proposed some simulations applied to remotely piloted vehicle are performed.