• Journal of Institute of Control, Robotics and Systems
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• v.21 no.2
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• pp.87-94
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• 2015

This paper proposes a situation awareness method based on data fusion and independent objects for autonomous driving in on-road environment. The proposed method, designed to achieve an accurate analysis of driving situations in on-road environment, executes preprocessing tasks that include coordinate transformations, data filtering, and data fusion and independent object based situation assessment to evaluate the collision risks of driving situations and calculate a desired velocity. The method was implemented in an open-source robot operating system called ROS and tested on a closed road with other vehicles. It performed successfully in several scenarios similar to a real road environment.

• Journal of Drive and Control
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• v.16 no.4
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• pp.48-55
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• 2019

The Electronic Stability Program (ESP), a system that improves vehicle safety, also known as YMC (Yaw Motion Controller) or VDC (Vehicle Dynamics Control), is a system that operates in unstable or sudden driving and braking situations. Developing conditions such as unstable or sudden driving and braking situations in a vehicle are very dangerous unless you are an experienced professional driver. Additionally, many repetitive tests are required to collect reliable data, and there are many variables to consider such as changes in the weather, road surface, and tire condition. To overcome this problem, in this paper, hardware and control software such as the ESP controller, vehicle engine, ABS, and TCS module, composed of three control zones, are modeled using MATLAB/SIMULINK, and the vehicle, climate, and road surface. Various environmental variables such as the driving course were modeled and studied for the real-time ESP real-time simulator that can be repeatedly tested under the same conditions.

• Journal of the Korea Society of Computer and Information
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• v.26 no.1
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• pp.93-101
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• 2021

In this paper, a vehicle license plate detection scheme is proposed that uses the spatial attention areas to detect accurately the license plates in various real-road situations. First, the previous WPOD-NET was analyzed, and its detection accuracy is evaluated as lower due to the unnecessary noises in the wide detection candidate areas. To resolve this problem, a vehicle license plate detection model is proposed that uses the candidate area of the license plate as a spatial attention areas. And we compared its performance to that of the WPOD-NET, together with the case of using the optimal spatial attention areas using the ground truth data. The experimental results show that the proposed model has about 20% higher detection accuracy than the original WPOD-NET since the proposed scheme uses tight detection candidate areas.

• IEMEK Journal of Embedded Systems and Applications
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• v.11 no.5
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• pp.313-323
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• 2016

With the development of autonomous vehicle, there has been active research on advanced driver assistance system for road marking detection using vision sensor and 3D Laser scanner. However, vision sensor has the weak points that detection is difficult in situations involving severe illumination variance, such as at night, inside a tunnel or in a shaded area; and that processing time is long because of a large amount of data from both vision sensor and 3D Laser scanner. Accordingly, this paper proposes a road marking detection and classification method using single 2D Laser scanner. This method road marking detection and classification based on accumulation distance data and intensity data acquired through 2D Laser scanner. Experiments using a real autonomous vehicle in a real environment showed that calculation time decreased in comparison with 3D Laser scanner-based method, thus demonstrating the possibility of road marking type classification using single 2D Laser scanner.

• Journal of Auto-vehicle Safety Association
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• v.7 no.2
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• pp.25-31
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• 2015

This paper presents a performance evaluation procedure for advanced emergency braking (AEB) system. To guarantee the performance of AEB system, AEB test scenario should contains various driving conditions which can be occurred in real driving condition. Also, performances of each elements of AEB system, such as sensor, decision, human machine interface (HMI) and control, should be evaluated in various situations. For this, driving conditions, road types, environment, and elements of AEB system were introduced. Test scenario has been designed to represent the real driving condition and to evaluate the safety performance of AEB system in various situations. To confirm that the proposed AEB test scenario is realistic and physically meaningful, vehicle test have been conducted in two cases of proposed AEB test scenario: subject vehicle cut-out scenario and narrow street turn left scenario.

• ETRI Journal
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• v.37 no.5
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• pp.1032-1043
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• 2015

ETRI's Co-Pilot project is aimed at the development of an automated vehicle that cooperates with a driver and interacts with other vehicles on the road while obeying traffic rules without collisions. This paper presents a core block within the Co-Pilot system; the block is named "Co-Pilot agent" and consists of several main modules, such as road map generation, decision-making, and trajectory generation. The road map generation builds road map data to provide enhanced and detailed map data. The decision-making, designed to serve situation assessment and behavior planning, evaluates a collision risk of traffic situations and determines maneuvers to follow a global path as well as to avoid collisions. The trajectory generation generates a trajectory to achieve the given maneuver by the decision-making module. The system is implemented in an open-source robot operating system to provide a reusable, hardware-independent software platform; it is then tested on a closed road with other vehicles in several scenarios similar to real road environments to verify that it works properly for cooperative driving with a driver and automated driving.

• The Journal of the Korea Contents Association
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• v.21 no.12
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• pp.190-201
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• 2021

The variable message sign (VMS) is equipment to improve the efficiency of traffic flow and safety of travel by providing real-time information to road users on traffic, road, weather situation, and traffic control due to construction. The information messages of the letter-based VMS taking up most of the VMS on the general national highways consist of section, travel time in sections, and road control situation. This study devised an improvement plan centered on road users, not road managers-centered existing message-based VMS through an analysis of road users' preference targeting the letter-based VMS operated in the general national highways in the Seoul Metropolitan Area. By presenting the measure for system improvement through which information on unexpected situations that road users prefer the most can be quickly and efficiently provided, this study aims to improve the efficiency of road user-centered VMS traffic information.

• Journal of Auto-vehicle Safety Association
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• v.8 no.4
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• pp.31-37
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• 2016

Since real road experiments have many restrictions, a multi-vehicle traffic simulator can be an effective tool to develop and evaluate fully automated driving systems. This paper presents multi-vehicle environment simulation tool to develop and evaluate motorway automated driving systems. The proposed simulation tool consists of following two main parts: surrounding vehicle model and environment sensor model. The surrounding vehicle model is designed to quickly generate rational complex traffic situations of motorway. The environment sensor model depicts uncertainty of environment sensor. As a result, various traffic situations with uncertainty of environment sensor can be proposed by the multi-vehicle environment simulation tool. An application to automated driving system has been conducted. A lane changing algorithm is evaluated by performance indexes from the multi-vehicle environment simulation tool.

• Journal of Mechanical Science and Technology
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• v.15 no.9
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• pp.1248-1256
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• 2001

In this study, a test bed for vehicle longitudinal control is developed using a chassis dynamometer and real time 3-D graphics. The proposed test bed system consists of a chassis dynamometer on which test vehicle can run longitudinally, a video system that shows virtual driver view, and computers that control the test vehicle and realize the real time 3-D graphics. The purpose of the proposed system is to test vehicle longitudinal control and warning algorithms such as Adaptive Cruise Control(ACC), stop and go systems, and collision warning systems. For acceleration and deceleration situations which only need throttle movements, a vehicle longitudinal spacing control algorithm has been tested on the test bed. The spacing control algorithm has been designed based on sliding mode control and road grade estimation scheme which utilizes the vehicle engine torque map and gear shift information.

• The KIPS Transactions:PartB
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• v.18B no.5
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• pp.255-264
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• 2011

In this paper, we propose a forward vehicle detection algorithm using column detection and bird's-eye view mapping based on stereo vision. The algorithm can detect forward vehicles robustly in real complex traffic situations. The algorithm consists of the three steps, namely road feature-based column detection, bird's-eye view mapping-based obstacle segmentation, obstacle area remerging and vehicle verification. First, we extract a road feature using maximum frequent values in v-disparity map. And we perform a column detection using the road feature as a new criterion. The road feature is more appropriate criterion than the median value because it is not affected by a road traffic situation, for example the changing of obstacle size or the number of obstacles. But there are still multiple obstacles in the obstacle areas. Thus, we perform a bird's-eye view mapping-based obstacle segmentation to divide obstacle accurately. We can segment obstacle easily because a bird's-eye view mapping can represent the position of obstacle on planar plane using depth map and camera information. Additionally, we perform obstacle area remerging processing because a segmented obstacle area may be same obstacle. Finally, we verify the obstacles whether those are vehicles or not using a depth map and gray image. We conduct experiments to prove the vehicle detection performance by applying our algorithm to real complex traffic situations.