• Journal of Digital Convergence
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• v.17 no.11
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• pp.207-213
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• 2019

The fuel-cut coast-down driving mode is activated when the acceleration pedal is released with transmission gear engaged, and it's a default function for electronic-controlled engine of vehicles. The fuel economy becomes better because fuel injection stops during fuel-cut driving mode. A fuel-cut detection method is suggested in the study and it's based on the speed, acceleration and road gradient data from GPS sensor. It detects fuel-cut driving mode by comparing calculated acceleration and realtime acceleration value. The one is estimated with driving resistance in the condition of fuel-cut driving and the other is from GPS sensor. The detection accuracy is about 80% when the method is verified with road driving data. The result is estimated with 9,600 data set of vehicle speed, acceleration, fuel consumption and road gradient from test driving on the road of 12km during 16 minutes, and the road slope is rather high. It's easy to detect fuel-cut without injector signal obtained by connecting wire. The detection error is from the fact that the variation range of speed, acceleration and road gradient data, used for road resistance force, is larger than the value of fuel consumption data.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.5
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• pp.550-557
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• 1999

This paper describes a road-following controller using the proposed neural network for autonomous vehicle. Road-following with visual sensor like camera requires intelligent control algorithm because analysis of relation from road image to steering control is complex. The proposed neural network, relative similarity modular network(RSMN), is composed of some learning networks and a partitioniing network. The partitioning network divides input space into multiple sections by similarity of input data. Because divided section has simlar input patterns, RSMN can learn nonlinear relation such as road-following with visual control easily. Visual control uses two criteria on road image from camera; one is position of vanishing point of road, the other is slope of vanishing line of road. The controller using neural network has input of two criteria and output of steering angle. To confirm performance of the proposed neural network controller, a software is developed to simulate vehicle dynamics, camera image generation, visual control, and road-following. Also, prototype autonomous electric vehicle is developed, and usefulness of the controller is verified by physical driving test.

• International Journal of Highway Engineering
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• v.15 no.1
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• pp.155-161
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• 2013

PURPOSES: This study is to develop a road traffic sign recognition and automatic positioning for road facility management. METHODS: In this study, we installed the GPS, IMU, DMI, camera, laser sensor on the van and surveyed the car position, fore-sight image, point cloud of traffic signs. To insert automatic position of traffic sign, the automatic traffic sign recognition S/W developed and it can log the traffic sign type and approximate position, this study suggests a methodology to transform the laser point-cloud to the map coordinate system with the 3D axis rotation algorithm. RESULTS: Result show that on a clear day, traffic sign recognition ratio is 92.98%, and on cloudy day recognition ratio is 80.58%. To insert exact traffic sign position. This study examined the point difference with the road surveying results. The result RMSE is 0.227m and average is 1.51m which is the GPS positioning error. Including these error we can insert the traffic sign position within 1.51m CONCLUSIONS: As a result of this study, we can automatically survey the traffic sign type, position data of the traffic sign position error and analysis the road safety, speed limit consistency, which can be used in traffic sign DB.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.8
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• pp.633-642
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• 2016

It is important for advanced active safety systems and autonomous driving cars to get the accurate estimates of the nearby vehicles in order to increase their safety and performance. This paper proposes a sensor fusion method for radar and vision sensors to accurately estimate the state of the preceding vehicles. In particular, we performed a study on compensating for the lateral state error on automotive radar sensors by using a vision sensor. The proposed method is based on the Interactive Multiple Model(IMM) algorithm, which stochastically integrates the multiple Kalman Filters with the multiple models depending on lateral-compensation mode and radar-single sensor mode. In addition, a Probabilistic Data Association Filter(PDAF) is utilized as a data association method to improve the reliability of the estimates under a cluttered radar environment. A two-step correction method is used in the Kalman filter, which efficiently associates both the radar and vision measurements into single state estimates. Finally, the proposed method is validated through off-line simulations using measurements obtained from a field test in an actual road environment.

• Journal of Korea Multimedia Society
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• v.19 no.8
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• pp.1505-1515
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• 2016

The age of autonomous vehicles has come according to development of high performance sensing and artificial intelligence technologies. And importance of the vehicle's surrounding environment sensing and observation is increasing accordingly because of its stability and control efficiency. In this paper we propose an integrated platform for efficient networking, analysis and monitoring of multiple sensing data on the vehicle that are equiped with various automotive sensors such as GPS, weather radar, automotive radar, temperature and humidity sensors. From simulation results, we could see that the proposed platform could perform realtime analysis and monitoring of various sensing data that were observed from the vehicle sensors. And we expect that our system can support drivers or autonomous vehicles to recognize optimally various sudden or danger driving environments on the road.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.107.2-107
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• 2002

The objective of this study is to propose a lane changing and keeping method on a curved road for an automatic guidance of a vehicle. It is well known that the speed control of a vehicle in a curved road is essential in terms of vehicle stability and passenger safety because centrifugal force makes a vehicle to be on out of lane. And it is also natural to avoid the collision with other cars or obstructions with keeping the stability and drivability. The vehicle pose and the road curvature were calculated by geometrically fusing sensor data from camera image, tachometer and steering wheel encoder though the Perception Net in which not only the state variables, but also the corresponding uncer...

• Management & Information Systems Review
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• v.4
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• pp.257-284
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• 2000

According as the drivers increase who have their cars, the comprehensive studies on the automobile for the traffic safety have been raised as the important problems. Visual Recognition System for radio-controled driving is a part of the sensor processor of Unmanned Autonomous Vehicle System. When a driver drives his car on an unknown highway or general road, it produces a model from the successively inputted road traffic information. The suggested Recognition System of the Road Traffic Safety Information Board is to recognize and distinguish automatically a Road Traffic Safety Information Board as one of road traffic information. The whole processes of Recognition System of the Road Traffic Safety Information Board suggested in this study are as follows. We took the photographs of Road Traffic Safety Information Board with a digital camera in order to get an image and normalize bitmap image file with a size of $200{\times}200$ byte with Photo Shop 5.0. The existing True Color is made up the color data of sixteen million kinds. We changed it with 256 Color, because it has large capacity, and spend much time on calculating. We have practiced works of 30 times with erosion and dilation algorithm to remove unnecessary images. We drawing out original image with the Region Splitting Technique as a kind of segmentation. We made three kinds of grouping(Attention Information Board, Prohibit Information Board, and Introduction Information Board) by RYB( Red, Yellow, Blue) color segmentation. We minimized the image size of board, direction, and the influence of rounding. We also minimized the Influence according to position. and the brightness of light and darkness with Eigen Vector and Eigen Value. The data sampling this feature value appeared after building the learning Code Book Database. The suggested Recognition System of the Road Traffic Safety Information Board firstly distinguished three kinds of groups in the database of learning Code Book, and suggested in order to recognize after comparing and judging the board want to recognize within the same group with Nearest Neighborhood Decision Making.

• International Journal of Highway Engineering
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• v.10 no.3
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• pp.79-86
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• 2008

This paper proposed the appropriate installation angle of skewed sensors for measuring vehicle wandering data, which are collected to figure out the location of dynamic weight of a moving vehicle on roadways. We developed a device using tape-switch sensors and a computer program and collected vehicle wandering data with the device and probe vehicles. As a result, the steeper the skewed sensor was installed, the lower the error was shown. However, we could not collect proper data when a skewed sensor was set up higher than $30^{\circ}$ due to tandem axle. Therefore, this study suggested the appropriate angle of skewed sensors as a degree of $20^{\circ}$ to $25^{\circ}$ for gathering wandering data.

• Journal of Auto-vehicle Safety Association
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• v.9 no.4
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• pp.32-37
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• 2017

For automated vehicles, the integrity and fault tolerance of environment perception sensor have been an important issue. This paper presents radar, vision, lidar(laser radar) fusion-based fault detection algorithm for autonomous vehicles. In this paper, characteristics of each sensor are shown. And the error of states of moving targets estimated by each sensor is analyzed to present the method to detect fault of environment sensors by characteristic of this error. Each estimation of moving targets isperformed by EKF/IMM method. To guarantee the reliability of fault detection algorithm of environment sensor, various driving data in several types of road is analyzed.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2007.04a
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• pp.825-830
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• 2007

For the design and maintenance of highways and road structures, the statistical data are needed for the vehicle, especially heavy truck crossing. So far, static weighing has been used but it needs fixed station, crews, and it takes a lot of time. Also truck mix and headway distances cannot be obtained. Weigh-In-Motion system uses the sensor as a weighing scale and collects the axle weights, axle distances, vehicle types and etc. without stopping or slowing down the vehicle. Objectives of the study is make a determination of WIM Sensor for Implementation of U-Overloaded Vehicle Regulation System.