• Journal of Positioning, Navigation, and Timing
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• v.9 no.1
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• pp.23-31
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• 2020

It is essential to estimate the vehicle localization for an autonomous safety driving. In particular, since LIDAR provides precise scan data, many studies carried out to estimate the vehicle localization using LIDAR and pre-generated map. The road marking always exists on the road because of provides driving information. Therefore, it is often used for map information. In this paper, we propose to generate the Gaussian mixture map based on road-marking information and localization method using this map. Generally, the probability distributions map stores the single Gaussian distribution for each grid. However, single resolution probability distributions map cannot express complex shapes when grid resolution is large. In addition, when grid resolution is small, map size is bigger and process time is longer. Therefore, it is difficult to apply the road marking. On the other hand, Gaussian mixture distribution can effectively express the road marking by several probability distributions. In this paper, we generate Gaussian mixture map and perform vehicle localization using Gaussian mixture map. Localization performance is analyzed through the experimental result.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.9
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• pp.526-532
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• 2003

This paper describes a recognition method of a road direction for an autonomous vehicle based on magnetic sensors. Using the sensors mounted on a vehicle and the magnetic markers embedded along the center of road, the autonomous vehicle can recognize a road direction and control a steering angle. Using the front lateral deviation of a vehicle and the rear one, the road direction is calculated. The analysis of magnetic field, the acquisition technique of training data, the training method of neural network and the computer simulation are presented. According to the computer simulation, the proposed method is simulated, and its performance is verified. Also, the experimental test is confirmed its reliability.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.274-281
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• 1998

This paper describes a visual control of autonomous vehicle using neural network. Visual control for road-following of autonomous vehicle is based on road image from camera. Road points on image are inputs of controller and vehicle speed and steering angle are outputs of controller using neural network. Simulation study confirmed the visual control of road-following using neural network. For experimental test, autonomous electric vehicle is designed and driving test is realized

• Journal of the Korean Society of Safety
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• v.32 no.6
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• pp.143-149
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• 2017

According to traffic accidents statistics, the number of fatalities, injuries and the rate of increase of traffic accidents have been decreasing over last 5-years. The fatality rate is 1.9 for total accidents but the fatality rate for single vehicle accidents shows a 7.9, which is 4 times greater than the average for all accidents. Single vehicle accidents, usually occur as a vehicle impacts a fixed objects on the roadside as the vehicle runs-off from the road. However, few researches have been conducted considering the accident severity of single vehicle accidents which impact to the fixed objects on the road. The single vehicle accident is directly related to the composition of road cross section, (since it is the required the minimum width of a road for all run-off-the-road vehicles to recover or come to a safe stop). Therefore, this study analyzes the influence of road cross section on traffic accidents to find out the severity of single vehicle accident. To analyze the road elements which are related to the accident severity, the Ordered Probit Model was used. As variables, the element of road cross section such as the radius(m), vertical curve(%), cross sectional grade(%), road width(m). number of climbing lane, median, and curb, were used (as was the 3-years of accidents data). This study found out that cross slope(%), road width(m), and the number of climbing lane are related to the severity of accident. The result of this study could be expected to improve the road safety and to be used as the base data for further road safety research.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2008.11a
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• pp.654-655
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• 2008

In order to investigate the availability of tire airborne noise for vehicle road noise development, We measured the noise in condition of smooth road and coarse road. The correlation coefficient was analyzed using the articulation index of the tire airborne noise and the vehicle road noise. It has been found that the correlation between the tire airborne noise and the vehicle road noise is positively strong.

• Transactions of the Korean Society of Automotive Engineers
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• v.19 no.5
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• pp.82-91
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• 2011

In this paper, a planar model for mechanics of a vehicle/pedestrian collision incorporating road gradient is derived to evaluate the pre-collision speed of vehicle. It takes into account a few physical variables and parameters of popular wrap and forward projection collisions, which include horizontal distance traveled between primary and secondary impacts with the vehicle, launch angle, center-of-gravity height at launch, distance from launch to rest, pedestrian-ground drag factor, the pre-collision vehicle speed and road gradient. The model including road gradient is derived analytically for reconstruction of pedestrian collision accidents, and evaluates the vehicle speed from the pedestrian throw distance. The model coefficients have physical interpretations and are determined through direct calculation. This work shows that the road gradient has a significant effect on the evaluation of the vehicle speed and must be considered in accident cases with inclined road. In additions, foreign/domestic empirical cases and multibody dynamic simulation results are used to construct a least-squares fitted model that has the same structure of the analytical one that provides an estimate of the vehicle speed based on the pedestrian throw distance and the band within which the vehicle speed would be expected to be in 95% of cases.

• Transactions of the Korean Society of Automotive Engineers
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• v.2 no.1
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• pp.128-141
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• 1994

This roport explain the basic theory of desinging the accelerating durability test road and the role of each factors contributing to test road surface profile. Also this road is designed by considering the charactors of vehicle suspension system and condition of driving. In test road, the factors affecting to the vehicle Structural durability are correlation among surface shape of road profile, frequency of vehicle suspension system, distribution of axletwist angle and vibration profile height Road PSD magnitude and frequency delay is used to control these factors relation.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.2
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• pp.25-32
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• 2013

Entry speed of the vehicle and lateral acceleration acting on the vehicle, roll-angle associated with the overthrow, and then the structure of the road, the friction of road surface are important factors in turning on the curved road. In this study, we analyzed the state change of the vehicle causing entry speed of the vehicle and superelevation of the road, the friction coefficient by using a PC-crash Program for traffic accident reconstruction. As a result, when vehicle is turning the curved road, we could ascertain that the structure of the road and state of the road surface are a major factor about the set up of limited speed.

• 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.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.17 no.6
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• pp.91-96
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• 2008

As a remarkable reduction of the vehicle noise, the important of tire noise which is generated from the vehicle and the necessity of the research for the noise reduction is being emphasized. In this study, the road noise which is excited by the interaction between tire and road has been studied. The subjective test(feeling test) according to SAE J1060 rating scale is applied to the evaluation of the road noise. The combination of the several tires and vehicles are made to consider the effect of the vehicle suspension and the tire structure for road noise. The vehicles with 3-different suspension system are applied to road noise test and the eight kinds of tires are selected. As the results, the effects of the vehicle suspension and tire structure which affects on road noise are investigated.