• Journal of Auto-vehicle Safety Association
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• v.10 no.2
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• pp.29-35
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• 2018

This paper presents a quantitative evaluation method and result of moving vehicle perception using automotive radar. It is also important to analyze the accuracy of the perception algorithm quantitatively as well as to accurately percept nearby moving vehicles for safe and efficient autonomous driving. In this study, accuracy of the automotive radar-based perception algorithm which is developed based on interacting multiple model (IMM) has been verified via vehicle tests on real roads. In order to obtain experimental data for quantitative evaluation, Long Range Radar (LRR) has been mounted on the front of the ego vehicle and Short Range Radar (SRR) has been mounted on the rear side of both sides. RT-range has been installed on the ego vehicle and the target vehicle to simultaneously collect reference data on the states of the two vehicles. The experimental data is acquired in various relative positions and velocity, and the accuracy of the algorithm has been analyzed according to relative position and velocity. Quantitative analysis is conducted on relative position, relative heading angle, absolute velocity, and yaw rate of each vehicle.

• 한국가스학회:학술대회논문집
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• 2005.10a
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• pp.37-43
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• 2005

The questionnaires about the safety of the urban gas have been carried out for the end users. about 8 of 10 persons said that the urban gas Is safe to use, whereas $35\%$ of them said there exists a hazard of an accident in thier residences. There cannot be found the clear evidences that the understandings on the safety of the urban gas have no relations to their ages, sex, and monthly incomes, while the safety is less confidential to the highly educated, the accident-experienced, or the mans who are poor at the safety inspections. Most of the questioned man know the inspection knacks for the gas utilities, but only $60\%$ of them carry out it. They said that they do not feel the necessity of the inspection because they are inspected routinely by the suppliers or the inspection companies. This says that the end user does not concern the safety inspections, and in order to improve the dependency of the user for the self-inspections, the inspection staff should educate the user for the necessity and the knack of inspections to encourage the self-inspection of the gas utilities.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.2
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• pp.186-190
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• 2015

Intelligent vehicle plans motion and navigate itself based on the surrounding environment perception. Hence, the precise environment recognition is an essential part of self-driving vehicle. There exist many vulnerable road users (e.g. vehicle, pedestrians) on vehicular driving environment, the vehicle must percept all the dynamic obstacles accurately for safety. In this paper, we propose an multiple vehicle tracking algorithm using microwave radar. Our proposed system includes various special features. First, exceptional radar measurement model for vehicle, concentrated on the corner, is described by mixture density network (MDN), and applied to particle filter weighting. Also, to conquer the curse of dimensionality of particle filter and estimate the time-varying number of multi-target states, reversible jump markov chain monte carlo (RJMCMC) is used to sampling step of the proposed algorithm. The robustness of the proposed algorithm is demonstrated through several computer simulations.