• Journal of Auto-vehicle Safety Association
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• v.11 no.3
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• pp.37-42
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• 2019

This paper presents an autonomous acceleration planning algorithm for pedestrian collision avoidance at urban. Various scenarios between pedestrians and a vehicle are designed to maneuver the planning algorithm. To simulate the scenarios, we analyze pedestrian's behavior and identify limitations of fusion sensors, lidar and vision camera. Acceleration is optimally determined by considering TTC (Time To Collision) and pedestrian's intention. Pedestrian's crossing intention is estimated for quick control decision to minimize full-braking situation, based on their velocity and position change. Feasibility of the proposed algorithm is verified by simulations using Carsim and Simulink, and comparisons with actual driving data.

• Journal of Internet Computing and Services
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• v.13 no.4
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• pp.95-102
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• 2012

The development of applications for vehicular ad hoc networks (VANETs) has very specific and clear goals such as providing intellectual safe transport systems. An emergency warning technic for public safety is one of the applications which requires an intelligent broadcast mechanism to transmit warning messages quickly and efficiently against the time restriction. The broadcast storm problem causing several packet collisions and extra delay has to be considered to design a broadcast protocol for VANETs, when multiple nodes attempt transmission simultaneously at the access control layer. In this paper, we propose an early intelligent alert broadcasting (EI-CAST) algorithm to resolve effectively the broadcast storm problem and meet time-critical requirement. The proposed algorithm uses not only the early alert technic on the basis of time to collision (TTC) but also the intelligent broadcasting technic on the basis of fuzzy logic, and the performance of the proposed algorithm was compared and evaluated through simulation with the existing broadcasting algorithms. It was demonstrated that the proposed algorithm shows a vehicle can receive the alert message before a collision and have no packet collision when the distance of alert region is less than 4 km.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.2
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• pp.98-112
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• 2018

Intentional aggressive driving (IAD) is a very dangerous driving behavior that threatens to attack the adjacent vehicles. Most existing studies have focused on the independent driving characteristics of attack drivers. However, the identification of interactions between the offender and the victim is necessary for the traffic safety analysis. This study established multi-agent driving simulation environments to systematically analyze vehicle interactions in terms of traffic safety. Time-to-collision (TTC) was adopted to quantify vehicle interactions in terms of traffic safety. In addition, a exponential decay function was further applied to compare the overall pattern of change in crash potentials when IAD events occurred. The outcome of this study would be useful in developing policy-making activities to enhance traffic safety by reducing dangerous driving events including intentional aggressive driving.

• Journal of Korean Society of Transportation
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• v.33 no.4
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• pp.323-336
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• 2015

Estimating the risks on the roadway using surrogate safety measures (SSM) has an advantage in that it focuses on the vehicle trajectory directly involved in conflicts. On the other hand, there is a restriction on estimating the risks of continuous segments due to the limited data collected from a location. To overcome the restriction, this study presents the scheme of acquiring the vehicular trajectory using real time kinematics-differential global positioning system (RTK-DGPS) and develops a methodology which contains the considerations of the problems to calculate the SSM such as time-to-collision (TTC), deceleration rate to avoid collision (DRAC) and acceleration noise (AN). By using the methodology, this study shows a result from an experiment executed in a section where the variation of vehicular movement can be observed from several continuous flow roadway sections near Seoul and Gyeonggi Province in Korea. The result illustrated the risks on the roadway by the SSM metrics in certain situations like merging and diverging, stop-and-go, and weaving. This study would be applied to relate the dangers with characteristics of drivers and roadway sections, and prevenst accidents or conflicts by detecting dangerous roadway sections and drivers' behaviors. This study contributes to improving roadway safety and reducing car-accidents.

• Journal of Auto-vehicle Safety Association
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• v.4 no.1
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• pp.12-17
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• 2012

ADAS(Advanced Driver Assistance System) which is developed for alleviating driver's load has become improved with extending it's role. Previously, ADAS offered simple function just to make driver's convenience. However, nowadays ADAS also acts as Active Safety system which is made to release and/or prevent accidents. Longitudinal control system, as one of major parts of Active Safety System, is assessed as doing direct effect on avoiding accidents. Therefore, many countries such as Europe and America has pushed longitudinal control system as a government-wide project. In this paper, it covers the result of evaluation system and vehicle evaluation for development study in FCW, ACC and AEB.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.12
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• pp.561-568
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• 2021

In recent years, autonomous driving technologies have become a high-value-added technology that attracts attention in the fields of science and industry. For smooth Self-driving, it is necessary to accurately detect an object and estimate its movement speed in real time. CNN-based deep learning algorithms and conventional dense optical flows have a large consumption time, making it difficult to detect objects and estimate its movement speed in real time. In this paper, using a single camera image, fast object detection was performed using the YOLOv5 algorithm, a deep learning algorithm, and fast estimation of the speed of the object was performed by using a local dense optical flow modified from the existing dense optical flow based on the detected object. Based on this algorithm, we present a system that can predict the collision time and probability, and through this system, we intend to contribute to prevent tram accidents.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.4
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• pp.392-397
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• 2014

Recently, there are many researches on active safety system of intelligent vehicle. To reduce the probability of collision caused by driver's inattention and mistakes, the active safety system gives warning or controls the vehicle toward avoiding collision. For the purpose, it is necessary to recognize and analyze circumstances around. In this paper, we will treat the problem about collision risk assessment. In general, it is difficult to calculate the collision risk before it happens. To consider the uncertainty of the situation, Monte Carlo simulation can be employed. However it takes long computation time and is not suitable for practice. In this paper, we apply neural networks to solve this problem. It efficiently computes the unseen data by training the results of Monte Carlo simulation. Furthermore, we propose the features affects the performance of the assessment. The proposed algorithm is verified by applications in various crash scenarios.

• Journal of Korean Society of Transportation
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• v.25 no.6
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• pp.65-77
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• 2007

A two-way left-turn lane is a continuous center left-turn lane that could be used as a deceleration and refuge area for both directions of left-turning vehicles. TWLTL's have been used effectively for access management treatment when applied to a highway that has wide-spread left turning traffic demand and a high-density of side streets. In this study, an effective analysis was carried out using a computer-based simulation tool, VISSIM, in order to evaluate performance and safety effects of TWLTLs and develop a warrant. In conclusion, the results indicated that there was a remarkable decrease of through and left-turning vehicle travel time delay on the main road and improvement of traffic safety.