• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2005.04a
• /
• pp.161-165
• /
• 2005

In order to produce an optimal traffic cycle. We must first check how many waiting cars are at the lower intersection, because waiting queue is bigger than the length of upper traffic intersection. Start up delay time and vehicle waiting time occurs. To reduce vehicle waiting time, in this paper, we present an optimal green time algorithm using fuzzy neural network. Through computer simulation has been proven to be improved average vehicle speed than fixed traffic signal light which do not consider different intersection conditions.

• Transactions of the Korean Society of Automotive Engineers
• /
• v.22 no.5
• /
• pp.35-43
• /
• 2014

The automobile industry requires technological innovations to reduce fuel consumption with the public interest in environmental conservation in recent years. Thus, the hybrid system is applied not only to passenger cars but also commercial vehicles. The purpose of this paper is to develop engine ECU_ILS to develop commercial hybrid vehicles. In order to develop the engine and vehicle, the dynamometer and exhaust gas analyzer is needed. However, a lot of time and cost are required. In contrast, the model-based development environment that can be applied to a variety of test conditions can reduce development time. Therefore, a HILS system environment that can consider the behavior of actual vehicles for evaluation of the control logic, fuel consumption and exhaust gas is required. This engine ECU_ILS system was developed in this study, can analyze parameter such as the fuel injection rate, fuel injection time, fuel consumption and exhaust gas like the actual vehicle test using map data. Also, this system is expected to be able to analyze the characteristic of vehicle behavior and the development of peripheral device in relation to engine and vehicles. This HILS system can be used to develop control strategies of commercial hybrid vehicle systems in the future.

• IEMEK Journal of Embedded Systems and Applications
• /
• v.10 no.5
• /
• pp.325-333
• /
• 2015

Vision based night-time vehicle detection has been an emerging research field in various advanced driver assistance systems(ADAS) and automotive vehicle as well as automatic head-lamp control. In this paper, we propose night-time vehicle detection method based on multi-class support vector machine(SVM) that consists of thresholding, labeling, feature extraction, and multi-class SVM. Vehicle light candidate blobs are extracted by local mean based thresholding following by labeling process. Seven geometric and stochastic features are extracted from each candidate through the feature extraction step. Each candidate blob is classified into vehicle light or not by multi-class SVM. Four different multi-class SVM including one-against-all(OAA), one-against-one(OAO), top-down tree structured and bottom-up tree structured SVM classifiers are implemented and evaluated in terms of vehicle detection performances. Through the simulations tested on road video sequences, we prove that top-down tree structured and bottom-up tree structured SVM have relatively better performances than the others.

• Journal of Drive and Control
• /
• v.16 no.4
• /
• pp.48-55
• /
• 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 Power Electronics
• /
• v.11 no.4
• /
• pp.479-489
• /
• 2011

The bidirectional dc-dc converter, being the interface between Energy Storage Element (ESE) and DC bus, is an essential component of the power management system for vehicle applications including electric vehicle (EV), hybrid electric vehicle (HEV), and fuel cell vehicle (FCV). In this paper, a novel multiphase bidirectional dc-dc converter interfacing with battery to supply and absorb the electric energy in the FCV system was studied with the help of real time digital simulator (RTDS). The mathematical models of fuel cell, battery and dc-dc converter were derived. A power management strategy was developed and first simulated in RTDS. A Power Hardware-In-the-Loop (PHIL) simulation using RTDS is then presented. The main challenge of this PHIL is the requirement for a highly dynamic bidirectional Simulation-Stimulation (Sim-Stim) interface. This paper describes three different interface algorithms. The closed-loop stability of the resulting PHIL system is analyzed in terms of time delay and sampling rate. A prototype bidirectional Sim-Stim interface is designed to implement the PHIL simulation.

• Journal of Korea Multimedia Society
• /
• v.24 no.12
• /
• pp.1632-1640
• /
• 2021

In order to improve target tracking performance of the conventional electro-optical tracking system (EOTS) in the high speed and high maneuvering vehicle, an EOTS navigation algorithm is proposed, in which an inertial measurement unit(IMU) is included and navigation results of the vehicle are used. The proposed algorithm integrates vehicle's navigation results and the IMU and the time-delay and the scale factor errors are augmented into the integrated Kalman filter. In order to evaluate the proposed navigation algorithm, a land vehicle navigation experiments were performed a navigation grade navigation system, TALIN4000 and a tactical grade IMU, LN-200 and a equipment for roll motion were loaded on the land vehicle. The performance evaluation results show that the proposed algorithm effecting works in high maneuvering environment and for the time-delay.

• Journal of Korean Society of Transportation
• /
• v.29 no.3
• /
• pp.73-82
• /
• 2011

Recent advancement in traffic surveillance systems has allowed the researchers to obtain more detailed vehicular movement such as individual vehicle trajectory data. Understanding the characteristics of interactions between leading and following vehicles in the traffic flow stream is a backbone for designing and evaluating more sophisticated traffic and vehicle control strategies. This study proposes a methodology for estimating rear-end crash potential, as a probabilistic measure, in real-time based on the analysis of vehicular movements. The methodology presented in this study consists of three components. The first predicts vehicle position and speed every second using a Kalman filtering technique. The second estimates the probability for the vehicle's trajectory to belong to either 'changing lane' or 'going straight'. A binary logistic regression (BLR) is used to model the lane-changing decision of the subject vehicle. The other component calculates crash probability by employing an exponential decay function that uses time-to-collision (TTC) between the subject vehicle and the front vehicle. The result of this study is expected to be adapted in developing traffic control and information systems, in particular, for crash prevention.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.45 no.3
• /
• pp.40-48
• /
• 2022

An automated material handling system (AMHS) has been emerging as an important factor in the semiconductor wafer manufacturing industry. In general, an automated guided vehicle (AGV) in the Fab's AMHS travels hundreds of miles on guided paths to transport a lot through hundreds of operations. The AMHS aims to transfer wafers while ensuring a short delivery time and high operational reliability. Many linear and analytic approaches have evaluated and improved the performance of the AMHS under a deterministic environment. However, the analytic approaches cannot consider a non-linear, non-convex, and black-box performance measurement of the AMHS owing to the AMHS's complexity and uncertainty. Unexpected vehicle congestion increases the delivery time and deteriorates the Fab's production efficiency. In this study, we propose a Q-Learning based dynamic routing algorithm considering vehicle congestion to reduce the delivery time. The proposed algorithm captures time-variant vehicle traffic and decreases vehicle congestion. Through simulation experiments, we confirm that the proposed algorithm finds an efficient path for the vehicles compared to benchmark algorithms with a reduced mean and decreased standard deviation of the delivery time in the Fab's AMHS.

• Journal of Navigation and Port Research
• /
• v.33 no.2
• /
• pp.143-151
• /
• 2009

The field of vehicle routing is currently growing rapidly because of many actual applications in truckload and less than truckload trucking, courier services, door to door services, and many other problems that generally hinder the optimization of transportation costs in a logistics network. The rapidly increasing number of customers in such a network has caused problems such as difficulty in cost optimization in terms of getting a global optimum solution in an acceptable time. Fast algorithms are needed to find sufficient solutions in a limited time that can be used for real time scheduling. In this paper, the nearest L-method (NLNM) is proposed to obtain a vehicle routing solution. String neighbors of different lengths were chosen, tested and compared. The applied de crossing procedure is meant to solve the routes by NLNM by giving a better solution and shorter computation time than that of NLNM with long string neighbors.

• International conference on construction engineering and project management
• /
• 2022.06a
• /
• pp.945-951
• /
• 2022

The major objective of this research is to evaluate performance of improved intersections for response time to emergency vehicle preemption. Smart technologies have been introduced to civil infrastructure systems for resilient communities. The technologies need to evaluate their effectiveness and feasibility to confirm their introduction. This research focuses on the performance of emergency vehicle preemption, represented by response time, when smart intersections are introduced in a community. The response time is determined by not only intersections but also a number of factors such as traffic, distance, road conditions, and incident types. However, the evaluation of emergency response has often ignored factors related to emergency vehicle routes. In this respect, this research synthetically analyzes geospatial and incident data using each route of emergency vehicle and conducts before-and-after evaluations. The changes in performance are analyzed by the impact of smart intersections on response time through Bayesian regression models. The result provides measures of the project's performance. This study will contribute to the body of knowledge on modeling the impacts of technology application and integrating heterogeneous data sets. It will provide a way to confirm and prove the effectiveness of introducing smart technologies to our communities.