• Journal of Communications and Networks
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• v.15 no.2
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• pp.173-178
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• 2013

It is a concern that eco-driving vehicles, because their driving behavior differs from other vehicles due to e.g. e-start, may inhibit smooth traffic flow. Therefore, it is necessary to study the cooperative eco-driving done by a vehicle group, putting "vehicle-to-vehicle communication" and "road-to-vehicle communication" into perspective. Based on these factors, this study aimed to: 1) Analyze fuel consumption rates and driving behaviors of more than one vehicle following an Eco-Driving vehicle. 2) Examine the effect of information on the fuel consumption rate of the preceding vehicles on the following vehicles. As a result, the following findings were obtained: 1) By providing information to multiple following vehicles, the fuel consumption rate of the second vehicle was not lowered, while that of the third one was. 2) It is possible that, when information on fuel consumption of a preceding vehicle is provided to the following one, an inter-vehicular distance is shortened during deceleration to contribute to smooth traffic flow. From the above results, it is suggested that, when targeting a vehicle group, sharing the information on preceding vehicles is effective.

• Journal of Biosystems Engineering
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• v.27 no.5
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• pp.417-424
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• 2002

This study was conducted to develop a vehicle, leading or following a worker at a certain distance to assist laborious transporting works in greenhouses. A prototype vehicle was tested in the practical field conditions using a developed control algorithm. Results of this study were summarized as following: 1. The sensing device consisted of infrared sensors was attached to the front of the vehicle and turning following algorithm was developed to make the vehicle turned as it follows a worker simultaneously. 2. The measured average power consumptions were 110W and 89W, equivalent to 5.2-6.4 hrs battery durations, at low speed with and without the maximum payload, respectively. 3. Results of the travel tests showed that the deviations from the center of row spacing were $\pm$100 mm along the ridge and $\pm$85 mm along the hydroponic bed in the greenhouse. Therefore, the worker-following transport vehicle was feasible to travel along the row without collision in the greenhouse.

• Transactions of the Korean Society of Automotive Engineers
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• v.8 no.4
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• pp.130-140
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• 2000

In this paper the reference model following control(RMFC) scheme through the optimal control theory is investigated for the independent rear wheel steering(IRWS) vehicle. RMFC vehicle follows the dynamic performance of a virtual vehicle as a reference model deisgned in the controller. Linear vehicle model of two degres-of-freedom is used to derive control scheme which is applied to full vehicle for evaluating handling performances. And 4WS vehicle through RMFC is compared to the conventional 2WS vehicle and 4WS vehicle in the J-turn test. The RMFC logic is also extended to IRWS vehicle, IRWS with RMFC shows not only the excellent handling performance but salso some advantages in terms of the directional stability and responsiveness from the simulation results.

• Journal of Auto-vehicle Safety Association
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• v.11 no.3
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• pp.30-36
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• 2019

This paper suggests a car-following algorithm for urban environment, with multiple target candidates. Until now, advanced driver assistant systems (ADASs) and self-driving technologies have been researched to cope with diverse possible scenarios. Among them, car-following driving has been formed the groundwork of autonomous vehicle for its integrity and flexibility to other modes such as smart cruise system (SCC) and platooning. Although the field has a rich history, most researches has been focused on the shape of target trajectory, such as the order of interpolated polynomial, in simple single-lane situation. However, to introduce the car-following mode in urban environment, realistic situation should be reflected: multi-lane road, target's unstable driving tendency, obstacles. Therefore, the suggested car-following system includes both in-lane preceding vehicle and other factors such as side-lane targets. The algorithm is comprised of three parts: path candidate generation and optimal trajectory selection. In the first part, initial guesses of desired paths are calculated as polynomial function connecting host vehicle's state and vicinal vehicle's predicted future states. In the second part, final target trajectory is selected using quadratic cost function reflecting safeness, control input efficiency, and initial objective such as velocity. Finally, adjusted path and control input are calculated using model predictive control (MPC). The suggested algorithm's performance is verified using off-line simulation using Matlab; the results shows reasonable car-following motion planning.

• International Journal of Naval Architecture and Ocean Engineering
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• v.13 no.1
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• pp.758-771
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• 2021

This paper presents a novel control scheme for the three-dimensional (3D) path following of underactuated Autonomous Underwater Vehicle (AUVs) subject to unknown internal and external disturbances, in term of the time scale decomposition method. As illustration, two-time scale motions are first artificially forced into the closed-loop control system, by appropriately selecting the control gain of the integrator. Using the singular perturbation theory, the integrator is considered as a fast dynamical control law that designed to shape the space configuration of fast variable. And then the stabilizing controller is designed in the reduced model independently, based on the time scale decomposition method, leading to a relatively simple control law. The stability of the resultant closed-loop system is demonstrated by constructing a composite Lyapunov function. Finally, simulation results are provided to prove the efficacy of the proposed controller for path following of underactuated AUVs under internal and external disturbances.

• 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.

• 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 Biosystems Engineering
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• v.27 no.5
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• pp.409-416
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• 2002

This study was conducted to develop a vehicle, leading or following a worker at a certain distance to assist laborious transporting works in greenhouses. A prototype vehicle, which consisted of the rear driving, the front steering and the console units, was designed and tested in the ideal indoor conditions. Results of this study were summarized as following: 1. The driving unit was designed to travel at the speed ranges of 0.3∼0.8 m/sec depending on the operating modes with a maximum payload of 100 kg. 2. The console unit consisted of a main-board including a 80C196KC microprocessor and peripheral devices, a power-board and safety interlock. Worker-leading, and following modes were available in automatic and manual modes. 3. Steering was achieved by turning the steering motor against the sensed direction. Proper steering angles for correcting travel direction were determined as 5 and 9 degrees when sensing cultivation beds and plants, respectively.

• Journal of Drive and Control
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• v.17 no.1
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• pp.13-20
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• 2020

Adaptive neural networks based lateral controller is presented to guarantee path following performance for vehicle lane keeping in the presence of parameter time-varying characteristics of the vehicle lateral dynamics due to the road surface condition, load distribution, tire pressure and so on. The proposed adaptive controller could compensate vehicle lateral dynamics deviated from nominal dynamics resulting from parameter variations by incorporating it with neural networks that have the ability to approximate any given nonlinear function by adjusting weighting matrices. The controller is derived by using Lyapunov-based approach, which provides adaptive update rules for weighting matrices of neural networks. To show the superiority of the presented adaptive neural networks controller, the simulation results are given while comparing with backstepping controller chosen as the baseline controller. According to the simulation results, it is shown that the proposed controller can effectively keep the vehicle tracking the pre-given trajectory in high velocity and curvature with much accuracy under parameter variations.

• Journal of Advanced Marine Engineering and Technology
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• v.38 no.10
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• pp.1287-1296
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• 2014

This paper is about control of Automated Guided Vehicle for path following using fuzzy logic controller. The Automated Guided Vehicle is a tricycle wheeled mobile robot with three wheels, two fixed passive wheels and one steering driving wheel. First, kinematic and dynamic modeling for Automated Guided Vehicle is presented. Second, a controller that integrates two control loops, kinematic control loop and dynamic control loop, is designed for Automated Guided Vehicle to follow an unknown path. The kinematic control loop based on Fuzzy logic framework and the dynamic control loop based on two PID controllers are proposed. Simulation and experimental results are presented to show the effectiveness of the proposed controllers.