• Proceedings of the KSME Conference
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• 2007.05a
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• pp.855-860
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• 2007

This paper describes a human driver model developed based on finite preview optimal control method. The human driver steering model is constructed to minimize a performance index which is a quadratic form of lateral position error, yaw angle error and steering input. Simulation studies are conducted using a vehicle simulation software, Carsim. The Carsim vehicle model is validated using vehicle test data. In order to validate the human driving steering model, the human driver steering model is compared to the driving data on a virtual test track(VTT) and the actual vehicle test data. It is shown that human driver steering behaviors can be well represented by the human driver steering model presented in this paper

• Journal of Auto-vehicle Safety Association
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• v.9 no.3
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• pp.19-23
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• 2017

This paper presents a lateral driver model with neuromuscular system to evaluate the performance of electric power steering (EPS). Output of most previously developed driver models is steering angle. However, in order to evaluate EPS system, driver model which results in steering torque output is needed. The proposed lateral driver model mainly consists of 2 parts: desired steering angle calculation and conversion of steering angle into steering torque. Desired steering angle calculation part results in steering angle to track desired yaw rate for path tracking. Conversion of steering angle into torque is consideration with neuromuscular system. The proposed driver model is investigated via actual driving data. Compared to other algorithms, the proposed algorithm shows similar pattern of steering angle with human driver. The proposed driver can be utilized to efficiently evaluate EPS system in simulation level.

• Transactions of the Korean Society of Automotive Engineers
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• v.13 no.1
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• pp.76-82
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• 2005

In this paper, validation of Driver Steering Model has been conducted. The comparison between the simulation model and vehicle test results shows that the model is very feasible for describing combined human driver and actual vehicle dynamic behaviors. The 3D vehicle model is consisted of 6-DOF sprung mass and 4-quarter car model for vehicle body dynamics. Powertrain model including differential gear and Pacejka tire model are applied. The driver steering model is also validated with vehicle test result. The driver steering model is based on angle and displacement error from the desired path, recognized by driver.

• Journal of Mechanical Science and Technology
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• v.19 no.spc1
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• pp.444-451
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• 2005

The analysis of human arm motion during steering maneuver is carried out for investigation of man-machine interface of driver and steering system Each arm is modeled as interconnection of upper arm, lower arm, and hand by rotational joints that can properly represents permissible joint motion, and both arms are connected to a steering wheel through spring and damper at the contact points. The joint motion law during steering motion is determined through the measurement of each arm movement, and subsequent inverse kinematic analysis. Combining the joint motion law and inverse dynamic analysis, joint stiffness of arm is estimated. Arm dynamic analysis model for steering maneuver is setup, and is validated through the comparison with experimentally measured data, which shows relatively good agreement. To demonstrate the usefulness of the arm model, it is applied to study the effect of steering column angle on the steering motion.

• Transactions of the Korean Society of Automotive Engineers
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• v.5 no.3
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• pp.209-219
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• 1997

A fuzzy driver model based on a preview-predictor and yaw rate is developed. The model is used to investigate the handling performance of two wheel steering system(2WS) and four wheel steering system(4WS) vehicles. The two degree-of- freedom model which has yaw and lateral motion predicts the path of the vehicles. Based upon the yaw rate and lateral deviations, the fuzzy engine describes the human driver's complicated control behavior which is adjusted for the driving environment. Both typical single lane change maneuver and double lane change maneuver are adopted to demonstrate the feasibility of fuzzy driver model.

• International Journal of Automotive Technology
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• v.7 no.4
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• pp.449-457
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• 2006

The driver model during drift cornering was examined, and a technique to improve vehicle movement performance during drift cornering was investigated. Based on the results obtained, the driver was found to steer using feedback of the body slip angle and the body slip angle velocity during drift cornering. Moreover, improvement of the cornering force characteristic, at which exceeded the maximum cornering force calm as much as possible is important.

• Journal of the Korean Society for Precision Engineering
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• v.30 no.1
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• pp.85-95
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• 2013

As the conventional hydraulic power steering system in the passenger vehicles is being rapidly replaced by EPS (Electric Power Steering) system, performance evaluation of the EPS system has become an important issue in the automotive industries. But the evaluation process takes significant expertise since steering conditions in the test protocols must be implemented with high accuracy. EPS HILS (Hardware-In the-Loop Simulation) system is developed together with robot steering system in this study. Main components of EPS HILS system include: C-EPS hardware, CarSim vehicle model, and road reaction force generation system powered by servo motor. The robot steering system, operated by another servo motor, was combined with EPS HILS system to substitute for steering efforts of human driver. The road reaction force generation system and the robot steering system were carefully validated by using the data obtained from vehicle tests. An on-center handling test was conducted by using EPS HILS system combined with the robot steering system. In the result of this study, robot-steered EPS HILS system developed with its high reliability and no need of skilled driver's, can be widely adopted to evaluate any performance of EPS system.

• Transactions of the Korean Society of Automotive Engineers
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• v.13 no.5
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• pp.195-201
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• 2005

In conventional Vehicle Stability Control (VSC) System, a control threshold is designed by average driver characteristics. Despite the stabilizing effort, VSC causes redundancy to an expert driver. An advanced VSC which has flexibility on its control property is proposed in this study. By using lateral velocity estimator, a control threshold is determined on side slip angle and angular velocity phase plane. Vehicle planar motion model based sliding controller is modified with respect to various control thresholds. The performance of the proposed VSC algorithm has been investigated by human-in-the-loop simulation using a vehicle simulator. The simulation results show that the control threshold has to be determined with respect to the driver steering characteristics. A VSC with variable control thresholds would provide an improvement compared to a VSC with a constant threshold.

• Journal of Power Electronics
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• v.13 no.4
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• pp.536-545
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• 2013

In this study, an integrated motor control algorithm for an in-wheel electric vehicle is suggested. It consists of slip control that controls the in-wheel motor torque using the road friction coefficient and slip ratio; yaw rate control that controls the in-wheel motor torque according to the road friction coefficient and the yaw rate error; and velocity control that controls the vehicle velocity by a weight factor based on the road friction coefficient and the yaw rate error. A co-simulator was developed, which combined the vehicle performance simulator based on MATLAB/Simulink and the vehicle model of CarSim. Based on the co-simulator, a human-in-the-loop simulation environment was constructed, in which a driver can directly control the steering wheel, the accelerator pedal, and the brake pedal in real time. The performance of the integrated motor control algorithm for the in-wheel electric vehicle was evaluated through human-in-the-loop simulations.

• The Transactions of the Korea Information Processing Society
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• v.3 no.5
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• pp.1037-1045
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• 1996

In this paper, the parallel model for stereo camera is employed to find the heralding angle and the distance between a leading vehicle and the following vehicle, BART(Binocular Autonomous Research Team vehicle). Two TDNNs (Time Delay Neural Network) such as S-TDNN and A-TDNN are introduced to control BART. S-TDNN controls the speed of the following vehicle while A-TDNN controls the steering angle of BATR. A human drives BART to collect data which are used for training the said neural networks. The trained networks performed the vehicle tracking function satisfactorily under the same driving conditions performed by the human driver. The neural network approach has good portability which decreases costs and saves development time for the different types of vehicles.