scholarly journals Measurement and Modeling of the Effect of Sensory Conflicts on Driver Steering Control

2019 ◽  
Vol 141 (6) ◽  
Author(s):  
Christopher J. Nash ◽  
David J. Cole
Keyword(s):  
Virtual Environment ◽  
Measurement Noise ◽  
Driver Model ◽  
Steering Control ◽  
New Model ◽  
Physical Motion ◽  
Average Variance ◽  
Scale Motion ◽  
Single Set ◽  
Visual Measurements

In previous work, a new model of driver steering control incorporating sensory dynamics was derived and used to explain the performance of drivers in a simulator with full-scale motion feedback. This paper describes further experiments investigating how drivers steer with conflicts between their visual and vestibular measurements, caused by scaling or filtering the physical motion of the simulator relative to the virtual environment. The predictions of several variations of the new driver model are compared with the measurements to understand how drivers perceive sensory conflicts. Drivers are found to adapt well in general, unless the conflict is large, in which case they ignore the physical motion and rely on visual measurements. Drivers make greater use of physical motion which they rate as being more helpful, achieving a better tracking performance. Sensory measurement noise is shown to be signal-dependent, allowing a single set of parameters to be found to fit the results of all the trials. The model fits measured linear steering behavior with an average “variance accounted for (VAF)” of 86%.

scholarly journals Model predictive control–based steering control algorithm for steering efficiency of a human driver in all-terrain cranes

2019 ◽  
Vol 11 (6) ◽  
pp. 168781401985978
Author(s):  
Ja-Ho Seo ◽  
Kwang-Seok Oh ◽  
Hong-Jun Noh
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Control Algorithm ◽  
Steering System ◽  
Driver Model ◽  
Control Technique ◽  
Change Scenario ◽  
Steering Control ◽  
Human Driver ◽  
Steering Effort

All-terrain cranes with multi-axles have large inertia and long distances between the axles that lead to a slower dynamic response than normal vehicles. This has a significant effect on the dynamic behavior and steering performance of the crane. Therefore, the purpose of this study is to develop an optimal steering control algorithm with a reduced driver steering effort for an all-terrain crane and to evaluate the performance of the algorithm. For this, a model predictive control technique was applied to an all-terrain crane, and a steering control algorithm for the crane was proposed that could reduce the driver’s steering effort. The steering performances of the existing steering system and the steering system applied with the newly developed algorithm were compared using MATLAB/Simulink and ADAMS with a human driver model for reasonable performance evaluation. The simulation was performed with both a double lane change scenario and a curved-path scenario that are expected to happen in road-steering mode.

The Improved Algorithm to Preview Follower Control Applied Research on Intelligent Electric Vehicle

2014 ◽  
Vol 1079-1080 ◽  
pp. 1022-1025
Author(s):  
Sheng Rui Liu
Keyword(s):  
Electric Vehicle ◽  
Control Strategy ◽  
Control Method ◽  
Search Algorithm ◽  
Path Model ◽  
Driver Model ◽  
Domain Modeling ◽  
Steering Control ◽  
Tracking Accuracy ◽  
Electric Cars

This paper presents an improved preview follower, electric vehicle intelligent driver model of steering control strategy. And from the preview following the model proposed steering control method, and the preview follower algorithm, propose a new preview search algorithm, in order to ensure the preview points fall within the expected path, avoid the path curvature caused by excessive electric cars from the path. In addition, by considering the steady state response, to improve the precision of steering control strategy. Use of the multi domain modeling software Dipolar, combined with the electric vehicle dynamic model, the path model of the steering control strategy simulation. The simulation results show that the strategy is applied to electric vehicle path goal good tracking accuracy.

Integrated Speed and Steering Control Driver Model for Vehicle–Driver Closed-Loop Simulation

2016 ◽  
Vol 65 (6) ◽  
pp. 4401-4411 ◽  
Author(s):  
Youngil Koh ◽  
Hyundong Her ◽  
Kyongsu Yi ◽  
Kilsoo Kim
Keyword(s):  
Closed Loop ◽  
Driver Model ◽  
Steering Control

Considerations for Minimum Lap Time Calculation on a Race Track

2002 ◽  
Author(s):  
Marco Di Pierro ◽  
Juergen Schuller
Keyword(s):  
Virtual Environment ◽  
Closed Loop ◽  
Maximum Velocity ◽  
Driver Model ◽  
Vehicle Modeling ◽  
Time Calculation ◽  
Race Track ◽  
Planning Strategies ◽  
Arbitrary Trajectory

While methods for vehicle modeling are well established for simulation of handling behavior, there is still a lack of driver models, which are important for the realization of closed-loop maneuvers in a virtual environment. This paper will present preliminary considerations for the development of such a driver model. First, trajectory planning strategies have to be generated and evaluated. To achieve this, a method will be deduced, which calculates the maximum velocity at each point of an arbitrary trajectory, taking into account simplified vehicle characteristics in terms of maximum longitudinal and lateral accelerations and considering the frictional ellipse. Thus, the minimum necessary time for each trajectory can be calculated, this being a possible parameter to rate the quality of a trajectory for a given course. The feasibility of the method is demonstrated with the Nuerburgring race track.

scholarly journals MIGSHIELD: A new model-based interactive point kernel gamma ray shielding package for virtual environment

2020 ◽  
Vol 52 (7) ◽  
pp. 1557-1564
Author(s):  
Mengkun Li ◽  
Zhihui Xu ◽  
Wei Li ◽  
Jun Yang ◽  
Ming Yang ◽  
...  
Keyword(s):  
Virtual Environment ◽  
Gamma Ray ◽  
New Model ◽  
Model Based

The Active Front Steering Control Strategy Based on Driver Behavior

2013 ◽  
Vol 373-375 ◽  
pp. 1277-1282
Author(s):  
Jian Zhao ◽  
Yun Fu Su ◽  
Bing Zhu ◽  
Peng Fei Wang
Keyword(s):  
Driver Behavior ◽  
Driver Model ◽  
Steering Wheel ◽  
Steering Control ◽  
Active Steering ◽  
Active Front Steering ◽  
Reference Vehicle ◽  
The Stability ◽  
Prediction Control ◽  
Two Degree Of Freedom

Active Front Steering (AFS) is an important application to improve the stability of the vehicle, and the driver characteristic is also an important factor for the vehicle stability. In this article, a driver-behavior-based prediction control algorithm for AFS is proposed. According to the informed road trajectory, the ideal preview driver model is introduced to predict the future steering wheel angle. Based on this, a two-degree-of-freedom (2DOF) reference vehicle model and a PID controller are used to generate active steering control. The algorithm is verified by Carsim and Matlab/Simulink co-simulation, and the results show that trajectory tracking of the vehicle can be guarantee and driver manipulation duty can be reduced.

An Analytical Approach to the Prediction of Handling Qualities of Vehicles With Advanced Steering Control System Using Multi-Input Driver Model

1998 ◽  
Vol 122 (3) ◽  
pp. 490-497 ◽  
Author(s):  
Shinichiro Horiuchi ◽  
Naohiro Yuhara
Keyword(s):  
Analytical Approach ◽  
Mental Workload ◽  
Essential Feature ◽  
Physical Workload ◽  
Driver Model ◽  
Subjective Rating ◽  
Subjective Ratings ◽  
Steering Control ◽  
Handling Qualities ◽  
Ground Test

This paper describes an analytical approach to predict the subjective rating on handling qualities of actively controlled vehicles. The approach was applied to the evaluation on handling qualities of 4 wheel steering vehicles. The validity of the approach is confirmed by comparison of the predicted results with test data. The essential feature of the approach is to use an objective function that takes into account three principal factors, i.e., the task performance, the driver’s mental workload, and his or her physical workload, to predict analytically the subjective ratings. The findings indicate that the predicted rating directly corresponds to the driver’s subjective ratings obtained through the proving ground test with a good correlation. [S0022-0434(00)00303-8]

A Mathematical Model of Driver Steering Control Including Neuromuscular Dynamics

2008 ◽  
Vol 130 (3) ◽  
Author(s):  
Andrew J. Pick ◽  
David J. Cole
Keyword(s):  
Path Following ◽  
Linear Quadratic Regulator ◽  
Driver Model ◽  
Steering Wheel ◽  
Steering Control ◽  
Linear Quadratic ◽  
Reflex Action ◽  
Path Following Control ◽  
Steering Torque ◽  
Neuromuscular Dynamics

A mathematical driver model is introduced in order to explain the driver steering behavior observed during successive double lane-change maneuvers. The model consists of a linear quadratic regulator path-following controller coupled to a neuromuscular system (NMS). The NMS generates the steering wheel angle demanded by the path-following controller. The model demonstrates that reflex action and muscle cocontraction improve the steer angle control and thus increase the path-following accuracy. Muscle cocontraction does not have the destabilizing effect of reflex action, but there is an energy cost. A cost function is used to calculate optimum values of cocontraction that are similar to those observed in the experiments. The observed reduction in cocontraction with experience of the vehicle is explained by the driver learning to predict the steering torque feedback. The observed robustness of the path-following control to unexpected changes in steering torque feedback arises from the reflex action and cocontraction stiffness of the NMS. The findings contribute to the understanding of driver-vehicle dynamic interaction. Further work is planned to improve the model; the aim is to enable the optimum design of steering feedback early in the vehicle development process.

Sign in / Sign up

Export Citation Format

Share Document