Development of a Driver Lateral Control Model by Integrating Neuromuscular Dynamics Into the Queuing Network-Based Driver Model

2015 ◽  
Vol 16 (5) ◽  
pp. 2479-2486 ◽  
Author(s):  
Luzheng Bi ◽  
Mingtao Wang ◽  
Cuie Wang ◽  
Yili Liu
Keyword(s):  
Control Model ◽  
Driver Model ◽  
Queuing Network ◽  
Lateral Control ◽  
Neuromuscular Dynamics

Racing Driver Modeling Based on Driving Behavior

2021 ◽  
Author(s):  
Jinzhen Wang ◽  
Yiming Cheng ◽  
Liangyao Yu
Keyword(s):  
Preliminary Test ◽  
Control Model ◽  
Skill Level ◽  
Driver Model ◽  
Test Results ◽  
Singularity Detection ◽  
Handling Characteristics ◽  
Driving Skill ◽  
Dynamic Velocity ◽  
The Given

Abstract The driver model is an important link in the research of shared autonomy control. In order to simulate the driver’s handling characteristics in the complex human-vehicle-road closed-loop system, the driver model is required to accomplish the driving operation under specific working conditions. In this paper, a lateral-longitudinal combined racing driver model is designed. The lateral control model adopts the preview model with far and near viewpoints and the dynamic velocity controller is added into the longitudinal control model to obtain the expected speed of the target trajectory. Finally, the racing driver model proposed in this paper is validated through simulation on track conditions of FSAE. In the given conditions, the result shows the racing driver model outperforms the typical driver model in lateral path tracking and the speed of racing driver model is higher than typical model on straight and corners. Meanwhile, the representation of driving skills is a key step to enhance the adaptive control of vehicles in the future. The control parameters can be adjusted according to the driver’s skill information to make the vehicle control system adapt to the driver’s skill level. This paper introduces the method of driving skill recognition based on wavelet transform and Lipschitz singularity detection theory and the preliminary test results prove the feasibility of using this method to characterize the driver’s operating skill level.

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.

scholarly journals Modeling a Driver’s Directional and Longitudinal Speed Control Based on Racing Track Features

2018 ◽  
Vol 2018 ◽  
pp. 1-12
Author(s):  
Jianfeng Wang ◽  
Weihua Li ◽  
Jun Li ◽  
Yiqun Liu ◽  
Baoyu Song ◽  
...  
Keyword(s):  
Control Strategy ◽  
Search Algorithm ◽  
Speed Control ◽  
Control Model ◽  
Operating Conditions ◽  
Driver Model ◽  
Operation Model ◽  
Driving System ◽  
Point Search ◽  
Feedback Control Strategy

This study firstly analyses the driver’s manipulation behaviour and relates the different components of the driver model. Then, a model controlling the driver directions is built according to the prediction-follower theory with the aim of improving the point search algorithm. A model of the driving system of an electric vehicle is used to establish the longitudinal speed control model of the driver by using a feedforward-PID feedback control strategy. Our approach is to release the coupling between direction and speed control and build an integrated model that includes the direction and speed for an arbitrary path. Finally, the characteristics of an actual racing track are considered to establish the fastest driver control model. We simulated the typical operating conditions of our driver operation model. The simulation confirmed the effectiveness of the improved predictive point search algorithm and the integrated driver model to control the direction and speed for an arbitrary path.

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