Driver directional control using two-point preview and fuzzy decision

Tan, Yunsheng; Shen, Huan; Huang, M.H.; Mao, Jianguo

doi:10.1016/j.jappmathmech.2017.06.004

Cited by 11 publications

(5 citation statements)

References 16 publications

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“…The tracking performance of the driver model can be improved by adaptively adjust preview distance according to the vehicle current tracking error and road curvature [26], which simulates the near-far scanning behavior of human drivers [28]. For TP preview reference, it can be combined with FP preview reference, which allow the driver model preview the far TP and near FP point [29]. However, none of them fully represents human drivers' preview mechanism, since drivers would also adjust their visual gaze point in the lateral direction on the road during driving process [25].…”

Section: Single Point Preview-based Human-like Driver Modelmentioning

confidence: 99%

A New Single Point Preview-Based Human-Like Driver Model on Urban Curved Roads

Zhou

Jiang

et al. 2020

IEEE Access

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To make intelligent vehicles obtain human drivers' steering characteristic, a new single point preview-based human-like driver model is proposed, which contains a preview decision module and a steering wheel angle calculation module. Enlightened by the visual gaze mechanism of human drivers when passing a curved road, the preview decision module is established using Takagi-Sugeno fuzzy inference system (T-S FIS) to adaptively adjust the preview point position in both longitudinal and lateral direction, and the steering angle calculation module would use the adjusted preview point to generate steering command based on pure pursuit algorithm. Ant colony optimization (ACO) method is used to optimize the fuzzy rules in the preview decision module according to the similarity of trajectories between the proposed driver model and human drivers. The proposed human-like driver model is verified on a two-lane urban curved road. Five experienced human drivers' driving trajectories under different speeds are collected for the verification. After the preview decision module optimization done in the PreScan/Simulink simulation platform, the proposed human-like driver model shows higher similarity with experienced human drivers than the driver model with fixed preview distance or the driver model only with changeable longitudinal preview distance.

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Section: Single Point Preview-based Human-like Driver Modelmentioning

confidence: 99%

A New Single Point Preview-Based Human-Like Driver Model on Urban Curved Roads

Zhou

Jiang

et al. 2020

IEEE Access

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“…Submit the Taylor series expansion of into (12), and the first two terms of the series are considered due to being small. We have…”

Section: Focus Point Preview Driver Modelmentioning

confidence: 99%

“…It has been widely applied because of its simple structure and clear physical meaning [10]. Two-point preview models are built based on the far and near preview cues, assuming that drivers employ the near and far preview points to estimate the required steering wheel angle to control the vehicle [11][12][13]. Unlike the single-point and two-point preview model, the multiplepoint model assumes that drivers focus on a front regional road to get multiple points of deviation [14].…”

Section: Introductionmentioning

confidence: 99%

A Fractional Derivative-Based Lateral Preview Driver Model for Autonomous Automobile Path Tracking

Wang

et al. 2018

Mathematical Problems in Engineering

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The concept of focus point preview is proposed, and fractional calculus is introduced to driver model to build focus point preview driver model. A formula for calculating lateral error is given, where the weight coefficients of fractional calculus are designed to imitate the driver’s focus preview property. The relationship between the speed and the order of fractional calculus is studied. A driver-vehicle-road simulation system is set up to illustrate the performances of the proposed preview model. The S-type road is used to test the model in the case of continuous small curvature turns and the Shanghai F1 track model is used as the case that automobile passing large curvature curve. The performances are evaluated from two aspects: path tracking effect and vehicle dynamic responses. It is concluded that, as the speed of the vehicle increases, the optimal order of fractional integral increases, so that the order is seen as the degree of driver’s attention. What is more, in the case of large curvature, the path tracking performance is improved by increasing the corresponding fractional order. Simulations results also show that, compared with the single-point preview model, the performances of the focus point preview model are better. On the one hand, the proposed driver model can be used to control the vehicle steering and path tracking. On the other hand, fractional calculus is used to reveal the driver preview property and the order is given a certain physical meaning, which is conducive to the development of fractional calculus applications.

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“…The preview information can be defined based on a single point [7], two points [8,9], or multiple points [10,11] along the future road. The single-point preview model assumes that drivers mainly focus on one point of the future road to obtain the lateral deviation and, then, control the direction of the vehicle.…”

Section: Introductionmentioning

confidence: 99%

“…As for the control methods, there are three main types: classical control based on transfer functions such as proportional-integral-derivative (PID) control [15], modern control based on state space models such as the linear quadratic regulator (LQR) [11,16] and model predictive control (MPC) [17], and intelligent control based on the imitation of driver behavior such as fuzzy logic (FL) [9,18] and neural networks (NNs) [19]. The classical control methods are generally applied to a single-input, single-output system and are not suitable for the control of time-varying and nonlinear vehicle models.…”

Section: Introductionmentioning

confidence: 99%

A Data-Driven Path-Tracking Model Based on Visual Perception Behavior Analysis and ANFIS Method

Hu,

Yu,

Yang

et al. 2023

Electronics

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This paper proposes a data-driven human-like driver model (HDM) based on the analysis and understanding of human drivers’ behavior in path-tracking tasks. The proposed model contains a visual perception module and a decision-making module. The visual perception module was established to extract the visual inputs, including road information and vehicle motion states, which can be perceived by human drivers. The extracted inputs utilized for lateral steering decisions can reflect specific driving skills exhibited by human drivers like compensation control, preview behavior, and anticipation ability. On this basis, an adaptive neuro-fuzzy inference system (ANFIS) was adopted to design the decision-making module. The inputs of the ANFIS include the vehicle speed, lateral deviation in the near zone, and heading angle error in the far zone. The output is the steering wheel angle. ANFIS can mimic the fuzzy reasoning characteristics of human driving behavior. Next, a large amount of human driving data was collected through driving simulator experiments. Based on the data, the HDM was established. Finally, the results of the joint simulation under PreScan/MATLAB verified the superior performances of the proposed HDM.

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Driver directional control using two-point preview and fuzzy decision

Cited by 11 publications

References 16 publications

A New Single Point Preview-Based Human-Like Driver Model on Urban Curved Roads

A New Single Point Preview-Based Human-Like Driver Model on Urban Curved Roads

A Fractional Derivative-Based Lateral Preview Driver Model for Autonomous Automobile Path Tracking

A Data-Driven Path-Tracking Model Based on Visual Perception Behavior Analysis and ANFIS Method

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