A real-time lane changing and line changing algorithm for driving simulators based on virtual driver behavior

Naseri, H.; Nahvi, Ali; Karan, Farshad Salimi Naneh

doi:10.1057/s41273-017-0053-1

Cited by 5 publications

(5 citation statements)

References 48 publications

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“…In this simulator, three high-resolution 3D video projectors have been employed to produce a field of view of 180°. In order to simulate road conditions, several pneumatic actuators have been exploited and an electric force feedback has also been used to provide steering wheel torque [ 36 ]. Figure 4 a,b show the external and internal views of the used driving simulator, respectively.…”

Section: Dataset and Experimental Setupmentioning

confidence: 99%

Driver Drowsiness Detection Based on Steering Wheel Data Applying Adaptive Neuro-Fuzzy Feature Selection

Arefnezhad

Samiee

Eichberger

et al. 2019

Sensors

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This paper presents a novel feature selection method to design a non-invasive driver drowsiness detection system based on steering wheel data. The proposed feature selector can select the most related features to the drowsiness level to improve the classification accuracy. This method is based on the combination of the filter and wrapper feature selection algorithms using adaptive neuro-fuzzy inference system (ANFIS). In this method firstly, four different filter indexes are applied on extracted features from steering wheel data. After that, output values of each filter index are imported as inputs to a fuzzy inference system to determine the importance degree of each feature and select the most important features. Then, the selected features are imported to a support vector machine (SVM) for binary classification to classify the driving conditions in two classes of drowsy and awake. Finally, the classifier accuracy is exploited to adjust parameters of an adaptive fuzzy system using a particle swarm optimization (PSO) algorithm. The experimental data were collected from about 20.5 h of driving in the simulator. The results show that the drowsiness detection system is working with a high accuracy and also confirm that this method is more accurate than the recent available algorithms.

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Section: Dataset and Experimental Setupmentioning

confidence: 99%

Driver Drowsiness Detection Based on Steering Wheel Data Applying Adaptive Neuro-Fuzzy Feature Selection

Arefnezhad

Samiee

Eichberger

et al. 2019

Sensors

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show abstract

“…The proposed lane‐changing model is capable of mimicking human drivers’ lane‐changing behaviors. Besides all these studies on human driver lane‐changing analyses, lots of researchers focus on lane‐changing models (Pierson, Schwarting, Karaman, & Rus, 2018; Rahman, Chowdhury, Xie, & He, 2013; Schubert, Schulze, & Wanielik, 2010; Zheng, 2014): especially dynamic trajectory planning and collision avoidance (Ji, Khajepour, Melek, & Huang, 2016; Naseri, Nahvi, & Karan, 2017; G. Xu, Liu, Ou, & Song, 2012; Yang, Zheng, Wen, Jin, & Ran, 2018). Tang, Liu, Zhang, Ke, and Zou (2018) propose a lane‐changing predictor based on the adaptive fuzzy neural network (AFNN) to predict steering angles.…”

Section: Introductionmentioning

confidence: 99%

Modeling and field experiments on autonomous vehicle lane changing with surrounding human‐driven vehicles

Wang

Zhao

et al. 2020

Computer aided Civil Eng

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Autonomous vehicle (AV) technology is widely studied in both industrial and academic communities since it is regarded as a promising means for improving transportation safety and efficiency. Lane changing is a critical link for higher‐level AV operations. However, few studies on AV lane changing consider the dynamics of surrounding vehicles, particularly in a mixed traffic environment including human‐driven vehicles (HVs). Therefore, this article presents a dynamic lane‐changing model for AV incorporating human driver behavior in mixed traffic. The proposed model includes four key components: car following (and lane keeping), lane‐changing decision, dynamic trajectory generation, and model predictive control (MPC)‐based trajectory tracking. AV longitudinal control algorithm is also depicted in detail in this article. Field experiments are conducted on a large‐scale test track to test and validate the proposed model. An AV and three HVs are used in the lane‐changing experiments. Different human driver behaviors are considered in the experiment settings. Experimental results show that the proposed lane‐changing model can complete lane‐changing maneuvers efficiently when HVs are cooperative and can also robustly abort them when HVs are uncooperative. Compared with the measured human lane‐changing maneuvers, AV lane‐changing maneuvers from the proposed model are more comfortable and safer.

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“…It is worth reminding that statistical physics considers each particle of the matter, and uses simple conservation laws [2] for them (by some simple assumptions). Next, the scientist tries to understand and formulate the behavior [3], [4] of the whole complex based on these assumptions and formulations. This point of view with which the properties of the whole structure or complex are obtained by considering particles (as individual) is the most powerful aspect of statistical physics which is used in chemistry (crystallization behavior of some materials [5]), material sciences (time dependent growth of lattices [6]), and even in social sciences (social dynamics).…”

Section: Introductionmentioning

confidence: 99%

Controllability of a Nonlinear Voter Model

Shiran¹

2018

MJMIE

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Converging and emerging behavior of different phenomena call for a strong tool to describe the behavior of mutli-agent systems. Economics and quantitative sociology are two of the most important areas where these behavioral patterns are observed. In this paper, a brief introduction to statistical physics is provided as a tool to understand and investigate the upper-mentioned phenomena. Some of the available interaction models used in quantitative sociology have been presented to get an idea of how they work. Using one of these models, a discrete nonlinear dynamic system is presented. Main focus of this paper is providing necessary tools and solutions for discrete nonlinear dynamical systems.

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A real-time lane changing and line changing algorithm for driving simulators based on virtual driver behavior

Cited by 5 publications

References 48 publications

Driver Drowsiness Detection Based on Steering Wheel Data Applying Adaptive Neuro-Fuzzy Feature Selection

Driver Drowsiness Detection Based on Steering Wheel Data Applying Adaptive Neuro-Fuzzy Feature Selection

Modeling and field experiments on autonomous vehicle lane changing with surrounding human‐driven vehicles

Controllability of a Nonlinear Voter Model

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