Proposed Driver Model Research Articles

Driver Model of a Powered Wheelchair Operation as a Tool of Theoretical Analyses

This paper describes the construction of a driver model of a powered wheelchair operation for the understanding of the characteristics of the driver. The main targets of existing researches about driver models are the operation of the automobiles and motorcycles, not a low-speed vehicle such as powered wheelchairs. Therefore, we started by verifying the possibility of modeling the turning operation at a corner of a corridor. At first, we conducted an experiment on a daily powered wheelchair user by using his vehicle. High reproducibility of driving and the driving characteristics for the construction of a driver model were both confirmed from the result of the experiment. Next, experiments with driving simulators were conducted for the collection of quantitative driving data. The parameters of the proposed driver model were identified from experimental results. From the simulations with the proposed driver model and identified parameters, the characteristics of the proposed driver model were analyzed.

Hardware-in-the-loop simulation of automatic steering control for lanekeeping manoeuvre: outer-loop and inner-loop control design

This paper presents a 9 Degree-of-Freedom (DOF) vehicle model combined with a closed-loop driver model for developing vehicle lateral control. The proposed driver model consists of a yaw effect adaptive fuzzy logic control. Stepper Motor Actuated Steering (SMAS) system is briefly introduced as an inner-loop subsystem. The Software-in-the-Loop (SILS) and Hardware-in-the-Loop (HILS) results show that the proposed driver model is capable of improving the Y-axis trajectory error and Y-axis trajectory manoeuvres significantly and the proposed SMAS system is capable of tracking the desired steering angle position and producing the front wheel angle for the use of vehicle model.

2110 交差点黄信号時の車両・ドライバ系のモデリングに関する研究(OS6-1 フィールドデータに基づく設計・評価,OS6 ヒューマンファクタ,オーガナイズド・セッション(OS))

In Japan, many traffic accidents occur at intersections. Thus, the necessity of countermeasures for safety at intersections becomes evident in reducing traffic accident. Especially, it is thought that yellow light at signalized intersection is more dangerous because of driver's dilemma. In order to realize safe and reliable traffic environment, it is necessary to comprehend driver behavior. In this study, driver model which control vehicle stopping maneuvering at intersection during yellow phase is studied. Drivers characteristic parameters such as time delay, brake pedal stroke velocity during yellow light are identified. In order to evaluate the validity of proposed driver model, numerical simulation with some driver models combined with a vehicle model is elaborately compared with actual driving data.

811 交差点黄信号時における自動車・ドライバ系のモデリングに関する研究(一般講演-(4)《複合材料の変形と破壊およびマルチスケール計算技術》)

In order to realize safe and reliable traffic environment, safety technologies that focus on both individual driver and vehicle characteristics is necessary. In this study, driver model which is based on analyzed data with drive recorder is studied. Driver's behavior such as delay, brake pedal stroke, velocity of brake pedal during yellow light is analyzed. In order to evaluate the validity of proposed driver model, numerical simulation with the driver model combined with vehicle model is compared with actual driving data.

2928 超小型電気自動車の市街地走行データに基づく加減速行動モデルの構築(J15-1 交通機械のダイナミクス(1),ジョイントセッション,21世紀地球環境革命の機械工学:人・マイクロナノ・エネルギー・環境)

This paper proposes the modeling of longitudinal driving behavior based on urban driving data of one-person micro-scale electric vehicle. First, the urban driving data is collected by on-road experiments using continuous sensing drive recorder equipped in the micro-scale electric vehicle. Next, the human pedal operation for longitudinal vehicle control is divided into several modes. Consequently, the driver model is formulated by setting up the input-output relations of each operation modes and the mode switching rules based on the collected urban driving data. It is proved that the proposed driver model is effective to express driver pedal operations. Finally, the proposed driver model is used for detecting hurry driving by comparison between the model-based estimated and the measured pedal operation in hurry driving situation.

A passenger car driver model for higher lateral accelerations

Due to increasing demands for time and cost efficient vehicle and driver assistant systems development, numerical simulation of closed-loop manoeuvres becomes increasingly important. Thus, the driver has to be considered in the modelling. On the basis of a two-layer approach to model a driver's steering behaviour, the field of application is extended to higher lateral accelerations in this study. An analytical method to determine the driver parameters is presented, which is based on the two-wheel vehicle model. The simulation results are determined using a full vehicle model including all essential nonlinearities. Standard manoeuvres in the nonlinear range of vehicle handling behaviour are performed. A cornering manoeuvre is chosen to show the characteristics of the proposed driver model.

Characterizing Multistage Nonlinear Drivers and Variability for Accurate Timing and Noise Analysis

Nanoscale device characteristics and noise coupling have rendered traditional waveform-based gate delay models increasingly difficult to adopt. While the widely adopted delay models are built upon the assumption of simple ramp-like signal waveforms, realistic signal shapes in nanoscale designs can be far more complex. The need for considering process-voltage-temperature (PVT) variations imposes further accuracy requirement on gate models. We present a parameterizable waveform independent gate model (PWiM) where no assumption is made upon the input waveforms. The PWiM model is constructed by encapsulating the driver's intrinsic nonlinear dc and dynamic characteristics, which are important to model for complex signal waveforms, via novel and yet easy-to-implement characterization steps. As such, PWiM can provide near-SPICE accuracy for input signals that significantly deviate from simple ramps. While recently developed current-based models can only be applied to single channel-connected component, PWiM can work for multistage cells leading to improved library compactness and analysis efficiency. Our experiments have indicated that the proposed driver model not only provides up to two orders of magnitude speedups over SPICE for delay and noise analysis, it also offers accurate assessment of performance variability introduced by process and environmental variations.

An analytical driver model for arbitrary path following at varying vehicle speed

In this paper, an analytical driver model in the form of a simple artificial neural network is presented. Based on the preview-follower theory and error elimination algorithm, all the driver model parameters can be calculated analytically. In order to extend the driver model's application scope, all the driver parameters are fitted as the functions of vehicle speed, and a preview point searching algorithm is proposed to enable the driver model to follow an arbitrary designated path defined in ground coordinate. Driver/vehicle closed-loop simulations indicate that the proposed driver model is suitable for arbitrary path following at varying vehicle speed.

An adaptive lateral preview driver model

Successful modelling and simulation of driver behaviour is important for the current industrial thrust of computer-based vehicle development. The main contribution of this paper is the development of an adaptive lateral preview human driver model. This driver model template has a few parameters that can be adjusted to simulate steering actions of human drivers with different driving styles. In other words, this model template can be used in the design process of vehicles and active safety systems to assess their performance under average drivers as well as atypical drivers. We assume that the drivers, regardless of their style, have driven the vehicle long enough to establish an accurate internal model of the vehicle. The proposed driver model is developed using the adaptive predictive control (APC) framework. Three key features are included in the APC framework: use of preview information, internal model identification and weight adjustment to simulate different driving styles. The driver uses predicted vehicle information in a future window to determine the optimal steering action. A tunable parameter is defined to assign relative importance of lateral displacement and yaw error in the cost function to be optimized. The model is tuned to fit three representative drivers obtained from driving simulator data taken from 22 human drivers.