The Implementation of Driver Model Based on the Attention Transfer Process

Hindawi Mathematical Problems in Engineering Volume 2017, Article ID 3714254, 15 pages https://doi.org/10.1155/2017/3714254 Research Article The Implementation of Driver Model Based on the Attention Transfer Process ShuanFeng Zhao and Wei Guo School of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, China Correspondence should be addressed to ShuanFeng Zhao; and Wei Guo; Received 28 July 2016; Revised 15 November 2016; Accepted 27 December 2016; Published 5 March 2017 Academic Editor: Michele Betti Copyright © 2017 ShuanFeng Zhao and Wei Guo. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. To describe the characteristics of driver’s attention changing with driving environment, establish the relation between driver model parameter and driver’s attention, seek for mapping relation between driver’s behavior and vehicle’s running status data, and provide individualized driver simulation model for unmanned car controller or for driver’s mental state inversion based on vehicle’s running status data, the paper established a driver model based on driver’s attention and deduced the relation between attention intensity and continuous driving time according to the process of driver’s attention change from concentration to distraction and the distribution characteristics of their durations. The relationship between driver’s mental state and manual closed-loop driving model parameters is established according to the transfer rule of attention in the driving course, and it is applied to driver model based on dynamical regulation neural network. Finally the paper researched dynamics evolution characteristics of vehicle running caused by fatigue driving in the environment of double lane change and large curvature, with test result verifying the effectiveness and accuracy of the driver model based on the attention transfer process. 1. Introduction With the rapid development of artificial intelligence, breakthrough has been made in research on unmanned car, which is a smart auto that perceives road environment through on-board sensing system, automatically plans travel way, and controls the vehicle to arrive at predetermined destination. The unmanned vehicle technology is the fruit of high development of computer science, pattern recognition, and intelligent control technology and also an important mark to measure a country’s scientific research strength and industrial level, with wide application prospects in national defense and economy domain. The on-board control system of unmanned technology can be regarded as a virtual driver, and to evaluate the performance of unmanned control system, the behavior characteristics of real driver’s controlling of vehicle need to be considered. Development of unmanned technology provides diversified research angles for the research on characteristics of driver, while the research on characteristics of driver can provide the theoretical support for unmanned technology in terms of principle of driver’s characteristic to boost development of unmanned technology. When verifying principle of unmanned car’s control module, it needs to use principle model or experiment vehicle or to be conducted on virtual driving simulator of high-performance vehicle. These experiments are relatively time-consuming and costly. Another option is to establish proper driver’s behavioral model to substitute real driver from the perspective of computer simulation and substitute costly and time-consuming real-car experiment, thereby greatly shortening R&D cycle of the onboard control system. Thus, how to establish a mathematical model conforming to driver’s behavioral characteristic is the core problem in the research. Driver’s driving behaviors can be divided into the longitudinal behavior and the horizontal behavior. The former one refers to the driver’s accelerate-skid operating characteristic and car-following to keep safe driving, while the latter one refers to driver’s turning corner and steering wheel operation to change the lane. Existing development progress for modeling approach of longitudinal driving behavior includes the following: during 2003∼2004, Brackstone issued 2 several papers and systematically put forward the theory of describing longitudinal driving behavior using comprehensive safe interval holding mode and vehicle’s longitudinal following model [1–3]. Longitudinal driving model can be regarded as a follow-up control system, thereinto, input is the safe interval expected by the driver, and output is actual safe interval. The controller (driver) controls throttle brake or gear according to difference between input and output to realize longitudinal following of target vehicle. Generally just two-order lag frequency model can reflect the driver’s characteristics (hesitation period), reaction speed, and so forth. To reflect longitudinal driving characteristics at different running speeds, the high order lag frequency model can be adopted [4]. In practical application of longitudinal running behavioral model, the interference problems caused by measurement noise and habitual movements must be solved. To cope, some researchers apply Kalman filter, wavelet analysis based on multiscale, shape filter, and so forth for pretreatment. In offline research, progressive mean method can also be used to remove the above interference. The above mathematical modeling research is all based on the mode of analysis. To match fuzzy behavior in driver’s longitudinal operation behavior, some scholars put forward fuzzy control system to describe driver’s behavior. This method is to fuzzify the methods of longitudinal driving rule utilization membership grade function and the domain of discourse, in practical application to establish fuzzy controller of longitudinal driving behavior to improve adaptability of vehicle’s longitudinal following system [5]. The existing progress for modeling approach of horizontal driving behavior includes the following: in 1981, MacAdam put forward optimal preview control (OPC) in which all parameters except preview time can be defined by automobile dynamics [6]. This model is derived based on the condition of orbit’s follow sum squared error, with orbit error follow precision able to meet physical demand. In 1993, Guo and Guan put forward preview optimization directional control driver model [7]. In 1996 MacAdam and Johnson put forward motor steering intelligent control system based on neural network and preview sensor [8], which obtained data of vehicle’s position relative to road boundary and operational data of steering wheel through image sensor and then used artificial neural network to learn the obtained sample data. The neural network after learning can simulate the driver’s horizontal driving behavior. In 2003 academician Guo and his s (...truncated)