A Driver Modeling Based on the Preview-Follower Theory and the Jerky Dynamics

Hindawi Publishing Corporation Mathematical Problems in Engineering Volume 2013, Article ID 952106, 10 pages http://dx.doi.org/10.1155/2013/952106 Research Article A Driver Modeling Based on the Preview-Follower Theory and the Jerky Dynamics Jianyong Cao,1,2 Hui Lu,1 Konghui Guo,1 and Jianwen Zhang2 1 2 School of Mechanical Engineering, Shanghai Jiaotong University, Shanghai 200240, China Shanghai Motor Vehicle Inspection Center, Shanghai 201805, China Correspondence should be addressed to Konghui Guo; Received 14 September 2013; Revised 15 November 2013; Accepted 17 November 2013 Academic Editor: Hui Zhang Copyright © 2013 Jianyong Cao et al. 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. Based on the preview optimal simple artificial neural network driver model (POSANN), a new driver model, considering jerky dynamics and the tracing error between the real track and the planned path, is established. In this paper, the modeling for the drivervehicle system is firstly described, and the relationship between weighting coefficients of driver model and system parameters is examined through test data. Secondly, the corresponding road test results are presented in order to verify the vehicle model and obtain the information on drive model and vehicle parameters. Finally, the simulations are carried out via CarSim. Simulation results indicate that the jerky dynamics need to be considered and the proposed new driver model can achieve a better pathfollowing performance compared with the POSANN driver model. 1. Introduction Although the chassis control systems of a vehicle can improve vehicle dynamics performances, enhance active safety, and reduce driver load, they bring more challenges for the evaluation of vehicle performance, especially for the evaluation of handling and stability in terms of subjective sense [1, 2]. Previous studies [3, 4] reveal that the driver-vehicle-road closed-loop system works effectively when investigating the performances of vehicle handling and stability. In order to evaluate the handling quality of a vehicle and avoid potential risk in actual tests, the study on driver modeling is essential. This research field has drawn significant attention and several typical models have been carried out by many researchers in an early time. In 1953, Kondo [5] started with driver modeling in Japan. His research is based on the 2-wheel vehicle model on a straight line, running at a constant speed with side wind disturbances. In addition, McRuer and Jex [6] extended pilot models to road driver models by considering the factors of reaction time and inertial delay and a compensation driver model was presented. However, the preview characteristics of the driver were not taken into consideration in the studies. Macadam [7] established a driver model by using optimal preview closed-loop control in 1980. Moreover, the Preview-Follower theory [8] was proposed for the purpose of modeling the driver’s path-following behaviors. The driver’s behaviors were assumed based on the path-following theory in which the driver’s operation always aims at minimizing the errors between the desired and actual vehicle trajectory [9]. With the development of intelligence technology, several artificial neural network driver models were proposed in order to accurately imitate human driving behaviors. For instance, Fujioka et al. [10] presented a NN driver model, in which the steering angle was mapped as a function of lateral deviation and heading angle. The model was trained by a human driver in a simulator environment. Neusser et al. [11] also proposed a neurocontroller for lateral vehicle guidance. This driver model was trained with measured human-driving data. In addition, Macadam and Johnson [12] constructed a two-layer neural network to represent driver steering behaviors. Sampled data collected by the sensors of an on-road car was employed to train the network. Guo et al. [13] proposed a preview optimal artificial neural network (ANN) driver model, whose training sample was 2 the ideal following path instead of experimental data. The global optimization of the closed-loop system was carried out in the training process of the network through the Genetic Algorithm. Further research showed that the weight factors of this artificial neural network could be calculated analytically through the Error Elimination Algorithm [14]. For the sake of simulating driver behaviors under some severe or critical scenarios, Edelmann et al. [15] presented a driver model for higher lateral accelerations. The driver model was able to perform a good tracking behavior even at higher lateral accelerations. Tracking accuracy was further enhanced by incorporating information on the change of curvature and the local curvature of vehicle motion in the prediction of anticipated vehicle positions. The above-mentioned models were established based on the driver’s visual sensory inputs; kinesthetic (steering torque) or vestibular (lateral acceleration, yaw rate, and slip angle) sensory was not taken into account. Recently, some driver models were proposed in [16–18]; those models integrate both anticipatory and compensatory visual strategies and take into account both visual perception and kinesthetic perception. Little amount of the literature gives information on how vestibular information is used. Many driver models have been established based on many kinds of modern control theories and methods. Unlike acceleration, velocity, and displacement of vehicle, the time derivative of acceleration (TDoA) of vehicle motion, which is used to show the vestibular information, has not been extensively studied in those studies. The TDoA, also referred to in the literature as jerk, is one of the parameters considered in vibration control [19] and comfort evaluation [20, 21]. It is a physical property that is felt by humans when a sudden change of motion occurs. Consequently, it is closely monitored for discomfort caused to a passenger in a vehicle. As a sudden change of motion occurs, the vehicle might drop into the boundary area of stability, and the tire forces are prone to sudden change. The driver response can be made according to the steering torque feedback and the jerk dynamics on the vehicle response to lateral force change. Thus, the optimal preview control driver model cannot achieve accurate vehicle performance, especially when the tires are in the sudden change conditions. Hence, it is essential to consider the TDoA of vehicle motion in the preview-follower driver model. Based on the preview-follower driver modeling approach and ANN, a modified driver model considering the jerky dynamics is investigated. The drivers’ behaviors are described with the parameters of preview and jerk characteristic. The steering angle is obtained according to the (...truncated)