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. 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 driver model was able to perform a good tracking behavior even at higher lateral accelerations. presented a driver model for higher lateral accelerations. For the sake of simulating driver behaviors under some severe or critical scenarios, Edelmann et al. Further research showed that the weight factors of this artificial neural network could be calculated analytically through the Error Elimination Algorithm. The global optimization of the closed-loop system was carried out in the training process of the network through the Genetic Algorithm. proposed a preview optimal artificial neural network (ANN) driver model, whose training sample was the ideal following path instead of experimental data. Sampled data collected by the sensors of an on-road car was employed to train the network. In addition, Macadam and Johnson constructed a two-layer neural network to represent driver steering behaviors. This driver model was trained with measured human-driving data.
also proposed a neurocontroller for lateral vehicle guidance.
Best offset driver 2013 simulator#
The model was trained by a human driver in a simulator environment. presented a NN driver model, in which the steering angle was mapped as a function of lateral deviation and heading angle. With the development of intelligence technology, several artificial neural network driver models were proposed in order to accurately imitate human driving 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. Moreover, the Preview-Follower theory was proposed for the purpose of modeling the driver’s path-following behaviors. Macadam established a driver model by using optimal preview closed-loop control in 1980. However, the preview characteristics of the driver were not taken into consideration in the studies. In addition, McRuer and Jex extended pilot models to road driver models by considering the factors of reaction time and inertial delay and a compensation driver model was presented.
His research is based on the 2-wheel vehicle model on a straight line, running at a constant speed with side wind disturbances. In 1953, Kondo started with driver modeling in Japan. This research field has drawn significant attention and several typical models have been carried out by many researchers in an early time. In order to evaluate the handling quality of a vehicle and avoid potential risk in actual tests, the study on driver modeling is essential. Previous studies reveal that the driver-vehicle-road closed-loop system works effectively when investigating the performances of vehicle handling and stability. IntroductionĪlthough 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. Simulation results indicate that the jerky dynamics need to be considered and the proposed new driver model can achieve a better path-following performance compared with the POSANN driver model. Finally, the simulations are carried out via CarSim. 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. In this paper, the modeling for the driver-vehicle system is firstly described, and the relationship between weighting coefficients of driver model and system parameters is examined through test data. 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.
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