Characterizing human driver characteristics using an artificial neural network and a theoretical model

关键词:  Neural network · Identification · Driver model · Chassis dynamometer · Driver characteristics

Key words:  Neural network · Identification · Driver model · Chassis dynamometer · Driver characteristics

摘要:

Human drivers seem to have different characteristics, so different drivers often yield different results from the same driving mode tests with identical vehicles and same chassis dynamometer. However, drivers with different experiences often yield similar results under the same driving conditions. If the features of human drivers are known, the control inputs to each driver, including warnings, will be customized to optimize each man–machine vehicle system. Therefore, it is crucial to determine how to characterize human drivers quantitatively. This study proposes a method to estimate the parameters of a theoretical model of human drivers. Themethod uses an artificial neural network (ANN) model and a numerical procedure to interpret the identified ANN models theoretically. Our approach involves the following process. First, we specify each ANN driver model through chassis dynamometer tests performed by each human driver and vehicle. Subsequently, we obtain the parameters of a theoretical driver model using the ANN model for the corresponding driver. Specifically, we simulate the driver’s behaviors using the identified ANN models with controlled inputs. Finally, we estimate the theoretical driver model parameters using the numerical simulation results. A proportional-integral-differential (PID) control model is used as the theoretical model. The results of the parameter estimation indicate that the PID driver model parameter combination can characterize human drivers. Moreover, the results suggest that vehicular factors influence the parameter combinations of human drivers.

Abstract:

Human drivers seem to have different characteristics, so different drivers often yield different results from the same driving mode tests with identical vehicles and same chassis dynamometer. However, drivers with different experiences often yield similar results under the same driving conditions. If the features of human drivers are known, the control inputs to each driver, including warnings, will be customized to optimize each man–machine vehicle system. Therefore, it is crucial to determine how to characterize human drivers quantitatively. This study proposes a method to estimate the parameters of a theoretical model of human drivers. Themethod uses an artificial neural network (ANN) model and a numerical procedure to interpret the identified ANN models theoretically. Our approach involves the following process. First, we specify each ANN driver model through chassis dynamometer tests performed by each human driver and vehicle. Subsequently, we obtain the parameters of a theoretical driver model using the ANN model for the corresponding driver. Specifically, we simulate the driver’s behaviors using the identified ANN models with controlled inputs. Finally, we estimate the theoretical driver model parameters using the numerical simulation results. A proportional-integral-differential (PID) control model is used as the theoretical model. The results of the parameter estimation indicate that the PID driver model parameter combination can characterize human drivers. Moreover, the results suggest that vehicular factors influence the parameter combinations of human drivers.