Driver Behavior Characteristics Identification Strategies Based on Bionic Intelligent Algorithms

Abstract

The exact and reliable understanding of driver behavior characteristics has a large potential contribution to the active safety control system, advanced driver assistance system, and intelligent traffic system. This study presents a driver behavior characteristics identification strategy based on bionic intelligent algorithms. First, a driver behavior data acquisition system is established to be used with test subjects of different driving skill levels for driving data acquisition. The preview optimal curvature model, which directly reflects the driver's behavior characteristics based on its parameters, is then chosen as the ideal driver behavior model and is identified through the genetic algorithm, particle swarm optimization, and back propagation neural network. Moreover, this paper discusses the application of driver behavior characteristics identification in an integrated chassis control system (ICC), which integrates active front steering with electronic stability control. Finally, simulations are performed to verify the identification results and the application in the ICC by PanoSim and MATLAB/Simulink software. Based on the results, the proposed identification strategy precisely characterized the driver behavior. As a result, its application in the ICC improved the path-following ability and vehicle stability performance of the drivers.