Characteristics Extraction of Vehicle State Information Based on Entropy Calculation

Abstract

A method of extracting and detecting vehicle stability state characteristics based on entropy is proposed. The vehicle’s longitudinal and lateral dynamics models are established for complex driving and maneuver conditions. The corresponding state observer is designed by adopting the moving horizon estimation algorithm, which realizes the observation of the vehicle stability state considering the global state information. Meanwhile, the Shannon entropy is modified to approximate entropy, and the approximate entropy value of the observed vehicle state is calculated. Furthermore, the optimal controller is designed to further validate the reliability of the entropy value as the reference of control system. Simulation results demonstrate that this method can quickly detect the instability state of the system during the process of vehicle driving, which provides a reference for risk prediction and active control.

Keywords

vehicle stability state / state observer / moving horizon estimation / Shannon entropy / approximate entropy