Vehicle Dynamic Prediction Systems with On-Line Identification of Vehicle Parameters and Road Conditions

Ling-Yuan Hsu,Tsung-Lin Chen

doi:10.3390/s121115778

Abstract

This paper presents a vehicle dynamics prediction system, which consists of a sensor fusion system and a vehicle parameter identification system. This sensor fusion system can obtain the six degree-of-freedom vehicle dynamics and two road angles without using a vehicle model. The vehicle parameter identification system uses the vehicle dynamics from the sensor fusion system to identify ten vehicle parameters in real time, including vehicle mass, moment of inertial, and road friction coefficients. With above two systems, the future vehicle dynamics is predicted by using a vehicle dynamics model, obtained from the parameter identification system, to propagate with time the current vehicle state values, obtained from the sensor fusion system. Comparing with most existing literatures in this field, the proposed approach improves the prediction accuracy both by incorporating more vehicle dynamics to the prediction system and by on-line identification to minimize the vehicle modeling errors. Simulation results show that the proposed method successfully predicts the vehicle dynamics in a left-hand turn event and a rollover event. The prediction inaccuracy is 0.51% in a left-hand turn event and 27.3% in a rollover event.

Highlights

In recent years, many vehicle control research propose using the future vehicle dynamics information to assist drivers’ maneuvers
In our previous work [20], we propose a kinematic based senor fusion system that employs a three-antenna global positioning system (GPS), an inertial measurement unit (IMU), and four suspension displacement sensors
This sensor fusion system can obtain the 6 DOF vehicle dynamics and two road angles. Based on this sensor system, we develop a vehicle dynamics prediction system in this paper using a vehicle model that is more complicated than most of the existing approaches

Summary

Introduction

Many vehicle control research propose using the future vehicle dynamics information to assist drivers’ maneuvers. The vehicle path predictions can provide the future position error for the vehicle guidance controls. Compared with the conventional “look-down” sensing system that provides current position error, the path prediction provides the information that are easier perceived by human drivers, and provides additional information of road conditions, weather conditions, etc. [1,2] As another example, many researchers propose using vehicle rollover predictions as anti-rollover measures [3,4,5]. The advance of the control action both saves actuation power and improves the driving safety. These examples highlight the importance of vehicle dynamics predictions

Results

Discussion

Conclusion