Model based off-road terrain profile estimation

Abstract

This paper investigates a method for estimating an off-road terrain profile using a vehicle suspension model. An augmented state Kalman filter (ASKF) is presented as a means to estimate the unknown inputs of the 7-DOF full suspension model. The Weierstrass-Mandelbrot function was used to generate a fractal terrain surface for a vehicle simulation. The terrain surface was used with Carsim in a simulation to test the proposed estimation method. To further validate the method an experimental study was conducted on an off-road terrain. A Light Detection and Ranging (LiDAR) sensor was used to measure the terrain profile. Global positioning system (GPS), inertial navigation sensors (INS) and suspension deflection sensors were used to as measurements to the ASKF. It is shown that the terrain profile can be estimated as the input to the 7-DOF suspension model. The method is effective at capturing the low frequency content of the profile while some of the higher frequency content cannot be recovered.

A study on a vehicle semi-active suspension control system based on road elevation identification

“…The third category includes an estimation method that is based on the dynamic response of suspension systems. The traditional method [11][12][13][14][15] estimates road conditions using the Kalman filter estimation algorithm or synovial observer [16,17]. In Reference [18], a method for estimating the road height using the inverse model of the suspension system and the dynamic response of the suspension system was proposed.…”