Estimating Road Angles With the Knowledge of the Vehicle Yaw Angle

Abstract

This paper presents a method of estimating road angles using state observers and three types of sensors (lateral acceleration sensors, longitudinal velocity sensors, and suspension displacement sensors). The proposed method differs from those in most existing literature in three aspects. First, a “full-state” vehicle model is used to describe nonlinear vehicle dynamics on a sloped road. Second, “switching observer” techniques are used to suggest suitable sensors and to construct state observers. Lastly, the road angles are described by three Euler angles, and two of them are estimated simultaneously. The analysis indicates that (1) road angles affect vehicle dynamics through components of the gravitational force acting on the vehicle body. These gravitational forces can be correctly estimated with an estimation accuracy less than 7.5%, even when road angles vary with time. (2) Those road angles can be correctly estimated only when the vehicle yaw angle is known.

Target fast recognition and risk identification based on dynamic programming

“…A positive value means that the vehicle tends to drive right. According to the fluctuating angle of yaw angle value while driving in straight lanes [13] [14] and the threshold of the existing systems [15] , choose 5 as the threshold value. Judge whether the vehicle is changing lane according to the result of lane line extraction.…”

Real-time estimation of the road bank and grade angles with unknown input observers

“…Roll dynamic parameters are also identified in their method. Hsu and Chen in [15] provide a modelbased estimation approach for the road angles. Their method combines multiple roll and pitch models and a switching observer scheme.…”

Road Slope Estimation in Bicycles without Torque Measurements

“…The issue of road slope estimation in vehicular applications is not new. Most of the wheeled vehicles literature focus on 4-wheeled vehicles (Hsu and Chen (2010), Sebsadji et al (2008), Schmidtbauer and Lingman (2003)), especially Heavy Duty Vehicles, in which mass can vary significantly (Vahidi et al (2003a), Vahidi et al (2003b), Johansson (2005)). These estimations rely on a vehicle longitudinal dynamic model (see Bae et al (2001), Han and Rizos (1999), Sahlholm and Henrik Johansson (2010), Johansson (2005), Sahlholm et al (2007a), Sahlholm et al (2007b) ).…”