Abstract
This paper presents an in-depth analysis of the application of different techniques for vehicle state and tyre force estimation using the same experimental data and vehicle models, except for the tyre models. Four schemes are demonstrated: (i) an Extended Kalman Filter (EKF) scheme using a linear tyre model with stochastically adapted cornering stiffness, (ii) an EKF scheme using a Neural Network (NN) data-driven linear tyre model, (iii) a tyre model-less Suboptimal-Second Order Sliding Mode (S-SOSM) scheme, and (iv) a Kinematic Model (KM) scheme integrated in an EKF. The estimation accuracy of each method is discussed. Moreover, guidelines for each method provide potential users with valuable insight into key properties and points of attention.
Highlights
Advanced Driver Assistance Systems (ADAS) as well as Automated Driving (AD) technologies are being increasingly implemented in mass market vehicles, aiming for improved driving safety and passenger comfort [1]
The four approaches have been selected to compare rather traditional with more recent techniques: Kalman Filter based (KF) only requires the well-established Extended Kalman Filter (EKF); NN employs Neural Networks for tyre modelling; Suboptimal-Second Order Sliding Mode (S-SOSM) is based on a recently proposed enhancement of Sliding Mode Control; Kinematic Model (KM) makes use of quaternion notation for ground vehicle state estimation which has not been demonstrated before
Suboptimal-Second Order Sliding Mode and Extended Kalman Filter approach. This approach consists of two stages: the first one is a tyre force observer relying on the so-called Suboptimal-Second Order Sliding Mode (S-SOSM) [49]; the second stage is an EKF enhancement allowing to smoothen the S-SOSM estimates alongside the sensor signals
Summary
Advanced Driver Assistance Systems (ADAS) as well as Automated Driving (AD) technologies are being increasingly implemented in mass market vehicles, aiming for improved driving safety and passenger comfort [1]. This work deals with techniques estimating tyre forces and planar motion states based on readily available measurement data and models. Those virtual sensors have clear benefits over their physical counterparts such as being far less intrusive and expensive. Linear model parameters can be estimated stochastically [21] or using sliding modes Another option are data-driven techniques, for example Neural Networks (NN) [22,23,24,25]. A body kinematics model is used in combination with suspension position (zij) sensors to calculate body angles required to correct IMU data when used in the planar states estimators. The employed estimation schemes are anticipated to work for backward driving
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