Rider model identification: neural networks and quasi‐LPV models

Abstract

The current development of Advanced Rider Assistance Systems (ARAS) would interestingly benefit from precise human rider modelling. Unfortunately, important questions related to motorbike rider modelling remain unanswered. The goal of the present paper is to propose an original cybernetic rider model suitable for ARAS oriented applications. The identification process is based on experimental data recorded in real driving conditions with an instrumented motorbike. Starting with a dynamic neural network, the proposed methodology firstly presents a non-linear rider model. The analysis of this model and some analogies with car driver modelling allow to deduce a quasi Linear Parameter Varying (quasi-LPV) rider model with explicit speed dependence and a clear distinction between linear and non-linear dynamics. This quasi-LPV model is further analysed and simplified and finally leads to a rider model with a reduced number of parameters and nice prediction capabilities. Such model opens up interesting perspectives for the improvement of rider assistances.

A Two-Phase Approach for Predicting Highway Passenger Volume

“…The DFNN is a deep learning model comprised of an input layer, several hidden layers, and an output layer [42][43][44]. The quantity of hidden layers defines the depth of the architecture [45]. The topological structure of the DFNN is shown in Figure 2.…”

“…The topological structure of the DFNN is shown in Figure 2. The theory of the DFNN is available in past research [44][45][46]. In this section, we introduce the activation function and objective function used in the DFNN algorithm.…”

Powered Two-Wheeler Riding Profile Clustering for an In-Depth Study of Bend-Taking Practices

“…In the first category, the most important topics addressed are the handlebar control (steering torque versus steering angle), dominance of different types of control (handlebars versus rider lean) [ 4 , 5 ], and differences between experienced and novice riders [ 6 ]. More recently, in [ 7 , 8 ] the authors addressed the problem of motorcycle rider model identification based on experimental data recorded during road tests with a fully instrumented motorcycle.…”