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 this study 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 a model opens up interesting perspectives for the improvement of rider assistances.

中文翻译：

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车手模型识别：神经网络和准LPV模型

有趣的是，先进的车手辅助系统（ARAS）的当前开发将受益于精确的车手建模。不幸的是，与摩托车骑手建模有关的重要问题仍未得到解答。这项研究的目的是提出一种适用于面向ARAS的应用程序的原始控制论模型。识别过程基于使用实际摩托车在实际驾驶条件下记录的实验数据。从动态神经网络开始，所提出的方法首先提出了非线性骑手模型。通过对该模型的分析以及与汽车驾驶员模型的一些类比，可以得出具有明显速度依赖性以及线性和非线性动力学之间明显区别的准线性参数变化（quasi-LPV）驾驶员模型。对该准LPV模型进行了进一步分析和简化，最终形成了参数数量减少且具有良好预测能力的车手模型。这样的模型为改善骑手辅助打开了有趣的前景。