A longitudinal driver model for long horizon speed prediction in powertrain applications

F. Morlock, O. Sawodny
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引用次数: 0

Abstract

Two major emerging fields of research in automotive engineering are autonomous driving and electromobility. The predictive or intelligent longitudinal control within the former and consumption forecasts for the latter are dependent on lookahead data provided by cloud based services as real time road and traffic data. Furthermore, these applications can be improved by customization to the driver. This paper proposes a simple, yet accurate parametric model for longitudinal driving characteristics which is designed for use in powertrain applications that could be a predictive, intelligent cruise controller or a personalized consumption forecast. A methodology for offline identification of the model parameters is presented that can be easily transferred to online implementation. The model is validated against measurement data and meaningful metrics for assessing its performance are introduced. It is shown that predicted speed yields good resemblance to measurements.
一种用于动力系统长视界速度预测的纵向驾驶员模型
汽车工程的两个主要新兴研究领域是自动驾驶和电动汽车。前者的预测性或智能纵向控制以及后者的消费预测依赖于基于云的服务提供的前瞻性数据,即实时道路和交通数据。此外,这些应用程序可以通过定制驱动程序来改进。本文提出了一个简单而准确的纵向驾驶特性参数模型,该模型可用于动力系统应用,可作为预测性智能巡航控制器或个性化消费预测。提出了一种离线识别模型参数的方法,该方法可以很容易地转移到在线实现中。通过实测数据对模型进行了验证,并介绍了评估模型性能的有意义的指标。结果表明,预测的速度与测量值有很好的相似性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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