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首页> 外文会议>International Joint Conference on Neural Networks >Design and optimization of a Neural Network-based driver recognition system by means of a multiobjective genetic algorithm
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Design and optimization of a Neural Network-based driver recognition system by means of a multiobjective genetic algorithm

机译：基于神经网络的驾驶员识别系统的多目标遗传算法设计与优化

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Advanced Driving Assitance Systems (ADAS) cover a wide range of systems that aim to provide increasingly a safe and efficient driving. Many of these systems are endowed with some intelligent skills which are, in many cases, addressed by means of Soft Computing (SC) paradigms like Neural Networks (NN) or fuzzy systems among others. However, SC algorithms require normally large computational resources which are incompatible with the kind of the electronic systems that can be deployed in cars where the size, cost and power consumption are always very restrictive. In this paper we present a NN-based driving recognition system, able to model the driving style of different drivers. Such a system could be used to detect abnormal driving behaviours and hence, to avoid dangerous situations. In order to obtain an optimized network in terms of size and complexity, we make the design by using a multiobjective genetic algorithm that provides at the same time a reduced number of input variables and a low number of neurons in the network. To make feasible the operation of the algorithm, we select Extreme Learning Machines as NNs as they have a learning mechanism very fast and precise.
机译：高级驾驶辅助系统（ADAS）涵盖了广泛的系统，旨在提供越来越安全和高效的驾驶。这些系统中的许多系统都具有一些智能技能，在许多情况下，可以通过诸如神经网络（NN）或模糊系统之类的软计算（SC）范例来解决。但是，SC算法通常需要大量的计算资源，这与可部署在尺寸，成本和功耗始终非常受限的汽车中的电子系统不兼容。在本文中，我们提出了一种基于NN的驾驶识别系统，能够对不同驾驶员的驾驶风格进行建模。这样的系统可用于检测异常驾驶行为，从而避免危险情况。为了在大小和复杂度方面获得优化的网络，我们通过使用多目标遗传算法进行设计，该算法同时提供了减少的输入变量数量和较少的神经元数量。为了使算法的操作可行，我们选择极限学习机作为神经网络，因为它们具有非常快速和精确的学习机制。

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