Driver Assistance Systems (DAS) have been progressively incorporated into commercial vehicles in recent years. All these systems are paving the way for the forthcoming autonomous vehicle which will become a reality in the near future. Existing systems are based on numerous electronic systems with advanced skills, high performances, and high degrees of adaptability and intelligence. As is to be expected, these cutting-edge features require, in most cases, the use of powerful computing platforms. However, the deployment of such platforms is not an easy task, since they have to be integrated in the vehicle where there exist important restrictions regarding size, power consumption and cost. In this sense, every smart proposal aimed at reducing the complexity of these systems without degrading performance, is always a valuable contribution in the field. In this work, we propose a methodology to reduce the dimensionality of a driver distraction recognition system. The methodology is based on a multi-objective genetic algorithm that looks for the minimum set of useful features collected during the driving task and also for the simplest recognition system. The recognition algorithm is an Extreme Learning Machine (ELM) whose simplicity and fast learning procedure make it especially suitable to be used by a Genetic Algorithm which needs to evaluate thousands of candidate solutions. The proposed methodology has been tested with a real-world database collected from different drivers performing an itinerary with an instrumented car. The results obtained validate the proposal as a method to reduce the complexity of a driver distraction recognition system.

中文翻译：

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通过模仿示例句子进行语法定制视频字幕


近年来，驾驶员辅助系统（DAS）已逐渐融入商用车中。所有这些系统都为即将到来的自动驾驶汽车铺平了道路，这将在不久的将来成为现实。现有系统基于众多具有先进技术、高性能、高度适应性和智能的电子系统。正如预期的那样，在大多数情况下，这些尖端功能需要使用强大的计算平台。然而，部署此类平台并非易事，因为它们必须集成在车辆中，而车辆在尺寸、功耗和成本方面存在重要限制。从这个意义上说，每一个旨在降低这些系统的复杂性而不降低性能的明智提案始终是该领域的宝贵贡献。在这项工作中，我们提出了一种降低驾驶员分心识别系统维度的方法。该方法基于多目标遗传算法，该算法寻找驾驶任务期间收集的最小有用特征集以及最简单的识别系统。识别算法是一种极限学习机（ELM），其简单且快速的学习过程使其特别适合需要评估数千个候选解决方案的遗传算法使用。所提出的方法已经通过从使用仪表汽车执行行程的不同驾驶员收集的真实数据库进行了测试。获得的结果验证了该提案作为降低驾驶员分心识别系统复杂性的方法的有效性。