智能汽车认知信息娱乐系统

Ilias E. Panagiotopoulos, G. Dimitrakopoulos
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引用次数: 3

摘要

最近,汽车世界见证了一种与车内智能交通系统(ITS)的部署和广泛使用相关的趋势。这样的系统会产生大量的实时数据,这些数据需要进行管理、交流、解释、汇总和分析,以支持实时决策能力。此外,汽车行业还引入了车载信息娱乐(IVI)系统,为驾驶员/乘客提供多种功能支持。本研究的主要目的是提出一种IVI认知功能,该功能可以自动动态地为驾驶员/用户提供最佳的音乐类型。拟议的系统利用(i)司机/用户的个人资料数据和他/她的现状,(ii)司机/用户的个人偏好,(iii)从传感器测量中获得的外部环境信息,以及(iv)以前的知识,以自动的方式转化为经验。知识是通过利用基于贝叶斯网络原理的机器学习技术获得的。指示性模拟结果展示了上述IVI认知功能在主动识别最佳音乐类型并相应地通知驾驶员/用户方面的行为。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Cognitive Infotainment Systems for Intelligent Vehicles
Lately, the automotive world is witnessing a trend related to the deployment and extensive use of Intelligent Transportation Systems (ITS) inside vehicles. Such systems generate large volumes of real-time data that need to be managed, communicated, interpreted, aggregated, and analyzed in order to support real-time decision-making capabilities. Furthermore, automotive industries have introduced In-Vehicle-Infotainment (IVI) systems in supporting drivers/passengers with a varying set of functions. The main purpose of this study is to present an IVI cognitive functionality that automatically and dynamically proposes the optimum music genre to the drivers /users when they want to travel with their vehicles. The proposed system utilizes (i) driver/user’s profile data and his/her current situation, (ii) driver/user’s personal preferences, (iii) external environment information obtained from sensor measurements, and (iv) previous knowledge, turned into experience, in an automated manner. Knowledge is obtained through the exploitation of a machine learning technique based on the Bayesian networking principles. Indicative simulation results showcase the behavior of the aforementioned IVI cognitive functionality in proactively identifying the optimal music genre and accordingly notifying the driver/user.
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