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arXiv 2202.01645cs.AIcs.HC
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面向自动驾驶中以人为中心的智能的AI即服务工具包

AI-as-a-Service Toolkit for Human-Centered Intelligence in Autonomous Driving

University of Pisa(比萨大学);National Research Council(国家研究委员会);Harokopio University of Athens(雅典哈洛克皮奥大学)
Valerio De Caro, Saira Bano, Achilles Machumilane, Alberto Gotta, Pietro Cassará, Antonio Carta, Rudy Semola, Christos Sardianos, Christos Chronis, Iraklis Varl… 展开作者

Valerio De Caro, Saira Bano, Achilles Machumilane, Alberto Gotta, Pietro Cassará, Antonio Carta, Rudy Semola, Christos Sardianos, Christos Chronis, Iraklis Varlamis, Konstantinos Tserpes, Vincenzo Lomonaco, Claudio Gallicchio, Davide Bacciu

摘要:

本文介绍了在H2020教学项目中开发的AI即服务工具包的概念验证实现,该工具包旨在根据自动驾驶员压力识别算法的输出来实现自动驾驶个性化系统,两者都实现了系统的网络物理系统。此外,我们还实现了一个数据收集子系统来收集来自不同传感器的数据,即,可穿戴设备和摄像头,来自动化压力识别。该系统被附加到驾驶模拟软件CARLA上进行测试,该软件可以以最低的成本测试该方法的可行性,而不会使驾驶员和乘客处于危险之中。在相关子系统的核心部分,分别采用深度神经网络、递归神经网络和强化学习实现了不同的学习算法。

英文摘要:

This paper presents a proof-of-concept implementation of the AI-as-a-Service toolkit developed within the H2020 TEACHING project and designed to implement an autonomous driving personalization system according to the output of an automatic driver's stress recognition algorithm, both of them realizing a Cyber-Physical System of Systems. In addition, we implemented a data-gathering subsystem to collect data from different sensors, i.e., wearables and cameras, to automatize stress recognition. The system was attached for testing to a driving simulation software, CARLA, which allows testing the approach's feasibility with minimum cost and without putting at risk drivers and passengers. At the core of the relative subsystems, different learning algorithms were implemented using Deep Neural Networks, Recurrent Neural Networks, and Reinforcement Learning.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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