摘要翻译：
通过使用车载传感和外部连接技术，互联自动化车辆(CAV)可以提高能源效率，改善路由，降低交通拥堵。随着CAV技术的快速发展和适应性的不断提高，开发一套通用的评价方法和测试标准，能够公正地评价CAV对能源消耗和环境污染的影响，尤其是在不同的交通条件下，显得尤为重要。本文提出了一种基于无监督学习方法的汽车能效和排放评价方法和框架。该方法利用被评估车辆的真实驾驶数据和大型自然驾驶数据集进行驾驶基元分析和耦合。然后用线性加权估计法计算出被评估车辆的测试结果。结果表明，该方法能够成功地识别出典型的驱动基元。来自被评估车辆的驾驶基元和来自大型真实驾驶数据集的典型驾驶基元之间的耦合非常吻合。这种新方法可以促进CAV和其他非循环信用的能效和排放测试的标准发展。
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英文标题：
《Energy Efficiency and Emission Testing for Connected and Automated
  Vehicles Using Real-World Driving Data》
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作者：
Yan Chang, Weiqing Yang, Ding Zhao
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最新提交年份：
2018
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Other Computer Science        其他计算机科学
分类描述：This is the classification to use for documents that do not fit anywhere else.
这是用于不适合其他任何地方的文档的分类。
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一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Signal Processing        信号处理
分类描述：Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用，包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、（生物）医学、图像、视频和多模态自然和人为信号，包括通信信号和数据。感兴趣的主题包括：统计信号处理、谱估计和系统辨识；滤波器设计；自适应滤波/随机学习；（压缩）采样、传感和变换域方法，包括快速算法；用于机器学习的信号处理和用于信号处理应用的机器学习；网络与图形信号处理；信号处理中的凸和非凸优化方法；雷达、声纳和传感器阵列波束形成和测向；通信信号处理；低功耗、多核、片上系统信号处理；信息物理系统的传感、通信、分析和优化，如电网和物联网。
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一级分类：Statistics        统计学
二级分类：Applications        应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
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英文摘要：
  By using the onboard sensing and external connectivity technology, connected and automated vehicles (CAV) could lead to improved energy efficiency, better routing, and lower traffic congestion. With the rapid development of the technology and adaptation of CAV, it is more critical to develop the universal evaluation method and the testing standard which could evaluate the impacts on energy consumption and environmental pollution of CAV fairly, especially under the various traffic conditions. In this paper, we proposed a new method and framework to evaluate the energy efficiency and emission of the vehicle based on the unsupervised learning methods. Both the real-world driving data of the evaluated vehicle and the large naturalistic driving dataset are used to perform the driving primitive analysis and coupling. Then the linear weighted estimation method could be used to calculate the testing result of the evaluated vehicle. The results show that this method can successfully identify the typical driving primitives. The couples of the driving primitives from the evaluated vehicle and the typical driving primitives from the large real-world driving dataset coincide with each other very well. This new method could enhance the standard development of the energy efficiency and emission testing of CAV and other off-cycle credits.
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PDF链接：
https://arxiv.org/pdf/1805.07643