使用真实驾驶数据对互联和自动驾驶车辆进行能效和排放测试
Energy Efficiency and Emission Testing for Connected and Automated Vehicles Using Real-World Driving Data
摘要：
通过使用车载传感和外部连接技术，互联和自动驾驶汽车（CAV）可以提高能源效率，更好地路由，并减少交通拥堵。随着CAV技术的快速发展和适应性的提高，开发通用的评价方法和测试标准，以公平地评价CAV在不同交通条件下对能源消耗和环境污染的影响，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新方法和框架。利用被评价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。然后采用线性加权估计法计算被评价车辆的试验结果。实验结果表明，该方法能够成功地识别出典型的驾驶基元。从评估车辆的驾驶基元和典型的驾驶基元从大型现实世界的驾驶数据集彼此吻合得很好。这种新方法可以加强CAV和其他非循环信用的能源效率和排放测试的标准制定。
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.
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。
