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 new evaluation method and  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  to evaluate the fuel economy and emission level of the vehicle based on the unsupervised learning of the real-world driving data of the evaluated vehicle and typical driving primitive analysis of the naturalistic driving dataset of a large number of different vehicles . The results show that this method can successfully identify the key driving primitives, patterns, and parameters of the vehicle speed and acceleration, and couple the driving primitives from the evaluated vehicle with typical driving primitives from the large real-world driving dataset , which could enhance the evaluation method and standard of fuel economy and emission for CAV .
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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