Abstract
Vehicle evaluation parameters, which are increasingly of concern for governments and consumers, quantify performance indicators, such as vehicle performance, emissions, and driving experience to help guide consumers in purchasing cars. While past approaches for driving cycle prediction have been proven effective and used in many countries, these algorithms are difficult to use in China with its complex traffic environment and increasingly high frequency of traffic jams. Meanwhile, we found that the vehicle dataset used by the driving cycle prediction problem is usually unbalanced in real cases, which means that there are more medium and high speed samples and very few samples at low and ultra-high speeds. If the ordinary clustering algorithm is directly applied to the unbalanced data, it will have a huge impact on the performance to build driving cycle maps, and the parameters of the map will deviate considerable from actual ones. In order to address these issues, this paper propose a novel driving cycle map algorithm framework based on an ensemble learning method named multi-clustering algorithm, to improve the performance of traditional clustering algorithms on unbalanced data sets. It is noteworthy that our model framework can be easily extended to other complicated structure areas due to its flexible modular design and parameter configuration. Finally, we tested our method based on actual traffic data generated in Fujian Province in China. The results prove the multi-clustering algorithm has excellent performance on our dataset.
Highlights
Car parameters are an important indicator to guide consumers in purchasing cars, and they quantify users’ driving experience as comparable figures
We found the unbalanced data will have a huge impact on the performance when building driving cycle maps, and the car parameters generated by above algorithms deviate quite a lot from actual ones
The T-distributed stochastic neighbor embedding (T-stochastic neighbor embedding (SNE)) algorithm is used to reduce the original data and visualize the Compared with the K-means algorithm, the DBSCAN algorithm has the advantage of recognizing clustering results
Summary
Car parameters are an important indicator to guide consumers in purchasing cars, and they quantify users’ driving experience as comparable figures. Test standards will lead to some car parameters that cannot accurately reflect the actual driving experience of consumers in China’s automotive test projects, which can cause the information to be false or misleading and lead to confusion among consumers. At present, developed countries such as Europe, the United States, and Japan generally accept driving cycle standards and energy consumption/emission certifications that are adapted to their respective driving cycles. In the past, with the New European Driving Cycle (NEDC), published in Europe for energy consumption/emission evaluation and widely used in China, it is difficult to accurately illustrate the actual performance of Sensors 2020, 20, 2448; doi:10.3390/s20092448 www.mdpi.com/journal/sensors cars in the various regions of China. The gap between the manufacturer’s published data and published data and test data of the users iswhich extremely obvious, whichthe seriously the actual test data of the theactual users is extremely obvious, seriously decreases overalldecreases buying the overall buying experience of users
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