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Recognition of Commercial Vehicle Driving Cycles Based on Multilayer Perceptron Model
In this paper, we propose a multilayer perceptron-based recognition method for driving cycles of commercial vehicles. Our method solves the problem of identifying the type of driving cycle for commercial vehicles, and improves the efficiency and sustainability of road traffic. We collect driving condition data of 106,200 km long-distance commercial vehicles to validate our method. We pre-proceed six kinds of quantitative features as the data description; these are average speed, gear ratio, and accelerator pedal opening. Our model includes an input layer, hidden layers, and an output layer. The input layer receives and processes the input as low-dimensional features. The hidden layers consist of the feature extraction module and class regression module. The output layer projects extracted features to the classification space and computes the likelihood for each type. We achieve 99.83%, 97.85%, and 99.40% on the recognition accuracy for the expressway driving cycle, the suburban road driving cycle, and the urban road driving cycle, respectively. The experimental results demonstrate that our model achieves better results than the statistical method using Naive Bayes. Moreover, our method utilizes the data more efficiently and thus gains a better generalization performance.
Recognition of Commercial Vehicle Driving Cycles Based on Multilayer Perceptron Model
In this paper, we propose a multilayer perceptron-based recognition method for driving cycles of commercial vehicles. Our method solves the problem of identifying the type of driving cycle for commercial vehicles, and improves the efficiency and sustainability of road traffic. We collect driving condition data of 106,200 km long-distance commercial vehicles to validate our method. We pre-proceed six kinds of quantitative features as the data description; these are average speed, gear ratio, and accelerator pedal opening. Our model includes an input layer, hidden layers, and an output layer. The input layer receives and processes the input as low-dimensional features. The hidden layers consist of the feature extraction module and class regression module. The output layer projects extracted features to the classification space and computes the likelihood for each type. We achieve 99.83%, 97.85%, and 99.40% on the recognition accuracy for the expressway driving cycle, the suburban road driving cycle, and the urban road driving cycle, respectively. The experimental results demonstrate that our model achieves better results than the statistical method using Naive Bayes. Moreover, our method utilizes the data more efficiently and thus gains a better generalization performance.
Recognition of Commercial Vehicle Driving Cycles Based on Multilayer Perceptron Model
Xianbin Wang (author) / Yuqi Zhao (author) / Weifeng Li (author)
2023
Article (Journal)
Electronic Resource
Unknown
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