Abstract
Vehicle speed prediction can obtain the future driving status of a vehicle in advance, which helps to make better decisions for energy management strategies. We propose a novel deep learning neural network architecture for vehicle speed prediction, called VSNet, by combining convolutional neural network (CNN) and long-short term memory network (LSTM). VSNet adopts a fake image composed of 15 vehicle signals in the past 15 s as model input to predict the vehicle speed in the next 5 s. Different from the traditional series or parallel structure, VSNet is structured with CNN and LSTM in series and then in parallel with two other CNNs of different convolutional kernel sizes. The unique architecture allows for better fitting of highly nonlinear relationships. The prediction performance of VSNet is first examined. The prediction results show a RMSE range of 0.519–2.681 and a R2 range of 0.997–0.929 for the future 5 s. Finally, an energy management strategy combined with VSNet and model predictive control (MPC) is simulated. The equivalent fuel consumption of the simulation increases by only 4.74% compared with DP-based energy management strategy and decreased by 2.82% compared with the speed prediction method with low accuracy.
Highlights
Speed prediction can obtain the future vehicle driving state in advance, which is helpful for energy management strategy to make more reasonable control and further develop the vehicle energy saving potential
Representative methods from stochastic prediction, machine learning and deep learning are selected for comparison with VSNet
It can be concluded that RMSE, Mean Absolute Error (MAE), Maximum Absolute Error (ME) and R2 of VSNet are better than the other methods
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. The commonly used methods for speed prediction are linear regression analysis [14,15], vehicle dynamics model prediction [16,17], stochastic prediction [18,19] and machine learning [20,21,22,23]. CNN reduces the number of parameters to be adjusted by neural networks through perceptual field and weight sharing, minimizes the preprocessing requirements of data and achieves high accuracy vehicle speed prediction. This paper combines CNN and LSTM to propose a novel neural network structure based deep learning for vehicle speed prediction, named VSNet. VSNet can identify the mapping relationship between vehicle signals and vehicle speed to accurately predict the future vehicle speed.
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