A Vehicle Velocity Prediction Method with Kinematic Segment Recognition

Abstract

Accurate vehicle velocity prediction is of great significance in vehicle energy distribution and road traffic management. In light of the high time variability of vehicle velocity itself and the limitation of single model prediction, a velocity prediction method based on K-means-QPSO-LSTM with kinematic segment recognition is proposed in this paper. Firstly, the K-means algorithm was used to cluster samples with similar characteristics together, extract kinematic fragment samples in typical driving conditions, calculate their feature parameters, and carry out principal component analysis on the feature parameters to achieve dimensionality reduction transformation of information. Then, the vehicle velocity prediction sub-neural network models based on long short-term memory (LSTM) with the QPSO algorithm optimized were trained under different driving condition datasets. Furthermore, the kinematic segment recognition and traditional vehicle velocity prediction were integrated to form an adaptive vehicle velocity prediction method based on driving condition identification. Finally, the current driving condition type was identified and updated in real-time during vehicle velocity prediction, and then the corresponding sub-LSTM model was used for vehicle velocity prediction. The simulation experiment demonstrated a significant enhancement in both the velocity and accuracy of prediction through the proposed method. The proposed hybrid method has the potential to improve the accuracy and reliability of vehicle velocity prediction, making it applicable in various fields such as autonomous driving, traffic management, and energy management strategies for hybrid electric vehicles.

Keywords

• vehicle velocity prediction
• neural network (NN)
• quantum particle swarm optimization (QPSO)
• long short-term memory (LSTM)