Driving Pattern Recognition Based on Improved LDA Model
Lingyun Xie, Ying Shi, Zhenwei Li · 2018
In recent years, the automobile industry has developed rapidly and the drivers' unregulated driving behaviors have caused a lot of traffic safety problems. By using the topic model, this paper can find the relationship between driving pattern with driving behavior and provide technical support for traffic safety. Taking the shortage of the current mainstream topic models pLSA and LDA (Latent Dirichlet Allocation) into account, this paper proposes an improved LDA model with time labels, namely the T-LDA model to identify driving patterns. The experimental results show that the improved model can effectively extract a series of continuous driving behavior characteristic and improve the accuracy of driving pattern recognition.