Trajectory Prediction of Vehicles Based on Deep Learning

In order to safely and efficiently drive through the complex traffic scenarios, predicting the trajectory of the forward vehicle accurately is important for intelligent vehicles. Accurate and realtime trajectory prediction can make the intelligent vehicles adjust their maneuvers according to the running state of the vehicles in front of them. In recent years, deep-lear-ning-based methods have been applied as novel alternatives for trajectory prediction with the development of the machine learning. But which kind of deep neural networks is the most suitable model for trajectory prediction is uncertain. In this paper, we design three kinds of deep neural networks: Long Short Term Memory (LSTM), Gated Recurrent Units (GRU), and Stacked Autoencoders (SAEs) to predict the position and the velocity of the forward vehicles. We verify the performance of these three network models on the NGSIM I-80 dataset which consists of real trajectories of vehicles on multi-lanes. What's more, we use Savitzky-Golay filter to filter noise in order to reduce the effect of noise on the training models. Our results demonstrate that in the three deep neural networks that we designed, the LSTM model perform better than GRU model and SAEs model in the area of trajectory prediction. The results of our works will have certain guiding significance for choosing the model of neural network to predict the vehicle trajectories.