Interacting Vehicle Trajectory Prediction with Convolutional Recurrent Neural Networks

Anticipating the future trajectories of surrounding vehicles is a crucial and challenging task in path planning for autonomy. We propose a novel Convolutional Long Short Term Memory (Conv-LSTM) based neural network architecture to predict the future positions of cars using several seconds of historical driving observations. This consists of three modules: 1) Interaction Learning to capture the effect of surrounding cars, 2) Temporal Learning to identify the dependency on past movements and 3) Motion Learning to convert the extracted features from these two modules into future positions. To continuously achieve accurate prediction, we introduce a novel feedback scheme where the current predicted positions of each car are leveraged to update future motion, encapsulating the effect of the surrounding cars. Experiments on two public datasets demonstrate that the proposed methodology can match or outperform the state-of-the-art methods for long-term trajectory prediction.