Interacting Vehicle Trajectory Prediction with Convolutional Recurrent Neural Networks
Saptarshi Mukherjee,Sen Wang,Andrew Wallace,Saptarshi Mukherjee,Sen Wang,Andrew Wallace
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 eff...
