Convolutional Neural Network for Trajectory Prediction
This work addresses trajectory prediction for autonomous robots interacting with humans, but it is incremental as it adapts an existing method (CNN) to a known problem.
The paper tackled pedestrian trajectory prediction for autonomous robots by proposing a convolutional neural network (CNN) approach, which achieved competitive results while being faster than current methods.
Predicting trajectories of pedestrians is quintessential for autonomous robots which share the same environment with humans. In order to effectively and safely interact with humans, trajectory prediction needs to be both precise and computationally efficient. In this work, we propose a convolutional neural network (CNN) based human trajectory prediction approach. Unlike more recent LSTM-based moles which attend sequentially to each frame, our model supports increased parallelism and effective temporal representation. The proposed compact CNN model is faster than the current approaches yet still yields competitive results.