Convolutional Neural Network for Trajectory Prediction

TL;DR

This paper introduces a CNN-based approach for pedestrian trajectory prediction that offers increased computational efficiency and competitive accuracy compared to traditional LSTM models, facilitating safer autonomous robot interactions.

Contribution

The paper presents a novel CNN architecture for trajectory prediction that improves speed while maintaining accuracy, contrasting with existing LSTM-based methods.

Findings

Faster trajectory prediction compared to LSTM models

Maintains competitive accuracy in pedestrian trajectory forecasting

Supports increased parallelism for real-time applications

Abstract

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.