Stepwise Goal-Driven Networks for Trajectory Prediction

by   W, et al.

We propose to predict the future trajectories of observed agents (e.g., pedestrians or vehicles) by estimating and using their goals at multiple time scales. We argue that the goal of a moving agent may change over time, and modeling goals continuously provides more accurate and detailed information for future trajectory estimation. In this paper, we present a novel recurrent network for trajectory prediction, called Stepwise Goal-Driven Network (SGNet). Unlike prior work that models only a single, long-term goal, SGNet estimates and uses goals at multiple temporal scales. In particular, the framework incorporates an encoder module that captures historical information, a stepwise goal estimator that predicts successive goals into the future, and a decoder module that predicts future trajectory. We evaluate our model on three first-person traffic datasets (HEV-I, JAAD, and PIE) as well as on two bird's eye view datasets (ETH and UCY), and show that our model outperforms the state-of-the-art methods in terms of both average and final displacement errors on all datasets. Code has been made available at:



page 1

page 8


Goal-driven Long-Term Trajectory Prediction

The prediction of humans' short-term trajectories has advanced significa...

Multi-Camera Trajectory Forecasting with Trajectory Tensors

We introduce the problem of multi-camera trajectory forecasting (MCTF), ...

FIERY: Future Instance Prediction in Bird's-Eye View from Surround Monocular Cameras

Driving requires interacting with road agents and predicting their futur...

Mutual Distillation Learning Network for Trajectory-User Linking

Trajectory-User Linking (TUL), which links trajectories to users who gen...

Multimodal Future Localization and Emergence Prediction for Objects in Egocentric View with a Reachability Prior

In this paper, we investigate the problem of anticipating future dynamic...

A Graph Attention Based Approach for Trajectory Prediction in Multi-agent Sports Games

This work investigates the problem of multi-agents trajectory prediction...

First-Person Activity Forecasting with Online Inverse Reinforcement Learning

We address the problem of incrementally modeling and forecasting long-te...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.