A Multi-Stage Goal-Driven Network for Pedestrian Trajectory Prediction

TL;DR

This paper introduces MGNet, a multi-stage goal-driven network for pedestrian trajectory prediction that improves accuracy by forecasting intermediate goals rather than a single long-term goal.

Contribution

The paper presents a novel multi-stage goal-driven approach combining CVAE, attention, and goal evaluation to enhance trajectory prediction accuracy over existing methods.

Findings

MGNet outperforms state-of-the-art algorithms on JAAD and PIE datasets.

Forecasting intermediate goals reduces cumulative prediction errors.

The approach demonstrates significant improvements in pedestrian trajectory forecasting.

Abstract

Pedestrian trajectory prediction plays a pivotal role in ensuring the safety and efficiency of various applications, including autonomous vehicles and traffic management systems. This paper proposes a novel method for pedestrian trajectory prediction, called multi-stage goal-driven network (MGNet). Diverging from prior approaches relying on stepwise recursive prediction and the singular forecasting of a long-term goal, MGNet directs trajectory generation by forecasting intermediate stage goals, thereby reducing prediction errors. The network comprises three main components: a conditional variational autoencoder (CVAE), an attention module, and a multi-stage goal evaluator. Trajectories are encoded using conditional variational autoencoders to acquire knowledge about the approximate distribution of pedestrians' future trajectories, and combined with an attention mechanism to capture the temporal dependency between trajectory sequences. The pivotal module is the multi-stage goal evaluator, which utilizes the encoded feature vectors to predict intermediate goals, effectively minimizing cumulative errors in the recursive inference process. The effectiveness of MGNet is demonstrated through comprehensive experiments on the JAAD and PIE datasets. Comparative evaluations against state-of-the-art algorithms reveal significant performance improvements achieved by our proposed method.