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

• URL: http://arxiv.org/abs/2406.18050v1
• Date: Wed, 26 Jun 2024 03:59:21 GMT
• Title: A Multi-Stage Goal-Driven Network for Pedestrian Trajectory Prediction
• Authors: Xiuen Wu, Tao Wang, Yuanzheng Cai, Lingyu Liang, George Papageorgiou,
• Abstract summary: This paper proposes a novel method for pedestrian trajectory prediction, called multi-stage goal-driven network (MGNet) The network comprises three main components: a conditional variational autoencoder (CVAE), an attention module, and a multi-stage goal evaluator. The effectiveness of MGNet is demonstrated through comprehensive experiments on the JAAD and PIE datasets.
• Score: 6.137256382926171
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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