Context-aware multi-task learning for pedestrian intent and trajectory prediction

doi:10.1016/j.trc.2025.105203

Context-aware multi-task learning for pedestrian intent and trajectory prediction

2025-06-21

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DOI：10.1016/j.trc.2025.105203

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• In this work, we asked whether predicting one aspect (intention) without the other (trajectory) limits the understanding of pedestrian behavior. Our results support this hypothesis by showing improved performance using our joint prediction approach. • We developed a novel multi-task framework, PTINet, that combines Local Contextual Features (LCF), such as pedestrian-specific attributes, with Global Features (GF)from image data and optical flow to analyze complex spatial–temporal patterns. • We chose a traditional LSTM over more complex transformers due to lower resource demands. Despite its simplicity, our approach outperforms transformer-based models in predicting pedestrian behavior. • Extensive experiments and ablation studies on benchmark datasets show PTINet’s superior performance, significantly outperforming state-of-the-art prediction models.

Keywords:

Pedestrian trajectory prediction

Intention prediction

Autonomous vehicle

Deep learning

Context-aware multi-task learning for pedestrian intent and trajectory prediction

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