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ESIA framework enhances pedestrian intention prediction for autonomous driving

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced ESIA, a new framework for predicting pedestrian intentions in autonomous driving scenarios. This approach models pedestrians and their environment as nodes in a graph, using energy functions to capture individual intentions and interactions. ESIA aims to improve the robustness and interpretability of predictions by ensuring scene-level consistency and penalizing logical contradictions. AI

IMPACT Enhances pedestrian intention prediction for autonomous driving systems, potentially improving safety and decision-making.

RANK_REASON This is a research paper describing a new framework for pedestrian intention prediction.

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

ESIA framework enhances pedestrian intention prediction for autonomous driving

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This is a research paper describing a new framework for pedestrian intention prediction.
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Yanping Wu, Meiting Dang, Lin Wu, Edmond S. L. Ho, Zhenghua Chen, Chongfeng Wei ·

    ESIA: An Energy-Based Spatiotemporal Interaction-Aware Framework for Pedestrian Intention Prediction

    arXiv:2604.23728v1 Announce Type: new Abstract: Recent advances in autonomous driving have motivated research on pedestrian intention prediction, which aims to infer future crossing decisions and actions by modeling temporal dynamics, social interactions, and environmental contex…

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