ESIA: Energy-Based Framework for Pedestrian Intention Prediction
A new framework called ESIA (Energy-based Spatiotemporal Interaction-Aware framework) has been introduced by researchers for predicting pedestrian intentions in autonomous vehicles, utilizing a Conditional Random Field (CRF) model. This innovative framework represents pedestrians and environmental factors as spatiotemporal nodes in a comprehensive graph, where unary potentials denote individual intentions and pairwise potentials reflect interactions. ESIA addresses shortcomings found in prior research, such as overly simplified multi-agent interaction models, unclear reasoning processes, and insufficient global coherence. By framing intention prediction as a structured prediction challenge, the framework seeks to enhance both robustness and interpretability. This research has been published on arXiv with the identifier 2604.23728.
Key facts
- ESIA is a CRF-based framework for pedestrian intention prediction.
- It uses a unified graph representation with spatiotemporal nodes.
- Unary potentials capture individual intentions; pairwise potentials encode interactions.
- The framework addresses oversimplified interaction patterns and opaque reasoning.
- It aims to improve robustness and interpretability in autonomous driving.
- The paper is available on arXiv with ID 2604.23728.
Entities
Institutions
- arXiv