HiLo-Align: A Hierarchical Semantic Alignment Framework for Driving Decision Generation via Virtual-Physical Integration

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dc.contributor.authorDuan, Zehaoen_US
dc.contributor.authorHuang, Chengyanen_US
dc.contributor.authorWang, Linen_US
dc.contributor.editorChristie, Marcen_US
dc.contributor.editorHan, Ping-Hsuanen_US
dc.contributor.editorLin, Shih-Syunen_US
dc.contributor.editorPietroni, Nicoen_US
dc.contributor.editorSchneider, Teseoen_US
dc.contributor.editorTsai, Hsin-Rueyen_US
dc.contributor.editorWang, Yu-Shuenen_US
dc.contributor.editorZhang, Eugeneen_US
dc.date.accessioned2025-10-07T06:02:46Z
dc.date.available2025-10-07T06:02:46Z
dc.date.issued2025
dc.description.abstractAutonomous vehicles operating in uncertain urban environments are required to reason over complex multi-agent interactions while adhering to stringent safety requirements. Hierarchical frameworks often use large models for high-level (virtual-layer) planning and deep reinforcement learning for low-level (physical-layer) control. However, semantic and temporal misalignment between layers leads to command errors and delayed response. We propose HiLo-Align, a hybrid hierarchical framework that unifies both layers via a shared semantic space and time scale. By explicitly modeling cross-layer alignment, HiLo-Align improves control coordination and semantic consistency. Experimental results on both simulation and real-world datasets indicate enhanced collision avoidance, generalization, and robustness in high-risk urban environments.en_US
dc.description.sectionheadersVehicle Dynamics and Interactions
dc.description.seriesinformationPacific Graphics Conference Papers, Posters, and Demos
dc.identifier.doi10.2312/pg.20251263
dc.identifier.isbn978-3-03868-295-0
dc.identifier.pages11 pages
dc.identifier.urihttps://doi.org/10.2312/pg.20251263
dc.identifier.urihttps://diglib.eg.org/handle/10.2312/pg20251263
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Computing methodologies → Machine learning; Reinforcement learning; Semantic networks; Applied computing → Transportation
dc.subjectComputing methodologies → Machine learning
dc.subjectReinforcement learning
dc.subjectSemantic networks
dc.subjectApplied computing → Transportation
dc.titleHiLo-Align: A Hierarchical Semantic Alignment Framework for Driving Decision Generation via Virtual-Physical Integrationen_US
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