Animating Vehicles Risk-Aware Interaction with Pedestrians Using Deep Reinforcement Learning

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dc.contributor.authorTsai, Hao-Mingen_US
dc.contributor.authorWong, Sai-Keungen_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.accessioned2025-10-07T06:02:36Z
dc.date.available2025-10-07T06:02:36Z
dc.date.issued2025
dc.description.abstractThis paper introduces a deep reinforcement learning-based system for ego vehicle control, enabling interaction with dynamic objects like pedestrians and animals. These objects display varied crossing behaviors, including sudden stops and directional shifts. The system uses a perception module to identify road structures, key pedestrians, inner wheel difference zones, and object movements. This allows the vehicle to make context-aware decisions, such as yielding, turning, or maintaining speed. The training process includes reward terms for speed, time, time-to-collision, and cornering to refine policy learning. Experiments show ego vehicles can adjust their behavior, such as decelerating or yielding, to avoid collisions. Ablation studies highlighted the importance of specific reward terms and state components. Animation results show that ego vehicles could safely interact with pedestrians or animals that exhibited sudden acceleration, mid-crossing directional changes, and abrupt stops.en_US
dc.description.sectionheadersVehicle Dynamics and Interactions
dc.description.seriesinformationPacific Graphics Conference Papers, Posters, and Demos
dc.identifier.doi10.2312/pg.20251261
dc.identifier.isbn978-3-03868-295-0
dc.identifier.pages12 pages
dc.identifier.urihttps://doi.org/10.2312/pg.20251261
dc.identifier.urihttps://diglib.eg.org/handle/10.2312/pg20251261
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 → Physical simulation; Collision detection; Reinforcement learning
dc.subjectComputing methodologies → Physical simulation
dc.subjectCollision detection
dc.subjectReinforcement learning
dc.titleAnimating Vehicles Risk-Aware Interaction with Pedestrians Using Deep Reinforcement Learningen_US
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