Animating Vehicles Risk-Aware Interaction with Pedestrians Using Deep Reinforcement Learning
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Date
2025
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
This 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.
Description
CCS Concepts: Computing methodologies → Physical simulation; Collision detection; Reinforcement learning
@inproceedings{10.2312:pg.20251261,
booktitle = {Pacific Graphics Conference Papers, Posters, and Demos},
editor = {Christie, Marc and Han, Ping-Hsuan and Lin, Shih-Syun and Pietroni, Nico and Schneider, Teseo and Tsai, Hsin-Ruey and Wang, Yu-Shuen and Zhang, Eugene},
title = {{Animating Vehicles Risk-Aware Interaction with Pedestrians Using Deep Reinforcement Learning}},
author = {Tsai, Hao-Ming and Wong, Sai-Keung},
year = {2025},
publisher = {The Eurographics Association},
ISBN = {978-3-03868-295-0},
DOI = {10.2312/pg.20251261}
}