生成带行为标注的多人行人在车流中过街数据集，支持自定义过街率
ARCANE-PedSynth: Synthetic Multi-Pedestrian Datasets with Behavioural Crossing Annotations
- 用混合AI与人工控制提升过街率至75%
- 生成含12种行为状态的多样化过街动作数据
- 适合自动驾驶行人过街预测研究者使用
我们提出ARCANE-PedSynth，一个基于CARLA的开源软件框架，用于生成带有密集行为标注的多行人合成数据集，以支持自动驾驶中的行人过街预测。该框架通过混合AI与人工控制架构，将原生9%的过街率提升至最高75%，可配置目标过街率。采用包含五种人物原型的12状态行为有限状态机，生成多样化的过街行为。框架同步输出RGB、LiDAR和DVS数据，每帧包含过街标签、行为状态及估计的2D姿态关键点。我们通过框架构建了示例数据集PedSynth++，包含533段多行人片段，覆盖12种天气条件，提供RGB、LiDAR和DVS流。整个流程可通过CLI参数化与Docker容器实现完全复现。
原文摘要 · Abstract (English)
We present ARCANE-PedSynth, an open-source CARLA-based software framework for generating synthetic multi-pedestrian datasets with dense behavioural annotations for pedestrian crossing prediction in autonomous driving. The framework overcomes CARLA's native 9% crossing rate through a hybrid AI-manual pedestrian control architecture, enabling configurable target rates up to 75%. A 12-state behavioural finite state machine with five character archetypes produces diverse crossing behaviours. The framework generates synchronised RGB, LiDAR, and DVS data with per-frame crossing labels, behavioural states, and estimated 2D pose keypoints. We demonstrate ARCANE-PedSynth through PedSynth++, an example dataset generated with the framework, comprising 533 multi-pedestrian clips across 12 weather conditions with RGB, LiDAR, and DVS streams. ARCANE-PedSynth is fully reproducible via CLI parameterisation and Docker containerisation.
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