通过弱监督方法识别驾驶风险源，提升对司机风险感知的理解。
Towards Driver Behavior Understanding: Weakly-Supervised Risk Perception in Driving Scenes
- 基于司机意图与反应关系建模，弱监督识别潜在风险
- 在RAID和HDDS数据集上分别提升20.6%和23.1%性能
- 适合自动驾驶、人机交互与行为分析方向研究者
实现零碰撞交通是智能车辆系统的核心目标，这需要理解司机的风险感知——一种由司机对外部刺激的自主反应及周围道路使用者对本车关注度共同决定的复杂认知过程。为推动该领域发展，我们构建了RAID（Risk Assessment In Driving scenes）数据集，包含4,691个标注视频片段，涵盖多样交通场景，标注内容包括司机意图动作、道路拓扑、风险事件（如横穿行人）、司机反应以及行人注意力状态。基于此，我们提出一种弱监督风险物体识别框架，通过建模司机意图与反应间的关联来定位潜在风险源。同时，分析行人注意力在风险评估中的作用，并验证数据集价值。实验表明，该方法在RAID和HDDS数据集上分别相较现有最优方法提升20.6%和23.1%。
原文摘要 · Abstract (English)
Achieving zero-collision mobility remains a key objective for intelligent vehicle systems, which requires understanding driver risk perception-a complex cognitive process shaped by voluntary response of the driver to external stimuli and the attentiveness of surrounding road users towards the ego-vehicle. To support progress in this area, we introduce RAID (Risk Assessment In Driving scenes)-a large-scale dataset specifically curated for research on driver risk perception and contextual risk assessment. RAID comprises 4,691 annotated video clips, covering diverse traffic scenarios with labels for driver's intended maneuver, road topology, risk situations (e.g., crossing pedestrians), driver responses, and pedestrian attentiveness. Leveraging RAID, we propose a weakly supervised risk object identification framework that models the relationship between driver's intended maneuver and responses to identify potential risk sources. Additionally, we analyze the role of pedestrian attention in estimating risk and demonstrate the value of the proposed dataset. Experimental evaluations demonstrate that our method achieves 20.6% and 23.1% performance gains over prior state-of-the-art approaches on the RAID and HDDS datasets, respectively.
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