Abstract
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.
Abstract (translated)
实现零碰撞的机动性仍然是智能车辆系统的关键目标，这需要理解驾驶员的风险感知——这是一种复杂的认知过程，由驾驶员对外部刺激的自愿反应和周围道路使用者对自身车辆的关注程度所塑造。为了推动这一领域的进步，我们引入了RAID（驾驶场景中的风险评估）——一个专门用于研究驾驶员风险感知和情境风险评估的大规模数据集。RAID包含4,691个注释视频片段，涵盖了各种交通场景，并为驾驶员的意图操作、道路拓扑结构、风险情况（如过马路行人）、驾驶员反应以及行人的注意程度等提供了标签。 利用RAID，我们提出了一种弱监督的风险对象识别框架，该框架通过建模驾驶员意图操作和响应之间的关系来识别潜在的风险来源。此外，我们还分析了行人注意力在评估风险中的作用，并展示了所提议数据集的价值。实验评估表明，在RAID和HDDS数据集上，我们的方法分别比现有的最先进的方法提高了20.6%和23.1%的性能。
URL
https://arxiv.org/abs/2603.05926
