Who Make Drivers Stop? Towards Driver-centric Risk Assessment: Risk Object Identification via Causal Inference
Who Make Drivers Stop? Towards Driver-centric Risk Assessment: Risk Object Identification via Causal Inference
复制标题
谁让司机停车？
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
Yi
中科院分区：
文献类型：
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作者：
Chengxi Li;Stanley H. Chan;Yi
大量人员因驾驶员失误而死于交通事故。为了减少死亡事故，迫切需要开发智能驾驶系统来帮助驾驶员识别潜在风险。现有工作中通常根据碰撞预测来定义风险情况。然而，碰撞只是潜在风险的一个来源，需要更通用的定义。在这项工作中，我们提出了一种新颖的以驾驶员为中心的风险定义，即影响驾驶员行为的对象是有风险的。引入了一项称为风险对象识别的新任务。我们将任务表述为因果问题，并提出了一种基于因果推理的新型两阶段风险对象识别框架，并提出了对象级可操作驱动模型。与本田研究院驾驶数据集 (HDD) 的强大基线相比，我们在风险对象识别方面表现出良好的性能。我们的框架的平均性能比强大的基线显着提高了 7.5%。
A significant amount of people die in road accidents due to driver errors. To reduce fatalities, developing intelligent driving systems assisting drivers to identify potential risks is in an urgent need. Risky situations are generally defined based on collision prediction in the existing works. However, collision is only a source of potential risks, and a more generic definition is required. In this work, we propose a novel driver-centric definition of risk, i.e., objects influencing drivers’ behavior are risky. A new task called risk object identification is introduced. We formulate the task as the cause-effect problem and present a novel two-stage risk object identification framework based on causal inference with the proposed object-level manipulable driving model. We demonstrate favorable performance on risk object identification compared with strong baselines on the Honda Research Institute Driving Dataset (HDD). Our framework achieves a substantial average performance boost over a strong baseline by 7.5%.