Pedestrian Models for Autonomous Driving Part II: High-Level Models of Human Behavior
Pedestrian Models for Autonomous Driving Part II: High-Level Models of Human Behavior
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DOI：
10.1109/tits.2020.3006767
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发表时间：
2021-09-01
影响因子：
8.5
通讯作者：
Fox, Charles
中科院分区：
文献类型：
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作者：
Camara, Fanta;Bellotto, Nicola;Fox, Charles
自动驾驶车辆(AVs)必须与行人共享空间，无论是在人行横道上的汽车这样的车行道情况下，还是在非车道情况下，例如在步行化的商业街上穿过人群的送货车辆。与静态障碍物不同，行人是具有复杂、交互运动的主动主体。因此，在行人在场的情况下计划反病毒行动需要对他们未来可能的行为进行建模，并对他们进行检测和跟踪。这篇叙事性评论文章是两篇文章的第二部分，共同考察了这一过程中涉及的当前技术堆栈，从反病毒设计师的角度，组织了从低级图像检测到高级心理模型的层级分类的最新研究。这一自成一体的第二部分涵盖了这一堆栈的更高层次，包括行人行为的模型，从预测单个行人可能的目的地和路径，到行人和自动驾驶车辆之间相互作用的博弈论模型。这项调查清楚地表明，尽管已经有了很好的最佳步行行为模型，但行人行为的高级心理和社会建模仍然是一个开放的研究问题，需要澄清许多概念性问题。关于行为的描述性和定性模型，早期的工作已经完成，但要将它们转化为实际AV控制的定量算法，仍有许多工作要做。
Autonomous vehicles (AVs) must share space with pedestrians, both in carriageway cases such as cars at pedestrian crossings and off-carriageway cases such as delivery vehicles navigating through crowds on pedestrianized high-streets. Unlike static obstacles, pedestrians are active agents with complex, interactive motions. Planning AV actions in the presence of pedestrians thus requires modelling of their probable future behavior as well as detecting and tracking them. This narrative review article is Part II of a pair, together surveying the current technology stack involved in this process, organising recent research into a hierarchical taxonomy ranging from low-level image detection to high-level psychological models, from the perspective of an AV designer. This self-contained Part II covers the higher levels of this stack, consisting of models of pedestrian behavior, from prediction of individual pedestrians' likely destinations and paths, to game-theoretic models of interactions between pedestrians and autonomous vehicles. This survey clearly shows that, although there are good models for optimal walking behavior, high-level psychological and social modelling of pedestrian behavior still remains an open research question that requires many conceptual issues to be clarified. Early work has been done on descriptive and qualitative models of behavior, but much work is still needed to translate them into quantitative algorithms for practical AV control.