Social acceptance is a major hurdle for autonomous vehicle technology, central to which is ensuring both passengers and nearby pedestrians feel safe. This idea of `feeling safe' and perceived safety is highly subjective and rooted in human intuition. As such, traditional analytical approaches to autonomous navigation often fail to cater for the social expectations of individuals. Therefore, this paper proposes an approach to capture the complexity of social expectations and integrate this complexity into a 3-layered Contextual Speed Controller. The layers were; the legal road speed limit, the socially acceptable speed given the number of nearby pedestrians, and the socially acceptable speed based on proximity to nearby pedestrians. An implementation of this layered approach was tested in areas of both low and high vehicle-pedestrian interactions. From the experiments conducted, the lower two layers were seen working in tandem to modulate the vehicle speed to appropriate levels that mimicked conservative human driver behaviour. In summary, this work quantified the relationship between pedestrian context and socially acceptable vehicle speeds, allowing for more perceivably safe autonomous driving. Furthermore, the need for different driving schemes for navigating different road environments was identified.
翻译:自主车辆技术的主要障碍是社会接受,这是确保乘客和附近行人感到安全的主要障碍。这种“感到安全”和感觉安全的想法是高度主观的,植根于人的直觉。因此,对自主航行的传统分析方法往往不能满足个人的社会期望。因此,本文件提出一种办法,以捕捉社会期望的复杂性,并将这种复杂性纳入一个三层次的上下文速度控制器。这些层次是:合法的公路速度限制,从附近行人人数来看,社会可接受的速度,以及以邻近行人为根据的社会可接受的速度。这一分层方法的实施在低和高车辆-速度相互作用的地区都进行了测试。从所进行的实验中可以看出,低两个层次是协同工作,将车辆速度调整到适当的水平,以模拟保守的驾驶者行为。概括地说,这项工作量化了行人与社会可接受的车辆速度之间的关系,允许更明显安全的自主驾驶。此外,还查明了不同道路环境的驾驶计划的必要性。