Decoding pedestrian and automated vehicle interactions using immersive virtual reality and interpretable deep learning

Kalatian Arash; Farooq Bilal

摘要

To ensure pedestrian-friendly streets in the era of automated vehicles, reassessment of current policies, practices, design, rules and regulations of urban areas is of importance. This study investigates pedestrian crossing behaviour which, as an important element of urban dynamics, is expected to be affected by the presence of automated vehicles. For this purpose, an interpretable machine learning framework is proposed to explore factors affecting pedestrians' wait time before crossing mid-block crosswalks in the presence of automated vehicles. To collect rich behavioural data, we developed a dynamic and immersive virtual reality experiment, with 180 participants from a heterogeneous population in 4 different locations in the Greater Toronto Area (GTA). Pedestrian wait time behaviour is then analysed using a data-driven Cox Proportional Hazards (CPH) model, in which the linear combination of the covariates is replaced by a flexible non-linear deep neural network. The proposed model achieved a 5% improvement in goodness of fit, but more importantly, enabled us to incorporate a richer set of covariates. A game theoretic based interpretability method is used to understand the contribution of different covariates to the time pedestrians wait before crossing. Results show that the presence of automated vehicles on roads, wider lane widths, high density on roads, limited sight distance, and lack of walking habits are the main contributing factors to longer wait times. Our study suggested that, to move towards pedestrian-friendly urban areas, educational programs for children, enhanced safety measures for seniors, promotion of active modes of transportation, and revised traffic rules and regulations should be considered.

机译：为确保自动化车辆时代的行人友好的街道，重新评估城市地区的当前政策，实践，设计，规则和规定是重要的。本研究调查了行人交叉行为，作为城市动态的重要因素，预计将受到自动车辆的存在影响。为此目的，提出了一种可解释的机器学习框架，探讨在在存在自动车辆的存在下穿过中间块人行横道之前影响行人等待时间的因素。要收集丰富的行为数据，我们开发了一种动态和沉浸式虚拟现实实验，其中180名来自多伦多地区（GTA）的4个不同地点的异质人口。然后使用数据驱动的Cox比例危险（CPH）模型进行步行等待时间行为，其中协变量的线性组合由柔性非线性深神经网络代替。拟议的模型实现了适合的善良的5％，但更重要的是，使我们能够纳入丰富的协变量。基于游戏的基于游戏的可解释性方法用于了解不同协变量在交叉前等待时间等待的贡献。结果表明，道路上的自动车辆的存在，道路宽度宽，道路上的高密度，有限的瞄准距离，缺乏行走习惯是更长的等待时间的主要因素。我们的研究表明，要走向行人友好的城市地区，儿童教育计划，应考虑加强前辈的促进活动模式，并考虑修订的交通规则和法规。

Decoding pedestrian and automated vehicle interactions using immersive virtual reality and interpretable deep learning

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