面向微观仿真模型的部分自动驾驶车辆驾驶员行为规范

Rita Rodrigues, A. Bastos, Á. Seco
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摘要

车辆安全系统的实施预计会给我们的交通系统带来巨大而显著的变化。目前,公众可以使用的最复杂的系统都属于汽车工程师协会的第2级——部分自动化。在过去的几十年里,交通建模已经成为可持续交通规划和运营过程中的基本工具。特别是,微观模拟工具赋予了通过数学模型(如汽车跟随等)来表示交通一般行为的能力。然而,随着自动驾驶汽车(AVs)在我们的交通系统中的数量不断增加,在不同的交通条件下,这些车辆将产生的影响仍然是不确定的。因此,微观模拟能够很好地应对新兴技术对交通的影响变得至关重要。本文档描述了将用于收集和合并汽车跟随模型中的部分自动化效果的研究方法,以评估对驾驶行为的影响和实际意义。为了正确解决本研究项目固有的挑战,它定义了四个主要的调查阶段:(1)评估现实的数据收集方法;(2)部分自动化车辆驾驶员行为适应;(3)微观建模集成问题;(4)分析部分自动化车辆在道路安全和运力方面的实际效果。该博士学位的主要贡献是改进了基于真实交通数据的汽车跟随模型,这将允许解决未来假设中的不确定性,并为决策者和规划者准备潜在的结果。
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Specification of Drivers’ Behavior in Partially Automated Vehicles for Microscopic Simulation Models
The implementation of safety systems on vehicles is expected to introduce dramatic and prominent changes, into our transportation system. On the present, most sophisticated systems currently available to the public, fall under the Society of Automotive Engineers Level 2 - Partial Automation. Over the past decades, modeling traffic has become a fundamental instrument in the planning and operational processes of sustainable mobility. In particular, microscopic simulation tools grant the power to represent the traffic general behavior, through mathematical models such as the car-following, among others. Yet, with the increasing number of automated vehicles (AVs) in our transportation system, it is still uncertain the impacts that these will have, in different traffic conditions. Therefore, becomes essential that microscopic simulation can cope as well with the emerging technologies effects on traffic. This document describes the research methodology that will be used to collect and incorporate Partial Automation effects in a car-following model, to assess impacts in the driving behavior and practical implications. To properly address the challenges inherent to this research project, it was defined four main phases of investigation: (1) Assessment of realistic data collection methods; (2) Drivers’ behavioral adaptation in vehicles equipped with Partial Automation; (3) Microscopic modeling integration problems, and (4) Analyze of the practical effects of vehicles equipped with partial automation in terms of road safety and capacity. The main contribution of this Ph.D. is the improvement of a car-following model based on real traffic data, which will allow addressing uncertainties in assumptions about the future and will prepare decision makers and planners for potential outcomes.
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