COMMOTIONS: Computational Models of Traffic Interactions for Testing of Automated Vehicles

COMMOTIONS：用于自动驾驶车辆测试的交通交互计算模型

• 批准号: EP/S005056/1
• 负责人: Gustav Markkula
• 金额: $ 149.18万
• 依托单位: University of Leeds
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FS005056%2F1
• 关键词: COMMOTIONS; Computational; Models; Traffic; Interactions

As automated vehicles (AVs) are being developed for driving in increasingly complex and diverse traffic environments, it becomes increasingly difficult to comprehensively test that the AVs always behave in ways that are safe and acceptable to human road users. There is wide consensus that a key part of the solution to this problem will be the use of virtual traffic simulations, where simulated versions of an AV under development can meet simulated surrounding traffic. Such simulations could in theory cover vast ranges of possible scenarios, including both routine and more safety-critical interactions. However, the current understanding and models of human road user behaviour is not good enough to permit realistic simulations of traffic interactions at the level of detail needed for such testing to be meaningful. This fellowship aims to develop the missing simulation models of human behaviour, to ensure that development of the future automated transport system can be carried out in a responsible, human-centric way.Behaviour of car drivers and pedestrians will be observed both in real traffic as well as in controlled studies in driving and pedestrian simulators, in some cases complementing behavioural data with neurophysiological (EEG) data, since several candidate component models make specific predictions about brain activity. The fellowship will then build on existing models of driver and pedestrian behaviour in routine and safety-critical situations, and extend these with state of the art neuroscientific models of specific phenomena like perceptual judgments, beliefs about others' intentions, and communication, to create an integrated cognitive modelling framework allowing simulations of traffic interactions across a variety of targeted scenarios. Such cognitive interaction models, based on well-understood underlying mechanisms, will be one main contribution from the fellowship. Some researchers have suggested the use of another type of model altogether, instead obtained directly by applying machine learning (ML) methods to large data sets of human road user behaviour, i.e., without an ambition to correctly model underlying mechanisms. This fellowship hypothesises that to achieve reliable virtual testing of AVs, both types of modelling approaches will be needed, and methods for combining them will be researched. Not least, due to their "black box" nature, ML models need to be investigated and benchmarked, to for example determine their ability to generalise to rare, safety-critical events. The multi-disciplinary research, building on and extending on the fellow's past experience in vehicle engineering, cognitive neuroscience, and machine learning, will be carried out at the Institute for Transport Studies, University of Leeds, with support also from the Schools of Psychology and Computing. The fellowship has direct support from industry, both in advisory capacities and as project partners actively sharing data and methods as well as providing first proof-of-concept uptake of the developed models into industrial environments for simulated testing.

随着自动驾驶汽车（AV）在日益复杂和多样化的交通环境中的驾驶，全面测试AV始终以人类道路使用者安全和可接受的方式运行变得越来越困难。人们普遍认为，解决这个问题的一个关键部分将是使用虚拟交通模拟，其中正在开发的AV的模拟版本可以满足模拟的周围交通。这种模拟在理论上可以涵盖广泛的可能场景，包括常规和更安全的相互作用。然而，目前对人类道路使用者行为的理解和模型还不够好，不足以在这种测试有意义所需的细节水平上对交通互动进行逼真的模拟。该奖学金旨在开发人类行为的缺失模拟模型，以确保未来自动化运输系统的开发能够以负责任的、以人为本的方式进行。汽车驾驶员和行人的行为将在真实的交通以及驾驶和行人模拟器的受控研究中观察，在某些情况下，用神经生理学（EEG）数据补充行为数据，因为几个候选成分模型对大脑活动做出了具体的预测。该奖学金将建立在现有的驾驶员和行人在常规和安全关键情况下的行为模型基础上，并将其扩展为特定现象的最先进的神经科学模型，如感知判断，对他人意图的信念和沟通，以创建一个集成的认知建模框架，允许模拟各种目标场景中的交通互动。这种认知互动模式以人们熟知的基本机制为基础，将是该研究金的主要贡献之一。一些研究人员建议使用另一种类型的模型，而不是直接通过将机器学习（ML）方法应用于人类道路使用者行为的大型数据集来获得，即，没有正确建模潜在机制的野心。该奖学金假设，以实现可靠的虚拟测试的自动驾驶汽车，这两种类型的建模方法将是必要的，并将研究它们相结合的方法。尤其重要的是，由于其“黑箱”性质，需要对ML模型进行研究和基准测试，例如确定其推广到罕见的安全关键事件的能力。多学科研究，建立和扩展研究员在车辆工程，认知神经科学和机器学习方面的过去经验，将在利兹大学运输研究所进行，并得到心理学和计算学院的支持。该研究金得到了工业界的直接支持，无论是在咨询能力方面，还是作为项目合作伙伴，积极分享数据和方法，以及将开发的模型首次纳入工业环境进行模拟测试。

项目成果

Explaining human interactions on the road by large-scale integration of computational psychological theory.

• DOI: 10.1093/pnasnexus/pgad163
• 发表时间: 2023-06
• 期刊: PNAS NEXUS
• 作者: Markkula, Gustav;Lin, Yi-Shin;Srinivasan, Aravinda Ramakrishnan;Billington, Jac;Leonetti, Matteo;Kalantari, Amir Hossein;Yang, Yue;Lee, Yee Mun;Madigan, Ruth;Merat, Natasha
• 通讯作者: Merat, Natasha

COMMOTIONS: Computational Models of Traffic Interactions for Testing of Automated Vehicles - a "green paper" for opening discussion with stakeholders and defining project scope

COMMOTIONS：用于测试自动驾驶车辆的交通交互计算模型 - 一份“绿皮书”，用于与利益相关者展开讨论并定义项目范围

• 发表时间: 2019
• 作者: Markkula G

How accurate models of human behavior are needed for human-robot interaction? For automated driving?

人机交互需要多准确的人类行为模型？

• 发表时间: 2022
• 作者: Markkula G

Learning to interpret novel eHMI: The effect of vehicle kinematics and eHMI familiarity on pedestrian' crossing behavior.

学习解释新颖的 eHMI：车辆运动学和 eHMI 熟悉程度对行人过路行为的影响。

• DOI: 10.1016/j.jsr.2021.12.010
• 发表时间: 2022
• 期刊: Journal of safety research
• 影响因子: 4.1
• 作者: Lee YM

A Utility Maximization Model of Pedestrian and Driver Interactions

• DOI: 10.1109/access.2022.3213363
• 发表时间: 2021-10
• 期刊: IEEE Access
• 影响因子: 3.9
• 作者: Yi-Shin Lin;Aravinda Ramakrishnan Srinivasan;M. Leonetti;J. Billington;G. Markkula