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

随着自动驾驶汽车（AVs）被开发用于在日益复杂和多样化的交通环境中行驶，全面测试自动驾驶汽车的行为方式总是安全且为人类道路使用者所接受变得越来越困难。人们普遍认为，解决这一问题的关键部分将是使用虚拟交通模拟，其中正在开发的自动驾驶汽车的模拟版本可以满足模拟的周围交通。理论上，这种模拟可以涵盖广泛的可能场景，包括日常和更重要的安全交互。然而，目前对人类道路使用者行为的理解和模型还不够好，无法在详细程度上对交通相互作用进行真实的模拟，从而使此类测试具有意义。该奖学金旨在开发缺失的人类行为模拟模型，以确保未来自动化运输系统的发展能够以负责任的、以人为本的方式进行。汽车司机和行人的行为将在真实交通中观察，也将在驾驶和行人模拟器的对照研究中观察，在某些情况下，用神经生理学（EEG）数据补充行为数据，因为几个候选组件模型对大脑活动做出了具体的预测。然后，该奖学金将建立在现有的驾驶员和行人在日常和安全关键情况下的行为模型的基础上，并将这些模型扩展为最先进的特定现象的神经科学模型，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下的交通互动。这样的认知互动模型，基于良好理解的潜在机制，将是一个主要贡献的奖学金。一些研究人员建议完全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，也就是说，没有正确建模潜在机制的野心。该研究假设，为了实现可靠的自动驾驶汽车虚拟测试，将需要两种建模方法，并将研究将它们结合起来的方法。同样重要的是，由于机器学习模型的“黑箱”性质，需要对其进行调查和基准测试，例如确定其推广到罕见的安全关键事件的能力。这项多学科研究将在利兹大学交通研究所进行，并得到心理学院和计算机学院的支持，该研究将基于并扩展该研究员过去在车辆工程、认知神经科学和机器学习方面的经验。该奖学金得到了工业界的直接支持，包括咨询能力和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用于工业环境的概念验证，以进行模拟测试。

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.