FORWARN - Towards an intelligent Forward Collision Warning System

FORWARN旨在创建一个前向碰撞警告（FCW）系统，该系统在决定警告的适当性和时机时能够考虑驾驶员的注意力分散。为了实现这一目标，驾驶员将被要求在需要FCW参与的模拟驾驶场景中从事各种分散注意力的任务。FCW随后将接受“培训”，以使用与车辆和驾驶员相关的指标来确定驾驶员分心。本文提出的研究旨在通过理解分心、警告和驾驶员表现之间的关系，弥合辅助系统工作与驾驶员分心影响工作之间的差距。这项研究将研究各种车载分散注意力的任务对驾驶性能的影响，并确定在设计先进的驾驶辅助系统（如FCW）时如何考虑这些因素。这项研究的一个特别重点是更好地了解非视觉任务的分散注意力的效果，例如参与免提移动的电话交谈。FCW使用传感器和雷达扫描车辆前方区域，旨在通过建议驾驶员刹车来避免追尾碰撞或减少其影响。一些较新的系统甚至在某些情况下进行干预以避免碰撞。这种系统有相当大的潜在好处，最近在北美的大规模现场操作测试（FOT）中得到了证明。然而，存在这样的危险，即具有永久固定标准的系统将被大量驾驶员视为呈现太多“错误”（不希望的）警告。事实上，在美国现场操作测试的司机都渴望能够调整系统，以他们的个人喜好。因此，拟议项目的一个主要目标是使用眼动跟踪和车辆相关性能指标来识别FCW在确定驾驶员是否分心之前所需的信息。在接近危险情况时，只有在确定驾驶员确实分心并且无法及时对危险做出反应之后，才会触发这种智能FCW。由于驾驶是一项多方面的活动，因此评估分心对驾驶性能的影响取决于所观察到的与驾驶员和车辆相关的确切指标以及分心任务本身的性质。该项目将建立在该小组在这一领域已经开展的工作的基础上，这些工作是在以前的欧洲项目中进行的，如AIDE（自适应集成驾驶员-车辆界面）和HASTE（人机界面和欧洲交通安全）以及最近完成的EPSRC项目EASY（自动化系统对安全的影响）。

FORWARN aims to create a Forward Collision Warning (FCW) system that is able to consider driver distraction when making decisions on the appropriateness and timing of warnings. To achieve this, drivers will be asked to engage in a variety of distracting tasks in simulated driving scenarios requiring the engagement of a FCW. The FCW will later be 'trained' to ascertain driver distraction using vehicle- and driver-related metrics. The research proposed here aims to bridge the gap between work on assistance systems and work on the impact of driver distractions, by understanding the relationship between distraction, warnings and driver performance. This research will examine the effect of a variety of in-vehicle distracting tasks on driving performance, and establish how these can then be taken into account when designing an advanced driver assistance system such as FCW. A particular focus of this research will be to gain a better understanding of the distracting effect of non-visual tasks, such as engagement in hands free mobile phone conversations. FCW uses sensors and radar to scan the area ahead of the vehicle, and aims to avoid rear-end collisions, or reduce their impact, by advising drivers to brake. Some newer systems even intervene in some cases to avoid a collision. There are considerable potential benefits of such systems which have recently been proven in a large-scale Field Operational Test (FOT) in North America. However, there is a danger that systems which have permanently fixed criteria will be viewed by a significant number of drivers as presenting too many "false" (unwanted) warnings. Indeed, drivers in the U.S. Field Operational Test were keen to be able to tune the system to their personal preferences. Therefore, one main aim of the proposed project is to use eye tracking and vehicle related performance measures to identify the information that is needed by a FCW before it can establish whether or not a driver is distracted. Upon approach to a hazardous condition, this intelligent FCW will then only be triggered after if it has ascertained that the driver is truly distracted and unable to respond to the hazard in good time. As driving is a multi-faceted activity, assessing the effects of distraction on driving performance depend on the exact driver- and vehicle-related metrics being observed as well as the nature of the distracting task itself. This project will build upon the work already conducted by the group in this area during previous European projects such as AIDE (Adaptive Integrated Driver-vehicle interfacE) and HASTE (Human machine interface And the Safety of Traffic in Europe) and a recently completed EPSRC project, EASY (Effects of Automated Systems on safetY).