FORWARN - Towards an intelligent Forward Collision Warning System

点击翻译按钮获取中文摘要

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).