Designing Feedback to Support Driver-Automation Coordination

车辆正变得高度自动化。在一定的范围内，车辆现在能够检测并对危险做出反应，以及保持横向和纵向控制；它们可以部分、高度或完全自动化地行动，假定车辆控制的某些或全部方面。此外，司机们被信息淹没了。传感器、无线和计算技术的快速发展催生了一系列设备(如智能手机)，这些设备可以娱乐司机并向他们提供信息，但也会分散他们的注意力。拟议研究的目标是在这种高度信息获取的环境中支持驾驶员-自动化协调。虽然在其他领域(如航空)的自动化研究可以提供一些指导，但驾驶是有特点的不同的，相关的驾驶研究现在才刚刚出现。主题1将侧重于个体差异。我们将调查不同个性特征、认知能力和驾驶经验的司机如何应对不同的自动化水平(例如，分心投入、对自动化状态的认识)。为此，将进行驾驶模拟器实验，并对个体差异进行定性和定量评估。根据我们的发现，我们将开发和评估反馈策略(例如，关于自动化状态或司机的自动化依赖行为的反馈)，这些策略可以在一系列司机特征上增强司机-自动化之间的协调。主题2将集中在司机的注意力上。除了随着时间的推移相对稳定的个体差异，司机在给定时间(分心)和一段时间(疲劳)经历的不同水平的信息负载也会改变司机-自动化协调。模拟器实验将调查司机如何在不同的信息负载下与不同的自动化水平进行交互。基于我们的发现，我们将开发和评估实时反馈策略，以减轻因分心和疲劳而对驾驶员-自动化协调造成的潜在负面影响(例如，情况意识丧失)。主题3将通过使用我们的仪表化车辆收集真实世界的数据来验证我们的模拟器发现，这在加拿大是独一无二的。由于需要大量的工作和设备，这种验证很少进行，但非常重要。虽然模拟器提供了安全、可控和可重复的测试环境，但它们也缺乏真正的崩溃风险。我们的结果将向加拿大国内和国际上目前与之互动的三组利益相关者提供信息：汽车制造商、政策制定者和公众。同样重要的是，该项目将培养具有独特技能的高素质人才，将人为因素、交通安全、驾驶模拟器、仪表化车辆和统计学方面的专业知识结合在一起。

Vehicles are becoming highly automated. Within certain limits, vehicles are now capable of detecting and reacting to hazards as well as maintaining lateral and longitudinal control; they can act in partial, high, or full automation, assuming some or all aspects of vehicle control. Further, drivers are inundated with information. The rapid development of sensor, wireless, and computing technology has given rise to a range of devices (e.g., smart phones), which can entertain and inform drivers, but also distract them. The objective of the proposed research is to support driver-automation coordination in this high information-access environment. Although automation research in other domains (e.g., aviation) can provide some guidance, driving is characteristically different and relevant research in driving is only emerging now.Theme 1 will focus on individual differences. We will investigate how drivers with different personality characteristics, cognitive abilities, and driving experience respond to various automation levels (e.g., distraction engagement, awareness of automation state). To this end, driving simulator experiments coupled with qualitative and quantitative assessments of individual differences will be conducted. Based on our findings, we will develop and evaluate feedback strategies (e.g., feedback about automation state or driver's automation reliance behaviour) that can enhance driver-automation coordination across a range of driver characteristics. Theme 2 will focus on driver attention. In addition to individual differences that are relatively stable over time, different levels of information load experienced by drivers at a given time (distraction) as well as over a period of time (fatigue) can also alter driver-automation coordination. Simulator experiments will investigate how drivers interact with different automation levels under varying information load. Based on our findings, we will develop and evaluate real-time feedback strategies to mitigate the potential negative effects on driver-automation coordination (e.g., situation awareness loss) that can arise from distraction and fatigue. Theme 3 will validate our simulator findings by collecting real world data using our instrumented vehicle, which is one-of-a-kind in Canada. Such validation is rarely undertaken due to significant effort and equipment required, but is very important. Although simulators provide a safe, controlled, and reproducible test environment, they also lack real crash risk. Our results will inform three sets of stakeholders, whom we currently interact with within Canada and internationally: vehicle manufacturers, policy makers, and the public. Just as importantly, this program will train highly qualified personnel with a unique skill set combining expertise in human factors, traffic safety, driving simulators, instrumented vehicles, and statistics.