Designing Feedback to Support Driver-Automation Coordination

汽车正变得高度自动化。在一定限度内，车辆现在能够检测和应对危险，以及保持横向和纵向控制;他们可以采取部分，高度或完全自动化，假设车辆控制的一些或所有方面。此外，司机被信息淹没。传感器、无线和计算技术的快速发展已经产生了一系列设备（例如，智能手机），这可以娱乐和通知司机，但也分散他们的注意力。拟议的研究的目标是在这个高信息访问环境中支持驾驶员自动化协调。虽然自动化研究在其他领域（例如，航空）可以提供一些指导，驾驶的特点是不同的，有关驾驶的研究只是现在才出现。 第一个主题是个体差异。我们将研究具有不同个性特征、认知能力和驾驶经验的驾驶员如何应对各种自动化水平（例如，分心参与、自动化状态的意识）。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。根据我们的调查结果，我们将制定和评估反馈策略（例如，关于自动化状态或驾驶员的自动化依赖行为的反馈），这可以增强跨驾驶员特性范围的驾驶员-自动化协调。 主题2将集中在司机的注意力。除了随着时间的推移而相对稳定的个体差异之外，驾驶员在给定时间（分心）以及一段时间（疲劳）经历的不同信息负载水平也会改变驾驶员-自动化协调。模拟器实验将研究驾驶员如何在不同的信息负载下与不同的自动化水平进行交互。根据我们的研究结果，我们将开发和评估实时反馈策略，以减轻对驾驶员自动化协调的潜在负面影响（例如，情况意识丧失），这可能是由于分心和疲劳。 主题3将验证我们的模拟器的调查结果，收集真实的世界数据使用我们的仪表车，这是一个在加拿大的一类。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。虽然模拟器提供了一个安全、可控和可重复的测试环境，但它们也缺乏真实的碰撞风险。 我们的研究结果将告知三组利益相关者，我们目前在加拿大和国际上与他们互动：汽车制造商，政策制定者和公众。同样重要的是，该计划将培养具有独特技能的高素质人才，这些人才将结合人为因素，交通安全，驾驶模拟器，仪表车辆和统计学方面的专业知识。

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.