In-Car Technology to Alert Attention-Imparied Drivers

描述（由申请人提供）：本研究项目的主要目标是识别驾驶性能和安全错误的模式，这些模式是由有用视野（UFOV）中与年龄相关的损伤引起的，并开发车内对策，以降低碰撞风险，提高安全性和机动性。具体目的是：1）确定驾驶员报警和碰撞警告装置的最佳信号参数，2）测试该装置在高保真驾驶模拟器中的挑战性情况下和在仪表化车辆中的道路上改善驾驶员反应的功效（IV）; 3）表征道路驾驶性能，以及搜索行为和补偿策略，在有UFOV障碍的危险驾驶员和操作IV的比较受试者中; 4）评估在IV中在道路上的危险驾驶员和比较受试者中的安全干预的效果;以及5）估计在避免撞车事故方面，美国各地拟议的安全干预措施的好处。该研究通过测试150名年龄在65至85岁之间的驾驶员来实现这些目标，其中包括75名UFOV受损的驾驶员和75名无损伤的对照驾驶员。这些小组将被随机分组，三分之二的人接受干预，三分之一的人作为对照。这些实验利用高保真驾驶模拟器、最先进的仪器车辆和复杂的认知测试工具，通过车载解决方案系统地解决驾驶员损伤问题，提高驾驶员监控信息、分配注意力以及做出准确及时决策的能力。研究小组成员是人因工程、神经学、生物统计学、计算机图形学和认知科学方面的专家。作为这些研究的结果，我们希望（1）为具有UFOV损失的风险驾驶员提供有前途的碰撞警告设备的建议，（2）开发关于有碰撞风险的人的真实驾驶策略的基本信息，（3）了解基本警告信号参数的有效性以及它们在认知受损和正常驾驶员中的差异，（4）开发工具，以便在有各种医疗缺陷的驾驶员中进行可能的临床试验。研究结果可用于设计更安全的汽车，使用新的策略和技术进行碰撞警告，感知增强和注意力管理。

DESCRIPTION (provided by applicant): The broad goals of this research project are to identify patterns of driving performance and safety errors resulting from age-related impairments in the useful field of view (UFOV), and to develop in-vehicle countermeasures that decrease crash risk and increase safety and mobility. The specific aims are to 1) determine optimal signal parameters for a driver alerting and collision warning device, 2) test the efficacy of the device in improving driver reactions in challenging situations in a high- fidelity driving simulator and on-road in an instrumented vehicle (IV); 3) characterize on-road driving performance, and search behavior and compensatory strategies, in at-risk drivers with UFOV impairments and comparison subjects operating an IV; 4) assess the effects of the safety intervention in at-risk drivers and comparison subjects on the road in the IV; and 5) estimate the benefits of the proposed safety interventions across the U.S. in terms of crashes averted. The research addresses these aims by testing 150 driver's ages 65 to 85 years, including 75 drivers with UFOV impairment and 75 comparison drivers without impairment. The groups will be randomized so than two-thirds receive the intervention and one-third act as controls. The experiments utilize a high-fidelity driving simulator, a state-of-the art instrumented vehicle, and sophisticated cognitive testing tools to systematically address driver impairments with in-vehicle solutions that improve a driver's ability to monitor information, allocate attention, and make accurate and timely decisions. The research team members are experts in human factors engineering, neurology, biostatistics, computer graphics, and cognitive science. As an outcome of these studies, we expect to (1) make recommendations on promising collision warning devices for at-risk drivers with UFOV loss, (2) develop basic information on real-world driving strategies in people at risk for collisions, (3) understand the efficacy of basic warning signal parameters and how they differ in cognitively impaired and normal drivers, (4) develop tools for possible clinical trials in drivers who have a variety of medical impairments. The results may be used to design safer automobiles that use new strategies and technologies for collision warning, perceptual augmentation, and attention management.