In-Car Technology to Alert Attention-Imparied Drivers

这项研究项目的主要目标是确定驾驶性能和安全错误的模式 由年龄相关的有用视野(UFOV)损害造成的，并开发车载 降低碰撞风险、提高安全性和机动性的对策。 具体目标是1)确定驾驶员警报和碰撞警告的最佳信号参数 设备，2)测试设备在高难度情况下改善驾驶员反应的有效性- Fidelity驾驶模拟器和仪表车的道路行驶(IV)；3)道路驾驶的特征 UFOV损害的高危司机的表现、搜索行为和补偿策略 4)评估高危司机安全干预的效果，以及 在IV中比较道路上的受试者；以及5)估计拟议安全干预措施的益处 在避免撞车事故方面，整个美国都是如此。 这项研究通过测试150名年龄在65岁至85岁之间的司机来满足这些目标，其中包括75名患有 UFOV减损和75个无减损的比较驱动器。这些小组将被随机分组，以便 三分之二的人接受干预，三分之一的人作为对照。这些实验利用了高保真 驾驶模拟器，一种最先进的仪表化车辆，以及复杂的认知测试工具 通过提高驾驶员监控能力的车载解决方案，系统地解决驾驶员的缺陷问题 信息，分配注意力，并做出准确和及时的决定。研究小组的成员是 人类因素工程学、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，我们期望(1)就有希望的碰撞警告提出建议 针对UFOV丢失的高风险司机的设备，(2)开发关于现实世界驾驶策略的基本信息 有碰撞风险的人，(3)了解基本警告信号参数的有效性以及它们如何 在认知障碍司机和正常司机方面存在差异，(4)开发工具，用于可能对以下司机进行临床试验 有各种医疗缺陷。这一结果可能被用于设计使用新技术的更安全的汽车 碰撞警告、感知增强和注意力管理的策略和技术。

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 drivers 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 ahigh-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.