针对听力障碍驾驶员的多模态接管请求：自动驾驶汽车中AI驱动通信的启示
Multimodal Takeover Requests for Drivers with Hearing Loss: Implications for AI-Enabled Communication in Automated Vehicles
浏览论文内容
中文总结 AI 辅助
该研究针对听力障碍驾驶员，通过驾驶模拟器实验发现视觉-触觉接管显示可缩短反应与接管时间，AI驱动汽车可据此调整接管消息内容以适配不同需求。
中文摘要 AI 辅助
全球有超过4.3亿人患有致残性听力障碍。尽管听力障碍者在法律上被允许驾驶，且可能从有条件自动驾驶汽车中获益，但SAE Level 3系统仍要求驾驶员在自动化系统达到极限时响应接管请求。现有的接管请求通常依赖听觉信息，但针对无法依赖声音的驾驶员，关于视觉和触觉设计的证据很少。这项包含40名参与者的驾驶模拟器研究，考察了信息类型（指导性、信息性和基线）、信号类型（视觉、触觉以及视觉-触觉）和听力状况（正常听力和模拟听力障碍）对接管性能的影响。信息类型显著影响反应时间，其中基线显示产生的时间最短；信号类型显著影响反应时间和接管时间，其中视觉-触觉显示产生的时间最短；信号类型与信息类型的交互对所有三项指标均显著，且在每种信息类型下，视觉-触觉显示都产生最短的反应时间。在视觉-触觉信号下，简单的基线警报促使反应最快且操作最急促，而信息性内容产生的平均最大加速度最低。听力状况对任何指标均无显著主效应。这些发现表明，AI驱动的汽车可通过视觉-触觉显示支持紧急接管通信，并可根据可用时间和所需操作质量调整消息内容，对不同听力能力的驾驶员均具有启示意义。
英文摘要
More than 430 million people worldwide live with disabling hearing loss. Although people with hearing loss are legally permitted to drive and may benefit from conditionally automated vehicles, SAE Level 3 systems still require drivers to respond to takeover requests when automation reaches its limits. Existing takeover requests often rely on auditory information, yet little evidence addresses visual and tactile designs for drivers who cannot rely on sound. This driving-simulator study with 40 participants examined the effects of information type (instructional, informative, and baseline), signal type (visual, tactile, and visual-tactile), and hearing condition (normal hearing and simulated hearing impairment) on takeover performance. Information type significantly affected reaction time, with baseline displays producing the shortest times. Signal type significantly affected reaction and takeover time, with visual-tactile displays producing the shortest times. The interaction between signal type and information type was significant for all three measures. Visual-tactile displays produced the shortest reaction times within every information type. With visual-tactile signaling, simple baseline alerts prompted the fastest reactions and the most abrupt maneuvers, whereas informative content produced the lowest mean maximum resulting acceleration. Hearing condition showed no significant main effect on any measure. These findings suggest that AI-enabled vehicles can support urgent takeover communication through visual-tactile displays and can adapt message content to the time available and the maneuver quality required, with implications for drivers across hearing abilities.
发表机构
- San Jose State University（圣何塞州立大学）
机构由 AI 辅助整理，请以论文原文为准。
