Texas A&M University, College Station, USA.
Virginia Tech Transportation Institute, Blacksburg, USA.
Hum Factors. 2019 Jun;61(4):642-688. doi: 10.1177/0018720819829572. Epub 2019 Mar 4.
This article provides a review of empirical studies of automated vehicle takeovers and driver modeling to identify influential factors and their impacts on takeover performance and suggest driver models that can capture them.
Significant safety issues remain in automated-to-manual transitions of vehicle control. Developing models and computer simulations of automated vehicle control transitions may help designers mitigate these issues, but only if accurate models are used. Selecting accurate models requires estimating the impact of factors that influence takeovers.
Articles describing automated vehicle takeovers or driver modeling research were identified through a systematic approach. Inclusion criteria were used to identify relevant studies and models of braking, steering, and the complete takeover process for further review.
The reviewed studies on automated vehicle takeovers identified several factors that significantly influence takeover time and post-takeover control. Drivers were found to respond similarly between manual emergencies and automated takeovers, albeit with a delay. The findings suggest that existing braking and steering models for manual driving may be applicable to modeling automated vehicle takeovers.
Time budget, repeated exposure to takeovers, silent failures, and handheld secondary tasks significantly influence takeover time. These factors in addition to takeover request modality, driving environment, non-handheld secondary tasks, level of automation, trust, fatigue, and alcohol significantly impact post-takeover control. Models that capture these effects through evidence accumulation were identified as promising directions for future work.
Stakeholders interested in driver behavior during automated vehicle takeovers may use this article to identify starting points for their work.
本文回顾了自动驾驶接管和驾驶员建模的实证研究，以确定影响因素及其对接管性能的影响，并提出能够捕捉这些因素的驾驶员模型。
在自动驾驶向手动驾驶的车辆控制转换过程中，仍存在重大安全问题。开发自动驾驶控制转换的模型和计算机模拟可能有助于设计人员缓解这些问题，但前提是使用准确的模型。选择准确的模型需要估计影响接管的因素的影响。
通过系统方法确定描述自动驾驶接管或驾驶员建模研究的文章。使用纳入标准来识别相关的研究和制动、转向以及完整接管过程的模型，以进行进一步审查。
对自动驾驶接管的回顾性研究确定了几个显著影响接管时间和接管后控制的因素。研究发现，驾驶员在手动紧急情况下和自动驾驶接管中的反应相似，尽管存在延迟。研究结果表明，现有的手动驾驶制动和转向模型可能适用于自动驾驶接管的建模。
时间预算、重复接触接管、无声故障和手持次要任务显著影响接管时间。除接管请求模式、驾驶环境、非手持次要任务、自动化程度、信任、疲劳和酒精之外，这些因素还显著影响接管后的控制。通过证据积累来捕捉这些影响的模型被确定为未来工作的有前途的方向。
对自动驾驶接管期间驾驶员行为感兴趣的利益相关者可以使用本文来确定他们工作的起点。
