Colorado State University, Fort Collins.
University of Washington, Seattle.
Hum Factors. 2019 Feb;61(1):152-164. doi: 10.1177/0018720818800538. Epub 2018 Sep 20.
A driving simulator study was conducted to evaluate the longitudinal effects of an intervention and withdrawal of a lane keeping system on driving performance and cognitive workload.
Autonomous vehicle systems are being implemented into the vehicle fleet. However, limited research exists in understanding the carryover effects of long-term exposure.
Forty-eight participants (30 treatment, 18 control) completed eight drives across three separate days in a driving simulator. The treatment group had an intervention and withdrawal of a lane keeping system. Changes in driving performance (standard deviation of lateral position [SDLP] and mean time to collision [TTC]) and cognitive workload (response time and miss rate to a detection response task) were modeled using mixed effects linear and negative binomial regression.
Drivers exposed to the lane keeping system had an increase in SDLP after the system was withdrawn relative to their baseline. Drivers with lane keeping had decreased mean TTC during and after system withdrawal compared with manual drivers. There was an increase in cognitive workload when the lane keeping system was withdrawn relative to when the system was engaged.
Behavioral adaptations in driving performance and cognitive workload were present during automation and persisted after the automation was withdrawn.
The findings of this research emphasize the importance to consider the effects of skill atrophy and misplaced trust due to semi-autonomous vehicle systems. Designers and policymakers can utilize this for system alerts and training.
本驾驶模拟器研究旨在评估车道保持系统干预和退出对驾驶性能和认知负荷的纵向影响。
自动驾驶汽车系统正在被应用于汽车领域。然而，对于长期暴露的后续影响，相关研究仍十分有限。
48 名参与者（30 名治疗组，18 名对照组）在驾驶模拟器中进行了三次为期三天的八次驾驶。治疗组的车道保持系统会进行干预和退出。驾驶性能（横向位置标准差 [SDLP] 和碰撞平均时间 [TTC]）和认知负荷（检测响应任务的响应时间和错误率）的变化使用混合效应线性和负二项回归进行建模。
与基线相比，暴露于车道保持系统的驾驶员在系统退出后 SDLP 增加。与手动驾驶的驾驶员相比，在系统退出期间和之后，车道保持的驾驶员 TTC 均值降低。与系统启用时相比，当车道保持系统退出时，认知负荷增加。
在自动化期间和自动化退出后，驾驶性能和认知负荷方面存在行为适应性。
本研究结果强调了考虑因半自动汽车系统导致的技能萎缩和信任错位的重要性。设计师和政策制定者可以利用这一点进行系统警报和培训。
