笔记本电脑
提前期
铅(地质)
控制(管理)
任务(项目管理)
自动化
计算机安全
Web应用程序
控制室
订单(交换)
计算机科学
工程类
模拟
作者
Xiaomei Tan,Yiqi Zhang
标识
DOI:10.1016/j.aap.2022.106593
摘要
Conditional automation systems allow drivers to turn their attention away from the driving task in certain scenarios but still require drivers to gain situation awareness (SA) upon a takeover request (ToR) and resume manual control when the system is unable to handle the upcoming situation. Unlike time-critical takeover situations in which drivers must respond within a relatively short time frame, the ToRs for non-critical events such as exiting from a freeway can be scheduled way ahead of time. It is unknown how the ToR lead time affects driver SA for resuming manual control and when to send the ToR is most appropriate in non-critical takeover events. The present study conducted a web-based, supervised experiment with 31 participants using conditional automation systems in freeway existing scenarios while playing a mobile game. Each participant experienced 12 trials with different ToR lead times (6, 8, 10, 12, 14, 16, 18, 20, 25, 30, 45, and 60 s) for exiting from freeways in a randomized order. Driver SA was measured by using a freeze probe technique in each trial when the participant pressed the spacebar on the laptop to simulate the takeover action. Results revealed a positive effect of longer ToR lead times on driver SA for resuming control to exit from freeways and the effect leveled off at the lead time of 16-30 s. The participants tended to postpone their takeover actions further when they were given a longer ToR lead time and it did not level off up to 60 s. Nevertheless, not all drivers waited till the last moment to take over AVs even though they did not get sufficient SA. The ToR lead time of 16-30 s was recommended for better SA; and it could be narrowed down to 25-30 s if considering the subjective evaluations on takeover readiness, workload, and trust. The findings provide implications for the future design of conditional automation systems used for freeway driving.
科研通智能强力驱动
Strongly Powered by AbleSci AI