International Journal of Human-Computer Studies ( IF 5.4 ) Pub Date : 2020-08-04 , DOI: 10.1016/j.ijhcs.2020.102518 Kuan-Ting Chen , Huei-Yen Winnie Chen
With increasing vehicle automation, drivers are likely to engage in non-driving related tasks. However, before fully autonomous vehicles can be achieved, drivers remain the fallback when automation limits are reached and/or in unexpected situations. Transition from automated to manual control could be particularly difficult for drivers who are “out-of-the-loop”. To support safe and smooth control transition, we adopted the blended sonification approach in manipulating background music to convey information about the reliability level of the automation. A driving simulator study consisting of 36 participants was conducted to investigate the effectiveness of sonification feedback—three levels of system reliability was mapped to background music pitch levels—on takeover events. Participants were assigned to one of three groups that received continuous sonification feedback, intermittent sonification feedback, or no feedback about automation reliability. The proposed blended music sonification was found effective in increasing monitoring behavior and decreasing visual response times to takeover requests during automated driving. Steering control in a takeover scenario that required substantial amount of maneuvering also benefitted from having sonification feedback. There was some evidence that sonification feedback provided continuously was more effective than when it was provided intermittently, possibly related to observed differences in the associated glance patterns. In conclusion, blended sonification may be an effective approach in helping drivers stay on the loop during automated driving, although the study was limited to the choice of music examined. Our future work will investigate how best to combine automation reliability information and traffic situational information to better support situation awareness and facilitate task-switching in automated driving.
中文翻译:
在有条件的自动驾驶中操纵音乐以传达自动化可靠性:驾驶模拟器研究
随着车辆自动化程度的提高,驾驶员可能会从事与非驾驶相关的任务。但是,在可以实现全自动驾驶的车辆之前,当达到自动化限制和/或在意外情况下,驾驶员仍然处于后备状态。对于“圈外”的驾驶员而言,从自动控制到手动控制的过渡可能特别困难。为了支持安全平稳的控制过渡,我们在处理背景音乐时采用了混合超声处理方法,以传达有关自动化可靠性级别的信息。进行了一项由36名参与者组成的驾驶模拟器研究,以研究在接管事件中超声反馈的有效性(将系统可靠性的三个级别映射到背景音乐的音调级别)。参与者被分配到三个组中的一组,这些组收到连续的超声反馈,间歇的超声反馈或没有关于自动化可靠性的反馈。发现建议的混合音乐超音波可有效提高自动驾驶过程中的监视行为并减少对接管请求的视觉响应时间。在需要大量操纵的接管情况下,转向控制也得益于声音反馈。有证据表明,连续提供的回声反馈比间歇性提供的回声反馈更有效,这可能与观察到的相关扫视模式的差异有关。总之,混合声处理可能是一种有效的方法,可以帮助驾驶员在自动驾驶过程中保持领先,尽管这项研究仅限于所检查音乐的选择。我们未来的工作将研究如何最好地结合自动化可靠性信息和交通状况信息,以更好地支持状况感知并促进自动驾驶中的任务切换。