Department of Creative Product Design, Southern Taiwan University of Science and Technology, Tainan, Taiwan.
Department of Neurology, National Taiwan University Hospital (Yunlin Branch), Yunlin, Taiwan; Department of Biomedical Engineering, National Cheng Kung University, Tainan, Taiwan.
Accid Anal Prev. 2021 Nov;162:106425. doi: 10.1016/j.aap.2021.106425. Epub 2021 Oct 1.
Automated driving is a developing trend that is coming to the consumer market, and conditionally automated driving (CAD) is anticipated to become the primary automated driving system. For enhancing both the comfort and security of human drivers in self-driving cars, the most significant concern of CAD is ensuring that not only can the driver conduct non-driving related tasks (NDRT) while automated driving is in progress, but also quickly and competently take over when the system reaches a limit and issues a takeover request (TOR). However, the level of distraction by NDRTs may affect the transition from automated driving to the human driver taking over. The focus of the present study was allowing a driver immersed in NDRTs to discover the TOR and take control of the driving quickly. A 3×2×2 factor experimental design was used: vehicle display interface information load (basic vs. prediction vs. advanced prediction interfaces); TOR information load (directional vs. non-directional information notifications); and degree of NDRT immersion (not performing vs. performing an NDRT when TOR prompt was issued). 48 participants were recruited, and different automotive display interfaces were used as TOR prompts with different information loads during driving to analyze the takeover behavior, performance, and subjective perception of the drivers, who were immersed in a smartphone-related task. The takeover process out of NDRT immersion was found to be more efficient with the advanced prediction interface, compared to the other two interfaces. All groups achieved faster takeovers and demonstrated better takeover performance if given directional rather than non-directional information, regardless of interface type or NDRT immersion.
自动驾驶是一个发展趋势，正逐渐进入消费市场，预计有条件自动驾驶（CAD）将成为主要的自动驾驶系统。为了提高自动驾驶汽车中人类驾驶员的舒适性和安全性，CAD 最关注的问题是确保驾驶员不仅可以在自动驾驶进行时执行与驾驶无关的任务（NDRT），而且在系统达到极限并发出接管请求（TOR）时能够快速而熟练地接管。然而，NDRT 的分心程度可能会影响从自动驾驶到驾驶员接管的过渡。本研究的重点是让沉浸在 NDRT 中的驾驶员发现 TOR 并快速接管驾驶。采用了 3×2×2 因素实验设计：车辆显示界面信息负载（基本、预测、高级预测界面）；TOR 信息负载（方向和非方向信息通知）；以及 NDRT 沉浸程度（执行和不执行 TOR 提示时执行 NDRT）。招募了 48 名参与者，使用不同的汽车显示界面作为 TOR 提示，并在驾驶过程中使用不同的信息负载来分析驾驶员的接管行为、性能和主观感知，驾驶员在智能手机相关任务中沉浸其中。与其他两种界面相比，高级预测界面下，从 NDRT 沉浸中接管的过程效率更高。无论接口类型或 NDRT 沉浸程度如何，所有组在接收到方向信息而非非方向信息时，都能更快地接管并表现出更好的接管性能。
