Pai Ganesh, Zhang Fangda, Hungund Apoorva P, Pamarthi Jaji, Roberts Shannon C, Horrey William J, Pradhan Anuj K
Department of Mechanical and Industrial Engineering, 160 Governors Drive, University of Massachusetts Amherst, Amherst, MA 01003, United States.
The Center for Injury Research and Policy, Abigail Wexner Research Institute at Nationwide Children's Hospital, 700 Children's Drive, RB3, Columbus, OH 43205, United States.
Accid Anal Prev. 2023 Sep;190:107130. doi: 10.1016/j.aap.2023.107130. Epub 2023 Jun 17.
Advanced Driver Assistance Systems (ADAS) support drivers with some driving tasks. However, drivers may lack appropriate knowledge about ADAS resulting in inadequate mental models. This may result in drivers misusing ADAS, or mistrusting the technologies, especially after encountering edge-case events (situations beyond the capability of an ADAS where the system may malfunction or fail) and may also adversely affect driver workload. Literature suggests mental models could be improved through exposure to ADAS-related driving situations, especially those related to ADAS capabilities and limitations. The objective of this study was to examine the impact of frequency and quality of exposure on drivers' understanding of Adaptive Cruise Control (ACC), their trust, and their workload after driving with ACC. Sixteen novice ACC users were recruited for this longitudinal driving simulator study. Drivers were randomly assigned to one of two groups - the 'Regular Exposure' group encountering 'routine' edge-case events, and the 'Enhanced Exposure' group encountering 'routine' and 'rare' events. Each participant undertook four different simulator sessions, each separated by about a week. Each session comprised a simulator drive featuring five edge-case scenarios. The study followed a mixed-subject design, with exposure frequency as the within-subject variable, and quality of exposure (defined by two groups) as the between-subject variable. Surveys measured drivers' trust, workload, and mental models. The results from the analyses using linear regression models revealed that drivers' mental models about ACC improve with frequency of exposure to ACC and associated edge-case driving situations. This was more the case for drivers who experienced 'rare' ACC edge cases. The findings also indicate that for those who encountered 'rare' edge cases, workload was higher and trust was lower than those who did not. These findings are significant since they underline the importance of experience and familiarity with ADAS for safe operation. While these findings indicate that drivers benefit from increased exposure to ACC and edge cases in terms of appropriate use of ADAS, and ultimately promise crash reductions and injury prevention, a challenge remains regarding how to provide drivers with appropriate exposure in a safe manner.
先进驾驶辅助系统（ADAS）可协助驾驶员完成一些驾驶任务。然而，驾驶员可能对ADAS缺乏足够的了解，从而导致心理模型不完善。这可能会导致驾驶员滥用ADAS，或者不信任这些技术，尤其是在遇到极端情况（超出ADAS能力范围，系统可能出现故障或失效的情况）之后，并且还可能对驾驶员的工作量产生不利影响。文献表明，通过接触与ADAS相关的驾驶场景，尤其是那些与ADAS能力和局限性相关的场景，可以改善心理模型。本研究的目的是检验接触频率和质量对驾驶员在使用自适应巡航控制（ACC）驾驶后对ACC的理解、信任以及工作量的影响。招募了16名ACC新手用户参与这项纵向驾驶模拟器研究。驾驶员被随机分配到两个组中的一组——“常规接触”组会遇到“常规”极端情况，“强化接触”组会遇到“常规”和“罕见”情况。每位参与者进行了四次不同的模拟器训练，每次训练间隔约一周。每次训练包括一次模拟器驾驶，其中有五个极端情况场景。该研究采用混合主体设计，将接触频率作为主体内变量，将接触质量（由两组定义）作为主体间变量。通过调查来衡量驾驶员的信任、工作量和心理模型。使用线性回归模型进行分析的结果显示，驾驶员对ACC的心理模型会随着接触ACC及相关极端情况驾驶场景的频率而改善。对于经历过“罕见”ACC极端情况的驾驶员来说更是如此。研究结果还表明，对于那些遇到“罕见”极端情况的驾驶员，其工作量比未遇到的驾驶员更高，信任度更低。这些发现具有重要意义，因为它们强调了对ADAS的经验和熟悉程度对于安全操作的重要性。虽然这些发现表明，驾驶员在正确使用ADAS方面受益于更多地接触ACC和极端情况，最终有望减少碰撞和预防伤害，但在如何以安全的方式为驾驶员提供适当的接触方面仍然存在挑战。
