Automated driving experiences, attention, and intentions following extensive on-road usage of a level 2 automation vehicle

Journal of Safety Research(2024)

引用 0|浏览1
暂无评分
摘要
Introduction: An on-road study was conducted to examine the effects of level 2 automation on the stressfulness and enjoyment of driving and driving attention following prolonged usage. The study also examined the changes in the automated driving experience and attention over time as well as important predictors such as pre-driving trust in technology and attitudes toward automated systems. Method: Motorists who had never used automated systems drove a level 2 automation vehicle for a 6–8 week period. Results: Participants reported that the automated systems reduced the stress of driving and made traveling more enjoyable and relaxing. They also reported that the automation did not make traveling boring and take the fun out of driving. Participants indicated that their minds tended to wander when the automation was operating. The stressfulness of the automated driving experience decreased over time. Participants also reported feeling increasingly comfortable driving with the automation without monitoring it closely. The enjoyment and stress of automated driving is important because it shapes the willingness to use the automation and, hence, the safeness of driving. As expected, intentions to use and purchase automated systems were strongly predicted by the perceived favorableness of driving with the automation. Participants’ pre-driving beliefs about automated systems, rather than their trust, appears to have shaped their experiences with the automation. Practical Applications: Although some of the findings suggest that automated systems increase unsafe behavior by novice users, other facets of the surveys suggest that motorists are cognizant of the risks of automated driving and discreet in their usage of the automation.
更多
查看译文
关键词
Level 2 automation vehicles,Driving enjoyment,Driving stress,Driving attention,Trust in automated systems
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要