FW-HTF-RL: Preparing the Future Workforce for the Era of Automated Vehicles

随着美国向自动车辆过渡，数以百万计的美国工作岗位将通过更换工人和改变使用和维护自动车辆所需的工作要求直接受到影响，也将间接通过组织、生活标准和工人福利的变化受到影响。向自动化汽车的过渡将影响就业渠道、驾驶职业的薪酬和司机的生活质量。这些相互关联且可能无处不在的经济、社会和政策变化需要跨学科、协作的方法，以检查谁、如何以及为什么工人和社会将受到这种转变的影响。这些知识将使组织、工人和政策制定者为这种潜在的颠覆性技术带来的工作场所变化做好准备，并为应对这些工作场所变化对社会的间接影响所需的政策变化提供信息。这项研究的长期目标是为当前和未来的驾驶劳动力做好准备，以应对随着更广泛的自动车辆传播以及美国最终出现自动车辆饱和而发生的转变。调查人员从组织心理学、经济学、社会学、地理学、技术学和交通工程学等方面分析了自动化车辆将对谁、为什么以及两种驾驶职业(出租车/叫车、重型卡车和拖拉机拖车)产生影响。调查人员采用了一种以司机、主管和管理人员为重点的混合方法来考察：(1)驾驶工作将如何因车辆自动化而改变，需要哪些新技能？(2)工人有多大意愿和能力来适应不断变化的驾驶工作性质，工作性质的变化是否会使某些工人群体比其他群体更不利？(3)在交通运输行业、组织和社会中，预计对司机的下游影响是什么(例如，就业趋势和收入不平等)？将使用焦点小组、调查和技能绘图技术来确定最有可能流离失所的驾驶职业，以及由于采用自动化车辆而需要广泛再培训的职业。技能地图和次要职业数据将与技术扩散模型结合起来，以估计两种驾驶环境的采用率。这些数据还将被用来估计经济模型，以了解失业、减薪以及在利益驱动背景下技能变化对社会和收入不平等的影响。技能地图将分发给劳动力和教育团体，他们可以开发工作培训和证书计划，以减少劳动力流离失所，并帮助工人在自动化车辆时代获得劳动力培训和重新技能。该项目的结果将通过访问地区高中和YouTube频道向更广泛的社区传播，与利益相关者分享网络研讨会和培训视频。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

As the United States transitions to automated vehicles, millions of American jobs will be impacted directly through the replacement of workers and changes in job requirements needed to work with and maintain automated vehicles, as well as indirectly through changes in organizations, standards of living, and worker well-being. The transition to automated vehicles will impact the employment pipeline, pay for driving occupations, and quality of life for drivers. These interrelated and potentially pervasive economic, social, and policy changes require interdisciplinary, collaborative approaches to examine who, how, and why workers and society will be impacted by this transition. This knowledge will prepare organizations, workers, and policymakers for workplace changes brought by this potentially disruptive technology and inform policy changes needed to deal with the indirect impacts of these workplace changes on society. The long-term goal of this research study is to prepare the current and future driving workforce for the shift that will occur as broader automated vehicle dissemination occurs and eventual automated vehicle saturation is seen in the United States. The investigators draw from organizational psychology, economics, sociology, geography, technology, and transportation engineering to analyze who, why, and how two driving occupations (taxi/ride hailing and heavy trucking and tractor trailers) will be affected by automated vehicles. The investigators utilize a mixed-method approach focusing on drivers, supervisors, and management to examine: (1) How will driving jobs change in response to automation of vehicles and what new skills will be required? (2) How willing and able are workers to adapt to the changing nature of driving jobs, and will the changing nature of jobs disadvantage some groups of workers more so than others? (3) What are the anticipated downstream impacts on drivers (e.g., employment trends and income inequality) in the transportation industry, organizations, and society? Focus groups, surveys, and skill mapping techniques will be used to identify driving occupations most at risk for displacement and the occupations that will require extensive retraining due to automated vehicle adoption. The skill maps and secondary occupational data will be incorporated with technology diffusion models to estimate adoption levels across the two driving contexts. These data will also be used to estimate economic models to understand job loss, wage reductions, and the impact of skills changes in the driving contexts of interest on society and income inequality. Skills maps will be disseminated to workforce and education groups who can develop job training and certificate programs to mitigate workforce displacement and help workers obtain workforce training and reskilling in the age of automated vehicles. Results of this project will be disseminated to the broader community via visits to area high schools and a YouTube channel to share webinars and training videos with stakeholders.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.