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

FW-HTF-RL：为自动驾驶汽车时代的未来劳动力做好准备

• 批准号: 1928422
• 负责人: Shelia Cotten
• 金额: $ 250万
• 依托单位: Michigan State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2020-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1928422&HistoricalAwards=false
• 关键词: FW; HTF; RL; Preparing; Future

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.

随着美国向自动驾驶汽车的过渡，数百万美国人的工作岗位将直接受到影响，包括更换工人以及使用和维护自动驾驶汽车所需的工作要求的变化，以及间接受到组织、生活水平和工人福利的变化。向自动驾驶汽车的过渡将影响就业渠道、驾驶职业的薪酬以及司机的生活质量。这些相互关联和潜在的普遍经济，社会和政策变化需要跨学科的合作方法来研究谁，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破坏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人员做好准备，以应对随着自动驾驶汽车的广泛传播以及美国自动驾驶汽车的最终饱和而发生的转变。研究人员从组织心理学、经济学、社会学、地理学、技术和运输工程学等领域入手，分析了自动驾驶汽车对两种驾驶职业（出租车/打车海陵、重型卡车和拖拉机拖车）的影响。调查人员利用混合方法的方法，重点是司机，监督员和管理人员，以检查：（1）如何将驾驶工作的变化，以响应车辆的自动化和什么新的技能将需要？(2)工人们有多大的意愿和能力去适应驾驶工作性质的变化，工作性质的变化是否会使某些工人群体比其他人更处于不利地位？(3)对驱动因素的预期下游影响是什么（例如，就业趋势和收入不平等）在运输行业，组织和社会？焦点小组，调查和技能映射技术将用于识别最有可能被取代的驾驶职业，以及由于自动驾驶汽车的采用而需要广泛再培训的职业。技能地图和二级职业数据将与技术扩散模型相结合，以估计两种驾驶环境的采用水平。这些数据还将用于估计经济模型，以了解失业，工资下降以及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，以减轻劳动力的流失，并帮助工人在自动驾驶汽车时代获得劳动力培训和再技能。该项目的成果将通过访问地区高中和YouTube频道与利益相关者分享网络研讨会和培训视频，向更广泛的社区传播。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。