智能网联汽车（ICV）代表着未来汽车产业的发展方向和汽车出行科技集群的战略制高点，正步入快速发展期，对相关新型人才产生了迫切需求。但ICV产业具有高度的复杂性和不确定性，且缺乏明确边界和历史数据，预测ICV人才需求的难度极大。为科学预测ICV人才需求，本文采用定性与定量研究相结合的方法，确定了ICV产业人才的结构，明确了以研发技术人才为预测对象，构建了分层级多指标的ICV人才需求预测模型，并基于情景分析，得出了未来五年ICV产业人才的需求量，并分析了ICV产业不同类型研发技术人才以及不同业务模块人才的需求差异。

The intelligent connected vehicle (ICV) industry, which is entering a period of rapid development, represents the future development direction of the automobile industry and the strategic commanding heights of the automobile travel technology cluster. As ICV industry includes multiple fields and involves many other technologies besides automotive technologies, it is in urgent need of a large number of talents. However, due to complexity and uncertainty of ICV industry and the lack of clear boundaries and historical data of relevant talents, it is of extreme difficulty to predict the demand for ICV talents. In order to predict it accurately, both qualitative and quantitative research methods were used to determine the talent structure. Based on the technologies of ICV, the structure of ICV talents was built up, including leading talents, research and development talents, manufacturing talents, sales service talents and other talents. Research and development talents, which is divided into vehicle architecture engineers, system/module architecture engineers, software development engineers, hardware development engineers, data and algorithm engineers as well as test and calibration engineers, were chosen to be the quantitative prediction objects. A hierarchical model with 3 first-grade indexes and 14 second-grade indexes was established to quantitatively predict the talent demand of ICV industry. Besides, a scenario analysis assuming that ICV industry would develop in optimistic, normal or pessimistic scenario was carried out on the development level of ICV industry to give a prediction on the talent demand of ICV industry in the next five years. And the differences of each type of research and development talents of ICV industry and talent demand of different business sectors were analyzed.

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2020智能网联汽车人才需求预测，2020RC14；国家自然科学基金“汽车智能化对安全、节能减排及缓解拥堵影响的系统评估方法”，项目编号U1764265