封硕 - 清华大学 - 自动化系

个人简介

教育背景 2014年-2019年，自动化系，获工学博士学位 2017年-2019年，美国密西根大学，访问学生 2010年-2014年，清华大学，自动化系，获工学学士学位工作履历 2022年-至今，清华大学自动化系，助理教授 2021年-2022年，美国密西根大学，助理研究员 2019年-2021年，美国密西根大学，博士后学术兼职 2022年至今，IEEE Transactions on Intelligent Vehicles，Associate Editor 2021年至今，Automotive Innovation， Academic Editor 奖励与荣誉 2021年，美国运筹与管理协会智能交通系统年度最佳论文奖 2020年，IEEE智能交通系统学会最佳博士学位论文奖 2020年，清华大学优秀博士论文 2020年，清华大学优秀毕业生

研究领域

智能系统测试验证

近期论文

查看导师最新文章（温馨提示：请注意重名现象，建议点开原文通过作者单位确认）

Feng, S., Sun, H., Yan, X., Zhu, H., Zou, Z., Shen, S. and Liu, H.X., 2023. Dense reinforcement learning for safety validation of autonomous vehicles. Nature 615, 620–627 (2023). (Cover Article) (Tsinghua Press Release: https://www.tsinghua.edu.cn/info/1175/102314.htm, 光明日报：https://tech.gmw.cn/2023-04/12/content_36491293.htm, Nature News, Nature Podcast, Nature Videos, TechXplore, ScienceDaily, More Media Coverage：https://nature.altmetric.com/details/144188044/news) Feng, S., Yan, X., Sun, H., Feng, Y. and Liu, H.X., 2021. Intelligent driving intelligence test for autonomous vehicles with naturalistic and adversarial environment. Nature Communications, 12, 748 (2021). (Featured Article) (TechXplore: https://techxplore.com/news/2021-02-intelligence-autonomous.html) Yan, X., Zou, Z., Feng, S., Zhu, H., Sun, H. and Liu, H.X, 2023. Learning naturalistic driving environment with statistical realism. Nature Communications, 14, 2037 (2023). (Featured Article) Feng, S., Feng, Y., Yu, C., Zhang, Y. and Liu, H.X., 2020. Testing scenario library generation for connected and automated vehicles, Part I: Methodology. IEEE Transactions on Intelligent Transportation Systems. DOI: 10.1109/TITS.2020.2972211. Feng, S., Feng, Y., Sun, H., Bao, S., Zhang, Y. and Liu, H.X., 2020. Testing scenario library generation for connected and automated vehicles, Part II: Case studies. IEEE Transactions on Intelligent Transportation Systems. DOI: 10.1109/TITS.2020.2988309. Feng, S., Feng, Y., Sun, H., Zhang, Y. and Liu, H.X., 2020. Testing scenario library generation for connected and automated vehicles: An adaptive framework. IEEE Transactions on Intelligent Transportation Systems. DOI: 10.1109/TITS.2020.3023668. Feng, S., Sun, H., Zhang, Y., Zheng, J., Liu, H.X. and Li, L., 2019. Tube-based discrete controller design for vehicle platoons subject to disturbances and saturation constraints. IEEE Transactions on Control Systems Technology, 28(3), pp.1066-1073. Feng, S., Song, Z., Li, Z., Zhang, Y. and Li, L., 2021. Robust Platoon Control in Mixed Traffic Flow Based on Tube Model Predictive Control. IEEE Transactions on Intelligent Vehicles. DOI: 10.1109/TIV.2021.3060626. Pei, H., Zhang, Y., Zhang, Y. and Feng, S.*, 2021. Optimal cooperative driving at signal-free intersections with polynomial-time complexity. IEEE Transactions on Intelligent Transportation Systems. 23 (8), 12908-12920. Pei, H., Feng, S.*, Zhang, Y*. and Yao, D., 2019. A Cooperative Driving Strategy for Merging at On-Ramps Based on Dynamic Programming. IEEE Transactions on Vehicular Technology, 68(12), pp.11646-11656. Feng, S., Feng, Y., Yan, X., Shen, S., Xu, S. and Liu, H.X., 2020. Safety assessment of highly automated driving systems in test tracks: A new framework. Accident Analysis & Prevention, 144, p.105664. Feng, S., Zhang, Y., Li, S.E., Cao, Z., Liu, H.X. and Li, L., 2019. String stability for vehicular platoon control: Definitions and analysis methods. Annual Reviews in Control, 47, pp.81-97. Feng, S., Wang, X., Sun, H., Zhang, Y. and Li, L., 2018. A better understanding of long-range temporal dependence of traffic flow time series. Physica A: Statistical Mechanics and its Applications, 492, pp.639-650. Feng, S., Ke, R., Wang, X., Zhang, Y. and Li, L., 2017. Traffic flow data compression considering burst components. IET Intelligent Transport Systems, 11(9), pp.572-580. Liu, L., Feng, S.*, Feng, Y., Zhu, X. and Liu, H.X., 2021. A learning-based stochastic driving model for autonomous vehicle testing. Transportation Research Record. DOI: 10.1177/03611981211035756. Sun, H., Feng, S.*, Yan, X. and Liu, H.X., 2021. Corner case generation and analysis for safety assessment of autonomous vehicles. Transportation Research Record. DOI: 10.1177/03611981211018697.