教育经历：

2010.09 – 2014.07   同济大学 交通工程   工学学士

2014.09 – 2017.07   同济大学 交通运输工程   工学硕士

2018.11 – 2019.12   加州大学伯克利分校   联合培养博士

2017.09 – 2020.08   同济大学 交通运输工程   工学博士

工作经历：

2020.10 – 至今   同济大学交通运输工程学院   博士后（合作导师：何积丰 院士）

2021.05 – 至今   同济大学交通科学与技术研究院   副研究员

代表性学术论文：

• 国际期刊论文

1. Du, Y., Shi, Y., Zhao, C.*, Du, Z., & Ji, Y. (2022). A lifelong framework for data quality monitoring of roadside sensors in cooperative vehicle-infrastructure systems. Computers and Electrical Engineering, 100, 108030. DOI: 10.1016/j.compeleceng.2022.108030

2. Zhao, C., Zhu, Y., Du, Y.*, Liao, F., & Chan, C. Y. (2022). A Novel Direct Trajectory Planning Approach Based on Generative Adversarial Networks and Rapidly-Exploring Random Tree. IEEE Transactions on Intelligent Transportation Systems. DOI: 10.1109/TITS.2022.3164391

3. Zhang, X., Zhao, C.*, Liao, F., Li, X., & Du, Y. (2022). Online parking assignment in an environment of partially connected vehicles: A multi-agent deep reinforcement learning approach. Transportation Research Part C: Emerging Technologies, 138, 103624. DOI: doi.org/10.1016/j.trc.2022.103624

4. Du, Y., Wang, F., Zhao, C.*, Zhu, Y., & Ji, Y. (2022). Quantifying the performance and optimizing the placement of roadside sensors for cooperative vehicle‐infrastructure systems. IET Intelligent Transport Systems. DOI: doi.org/10.1049/itr2.12185‍

5. Zhao, C., Cao, J.*, Zhang, X., & Du, Y. (2022). From Search-for-Parking to Dispatch-for-Parking in an Era of Connected and Automated Vehicles: A Macroscopic Approach. Journal of Transportation Engineering, Part A: Systems, 148(2): 04021112. DOI: doi.org/10.1061/JTEPBS.0000640

6. Jiang, S., Zhao, C.*, Zhu, Y., Wang, C., & Du, Y. (2022). A Practical and Economical Ultra-wideband Base Station Placement Approach for Indoor Autonomous Driving Systems. Journal of Advanced Transportation, 2022. DOI: doi.org/10.1155/2022/3815306

7. Du, Y., Chen, J., Zhao, C.*, Liu, C., Liao, F., & Chan, C. (2022). Comfortable and Energy-efficient Speed Control of Autonomous Vehicles on Rough Pavements using Deep Reinforcement Learning. Transportation Research Part C: Emerging Technologies, 134, 103489. DOI: doi.org/10.1016/j.trc.2021.103489

8. Zhao, C., Liao, F., Li, X., & Du, Y. (2021). Macroscopic Modeling and Dynamic Control of On-Street Cruising-for-Parking of Autonomous Vehicles in a Multi-Region Urban Road Network. Transportation Research Part C: Emerging Technologies, 128, 103176.DOI: doi.org/10.1016/j.trc.2021.103176

9. Liu, C., Du, Y., Ge, Y., Wu, D., Zhao, C., & Li, Y. (2021). New Generation of Smart Highway: Framework and Insights. Journal of Advanced Transportation, 2021. DOI: doi.org/10.1155/2021/9445070

10. Du, Y., Qin, B., Zhao, C.*, Zhu, Y., Cao, J., & Ji, Y. (2021). A Novel Spatio-Temporal Synchronization Method of Roadside Asynchronous MMW Radar-Camera for Sensor Fusion. IEEE Transactions on Intelligent Transportation Systems. DOI: 10.1109/TITS.2021.3119079

11. Zhao, C., Li, S., Wang, W., Li, X., & Du, Y. (2018). Advanced Parking Space Management Strategy Design: An Agent-based Simulation Optimization Approach. Transportation Research Record, 2672(8), 901-910. DOI: doi.org/10.1177/0361198118758671

12. Du, Y., Zhao, C.*, Li, F., & Yang, X. (2017). An Open Data Platform for Traffic Parameters Measurement via Multirotor Unmanned Aerial Vehicles Video. Journal of Advanced Transportation, 2017. DOI: doi.org/10.1155/2017/8324301

13. Du, Y., Zhao, C., Zhang, X., & Sun, L. (2015). Microscopic Simulation Evaluation Method on Access Traffic Operation. Simulation Modelling Practice and Theory, 53, 139-148. DOI: doi.org/10.1016/j.simpat.2015.02.004

• 国内期刊论文

1. 杜豫川, 刘成龙, 吴荻非, & 赵聪. (2021). 新一代智慧高速公路系统架构设计. 中国公路学报, 35(4), 203-214. DOI: 10.19721/j.cnki.1001-7372.2022.04.017

2. 杜豫川, 师钰鹏, 都州扬, 赵聪*, & 暨育雄. (2022). 智能网联环境下路侧感知单元数据质量在线监测方法框架. 中国公路学报, 35(3), 273-285. DOI: 10.19721/j.cnki.1001-7372.2022.03.023

3. 赵聪, 张昕源, 李兴华, & 杜豫川. (2021). 基于多智能体深度强化学习的停车系统智能延时匹配方法. 中国公路学报, 1-18.   http://kns.cnki.net/kcms/detail/61.1313.U.20210623.1514.010.html.

4. 杜豫川, 都州扬, 师钰鹏, 赵聪*, & 暨育雄. (2021). 路侧感知车辆轨迹数据质量智能评估方法. 中国公路学报, 34(7), 164-176. DOI: 10.19721/j.cnki.1001-7372.2021.07.013

5. 刘成龙, 陶莎, 赵聪, 暨育雄, & 杜豫川. (2021). 高速路网不停车收费车道优化布设方法. 中国公路学报, 1-18. http://kns.cnki.net/kcms/detail/61.1313.U.20210308.1125.004.html.

6. 赵聪, 朱逸凡, 李兴华, & 杜豫川. (2020). 动态管理模式下路侧停车泊位占有率预测方法. 交通运输系统工程与信息, 20(5), 107-113. http://www.tseit.org.cn/CN/Y2020/V20/I5/107

• 国际会议论文

1. Chen, J., Zhao, C.*, & Du, Y. (2022). Comfortable Speed Planning and Intelligent Suspension Control with Knowledge Transfer on Rough Pavements for Autonomous Vehicles. In 101th Transportation Research Board Annual Meeting (No. 22-04762). Transportation Research Board.

2. Ni, L., Zhao, C.*, Ji, Y., & Du, Y. (2022). Quantification and Analysis of Driving Risk based on Modified Safety Potential Field. In 101th Transportation Research Board Annual Meeting (No. 22-04232). Transportation Research Board.

3. Chen, J., Zhao, C.*, & Du, Y. (2021). Comfortable and Energy Efficient Velocity Control on Rough Pavements using Deep Reinforcement Learning. In 100th Transportation Research Board Annual Meeting (No. 21-03858). Transportation Research Board.

4. Liu, C., Wu, D., Zhao, C., Du, Y., & Li, Y. (2021). Concept and Framework of the New Generation of Smart Highway. In 100th Transportation Research Board Annual Meeting (No. 21-01257). Transportation Research Board.

5. Zhao, C., Chen, I. M., Li, X., & Du, Y. (2019). Urban parking system based on dynamic resource allocation in an era of connected and automated vehicles. In 2019 IEEE Intelligent Transportation Systems Conference (ITSC) (pp. 3094-3099). IEEE.

6. Chen, I. M., Zhao, C., & Chan, C. Y. (2019). A Deep Reinforcement Learning-Based Approach to Intelligent Powertrain Control for Automated Vehicles. In 2019 IEEE Intelligent Transportation Systems Conference (ITSC) (pp. 2620-2625). IEEE.

7. Du, Y., Zhao, C.*, Zhang, H., Wong, S. C., & Liao, F. (2017). Modeling park-and-ride services in a multi-commodity discrete-continuum transport system with elastic demand. In 96th Transportation Research Board Annual Meeting (No. 17-02785). Transportation Research Board.

应用成果：

• 中国首个基础设施支持的智能代客泊车系统

面向城市“停车难”痛点问题，综合利用人工智能、云计算、5G、数字孪生等技术，设计并开发了“车-路-云”一体化的智能代客泊车系统，攻克了“最后一公里”车辆高精度定位、复杂环境决策控制以及城市停车资源重构、管理优化等关键技术难题；为推进智能代客泊车在城市全面推广应用，提出了与智能网联汽车相匹配的智慧交通基础设施建设方案，构建了以停车全息场景库为基础，集成模拟仿真、车辆硬件在环和实体测试场于一体的完整自动泊车技术研发与测试工具链。

• 智能车路协同系统精准感知与管控决策支持

面向新一代智能网联交通系统，基于智慧公路路侧感知单元多源传感器数据（高清视频、毫米波雷达、激光雷达等），综合利用大数据、云边计算、人工智能和数字孪生等技术，在多目标识别与追踪、轨迹预测与修补、全域交通运行重构、平行仿真推演与管控决策支持等方面取得创新性成果，实现了全时空交通流智能主动管控、交通运行风险分析与预警、自动驾驶车路协同超视距感知与定位增强等功能，有效提升了道路交通运输效率与安全。依托上海国际航运中心洋山深水港，建成路侧交通全息感知、车路协同通信网络及平行虚拟系统应用示范，支持洋山港-东海大桥-深水港物流园区自动驾驶集卡车路协同系统的规模化建设及应用。