Xincao Xu (Chinese character 许新操) received the Ph.D. degree in Computer Science from the College of Computer Science at the Chongqing University (CQU), Chongqing, China, in 2023 and the B.S. degree in Network Engineering from the School of Computer and Control Engineering at the North University of China (NUC), Taiyuan, China, in 2017. Dr. Xu is currently a Postdoctoral Research Fellow in cooperation with Prof. Shaohua Wan at the Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China (UESTC), Shenzhen, China. He has authored and co-authored more than 10 papers with total google scholar .

His research interests include:

  • AI for Driving: Heterogeneous information fusion, Cooperative sensing, Multimodal deep learning, Driving behavior control, etc.
  • Vehicular Networks: Coopertive transmission and computing, Integrated sensing and communications, Resource allocation, etc.
  • Vehicular Cyber-Physical Systems: Sensing, Transmitting, Modeling, Controlling.
  • Edge Computing: Offloading the communication, computing, and caching abilities from the cloud to the network edge.
  • Deep Reinforcement Learning: Multi-agent DRL, Hierarchical DRL, DRL + Other technologies, e.g., Game theory, Evaluation algorithm, etc.
  • Game Theory: Potential game, Matching, Auction, etc.
Office Address:
706, Building 3, YESUN Intelligent Community II, Guanlan Street, Longhua District, Shenzhen 518110, China.
[email protected]
[email protected]

🔥 News

  • 2023.11: A paper has been accepted by IEEE Transactions on Intelligent Transportation Systsms (T-ITS)! 🎉🎉
  • 2023.11: A general project has received approval from the China Postdoctoral Science Foundation! 🎉🎉
  • 2023.10: A paper has been accepted by the 2023 IEEE International Symposium on Product Compliance Engineering-Asia (ISPCE-AS)!
  • 2023.09: Delighted to share that one of our papers has been accepted by the IEEE Transactions on Consumer Electronics (TCE)! 🎉🎉
  • 2022.10: A journal paper has been approved for publication in the Journal of Systems Architecture (JSA)!
  • 2022.10: The citations on my Google Scholar have reached a milestone of 100!
  • 2022.09: An invention patent has been officially approved by the China National Intellectual Property Administration.
  • 2022.06: A conference paper has been accepted for the 2022 IEEE International Conference on Intelligent Transportation Systems (ITSC)!

🕒 Research


Cooperative Sensing and Uploading for Quality-Cost Tradeoff of Digital Twins in VEC
Kai Liu*, Xincao Xu*, Penglin Dai, and Biwen Chen

  • We formulate a bi-objective problem for enabling Digital Twins in Vehicular Edge Computing (DT-VEC), where a cooperative sensing model and a V2I uploading model are derived, and novel metrics for quantitatively evaluating system quality and cost are designed.
  • We propose a multi-agent multi-objective (MAMO) deep reinforcement learning model, which determines the sensing objects, sensing frequency, uploading priority, and transmission power of vehicles, as well as the V2I bandwidth allocation of edge nodes. The model includes distributed actors interacting with the environment and storing their interaction experiences in the replay buffer, a learner with a dueling critic network for evaluating actions of vehicles and edge nodes.
  • We give comprehensive performance evaluation by implementing three representative algorithms, including random allocation (RA), distributed distributional deep deterministic policy gradient (D4PG) and multi-agent D4PG (MAD4PG), and the simulation results demonstrate that the proposed MAMO significantly outperforms existing solutions under different scenarios with respect to both maximizing system quality and saving system cost.
  • Accepted by IEEE Transactions on Consumer Electronics [JCR Q2|SCI Q2]
System Model
System Model

Cooperative Sensing and Heterogeneous Information Fusion in VCPS: A Multi-agent Deep Reinforcement Learning Approach
Xincao Xu, Kai Liu*, Penglin Dai, Ruitao Xie, and Jiangtao Luo

  • We present a cooperative sensing and heterogeneous information fusion architecture in VCPS via vehicular edge computing. The heterogeneous information can be sensed via either onboard sensors such as LIDAR, GPS, and cameras, or roadside infrastructures such as traffic lights. The sensed information is queued in vehicles for uploading via the V2I bandwidth, which is allocated by the corresponding edge node. Logical views can be constructed via the information fusion at edge nodes, and different views may be required to enable upper-layer applications.
  • We formulate the problem to maximize the quality of VCPS. Specifically, we derive a cooperative sensing model, in which the information queuing and data uploading are modeled based on the multi-class M/G/1 priority queue and the Shannon theory, respectively. Then, we derive a heterogeneous information fusion model by modeling the timeliness, completeness, and consistency of views. On this basis, a new metric called Age of View (AoV) is defined to quantitatively measure the quality of information fusion. Finally, we model the quality of VCPS and present the optimization objective, which is to maximize the VCPS quality.
  • We propose a multi-agent deep reinforcement learning solution. Specifically, the solution model is presented, in which vehicles act as independent agents with action space of determining the sensing frequencies and uploading priorities, and the edge action space is the V2I bandwidth allocation. The system state consists of vehicle sensed information, edge cached information, and view requirements. The system reward is defined as the achieved VCPS quality. In particular, the system reward is divided into the Difference Reward (DR) to capture vehicle individual contributions on view constructions by the DR-based credit assignment.
  • Accepted by IEEE Transactions on Intelligent Transportation Systems [JCR Q1|SCI Q1]
NOMA-based VEC
NOMA-based VEC

Joint Task Offloading and Resource Optimization in NOMA-based Vehicular Edge Computing: A Game-Theoretic DRL Approach
Xincao Xu, Kai Liu*, Penglin Dai, Feiyu Jin, Hualing Ren, Choujun Zhan, and Songtao Guo

  • We present a NOMA-based VEC architecture, where the vehicles share the same frequency of bandwidth resources and communicate with the edge node with the allocated transmission power. The tasks arrive stochastically at vehicles and are heterogeneous regarding computation resource requirements and deadlines, which are uploaded by vehicles via V2I communications. Then, the edge nodes with heterogeneous computation capabilities, i.e., CPU clock speed, can either execute the tasks locally with allocated computation resources or offload the tasks to neighboring edge nodes through a wired network.
  • We propose a cooperative resources optimization (CRO) problem by jointly offloading tasks and allocating communication and computation resources to maximize the service ratio, which is the number of tasks serviced before the deadlines divided by the number of requested tasks. Specifically, a V2I transmission model considering both intra-edge and inter-edge interference and a task offloading model considering the heterogeneous resources and cooperation of edge nodes are theoretically modeled, respectively.
  • We decompose the CRO problem into two subproblems: 1) task offloading game model and 2) resource allocation convex problem. Specifically, we model the first subproblem as a non-cooperative game among edge nodes, which is further proved as an EPG with the existence and convergence of NE. Then, we design a MAD4PG algorithm, a multi-agent version of D4PG, to achieve the NE, where edge nodes act as independent agents to determine the task offloading decisions and receive the achieved potential as rewards. Further, we model the second subproblem as two independent convex problems and derive an optimal mathematical solution based on the gradient-based iterative method and KKT condition.
  • Accepted by Journal of Systems Architecture [JCR Q1|SCI Q2|CCF B]

💻 Publication

JCR: Journal Citation Reports by Clarivate Com.
SCI: Journal Partition List by National Science Library, Chinese Academy of Sciences
CCF: Recommended Publications by China Computer Federation
*: Corresponding Author




Chinese Papers

  • Xincao Xu, Kai Liu*, Chunhui Liu, Hao Jiang, Songtao Guo, and Weiwei Wu, Potential Game Based Channel Allocation for Vehicular Edge Computing, Tien Tzu Hsueh Pao/Acta Electronica Sinica (电子学报), volume 49, issue 5, pp.851-860, July 2021. [CCF T1]
  • Xincao Xu, Yi Zhou, Kai Liu, Chaocen Xiang, Yantao Li, and Songtao Guo, Potenial Game based Distributed Channel Allocation in Vehicular Fog Computing Environments, 14th China Conference on Internet of Things (CWSN’20), Dunhuang, China, September 18-21, 2020. (Best Paper Candidate)


  • Xincao Xu, Research on Key Techniques for Modeling and Optimization of Vehicular Cyber-Physical Systems, Chongqing University, Doctoral Dissertation, June 2023.

✨ Others

🚧 Grants

Principal Investigator

  • General Project of China Postdoctoral Science Foundation, Modeling and Optimization of Vehicular Cyber-Physical System for Vehicular Edge Computing, 2023M740515, 2023/11-2025/06.


  • General Project of National Natural Science Foundation of China, Computing Model Deployment and Collaborative Cross Domain Optimization for Edge Intelligence of Internet of Vehicles, 62172064, 2022/01–2025/12.
  • General Project of National Natural Science Foundation of China, Research on High Performance Data Transmission and Reception Mechanism of Wireless Energy-capable Communication Network Based on Successive Interference Cancellation, 62072064, 2021/01-2024/12.
  • General Project of National Natural Science Foundation of China, Research on Continuous Authentication Method and Key Technologies for Mobile Users Based on Behavioral Characteristics, 62072061, 2021/01-2024/12.
  • General Project of National Natural Science Foundation of China, Research on Intelligent Multitask High-Performance Optimization Algorithms Based on Transfer Learning, 61876025, 2019/01–2022/12.
  • General Project of National Natural Science Foundation of China, Research on Architecture and Protocols for Large-scale Data Services in Converged Heterogeneous Internet of Vehicles, 61872049, 2019/01–2022/12.

📄 Patents

  • Xincao Xu, Kai Liu, Dong Li, A Control Plane View Construction Method for Software-Defined Vehicular Networks, Chinese Invention Patent (ZL202110591822.1), 2022.
  • Liu Kai, Zhang Lang, Xincao Xu, Ren Hualing, Zhou Yi, An Edge Computing Based Collision Warning Method for Vehicles in Blind Areas, Chinese Invention Patent (ZL201910418745.2), 2021.
  • Ren Hualing, Liu Kai, Chen Mengliang, Zhou Yi, Xincao Xu, A Fog Computing-based Information Acquisition, Computing, and Transmission Architecture, Chinese Invention Patent (ZL201910146357.3), 2021.

📺 Demos

  • Non-Light-of-Sight Collision Warning System

👨🏻‍💻 Experience

💼 Work

  • 2023.07 - 2025.06(now), Postdoctoral Research Fellow, Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, Shenzhen, China. (In cooperation with Prof. Shaohua Wan)

🎓 Education

  • 2019.09 - 2023.06, Ph.D., Computer Science, College of Computer Science, Chongqing University, Chongqing, China. (Supervised by Prof. Kai Liu)
  • 2017.09 - 2019.06, Postgraduate student (Join the successive postgraduate-doctor program in 2019), Computer Science, College of Computer Science, Chongqing University, Chongqing, China.
  • 2013.09 - 2017.06, Bachelor, Network Engineering, College of Computer and Control Engineering, North Univeristy of China, Taiyuan, China.

😎 Membership

  • 2023.09 - now, Member #G0818M, China Computer Federation (CCF)
  • 2023.10 - now, Member #3120145, Association for Computing Machinery (ACM)
  • 2023.09 - now, Member #99619216, Institute of Electrical and Electronics Engineers (IEEE)
  • 2020.11 - 2023.08, Student Member #G0818G, China Computer Federation (CCF)

👀 Reviewer


  • IEEE Transactions on Vehicular Technology (TVT)
  • Journal of Systems Architecture (JSA)
  • Neural Computing and Applications (NCAA)
  • The Journal of Supercomputing (TJSC)
  • Journal of Computer Research and Development
  • Tien Tzu Hsueh Pao/Acta Electronica Sinica


  • IEEE Vehicular Technology Conference (VTC-Fall’23)
  • International conference on Artificial Intelligence of Things and Systems (AIoTSys’23)
  • IEEE International Conference on Intelligent Transportation Systems (ITSC’22,23)
  • IEEE Global Communications Conference (GLOBECOM’21,23)
  • International Conference on Algorithms and Architectures for Parallel Processing (ICA3PP’21)
  • China Conference on Internet of Things (Wireless Sensor Network) (CWSN’21)
  • IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom’20)
  • IEEE International Symposium on Product Compliance Engineering-Asia (ISPCE-CN’20)

🎒 Learning

Shareable Materials

  • Conference Proceedings and Journals Related to Internet of Vehicles [PDF]
  • Handbook for Research Beginner 01: Research Tools and Paper Reading (Chinese) [PDF]
  • Handbook for Research Beginner 02: Paper Architecture and Writting (Chinese) [PDF]
  • Handbook for Research Beginner 03: Patent Drafting (Chinese) [PDF]



  • Meachine Learning by Prof. Zhi-Hua Zhou [Chinese]
  • Deep Learning by Dr. Ian Goodfellow, Prof. Yoshua Bengio, and Prof. Aaron Courville [English] [Chinese]
  • Machine Learning Yearning by Prof. Andrew Ng [Chinese]
  • Dive into Deep Learning by Dr. Aston Zhang, Dr. Zachary C. Lipton, Dr. Mu Li, and Dr. Alexander I. Smola [English] [Chinese]


  • Google Scholar: Largest Search Engine for Academic Publishing [Link]
  • dblp: Computer Science Bibliography [Link]
  • Conference Partner: Information about Academic Conferences [Link]
  • LetPub: Information about SCI Journals [Link]
  • Academic Accelerator: Wonderful Tool to Accelerate Your Scientific Research [Link]
  • WikiCFP: A Wiki for Calls for Papers [Link]
  • Zotero: Personal Research Assistant [Link]
  • IEEE Template Selector: Find the Right IEEE Article Template for Your Target Publication [Link]


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