Kaidi Yang

Kaidi Yang


Kaidi Yang is a Postdoctoral Scholar with the Autonomous Systems Lab in the Department of Aeronautics and Astronautics at Stanford University. He obtained the Ph.D. degree in Civil Engineering from ETH Zurich in 2019. Prior to that, he received a BEng. in Automation, a BSc. in Pure and Applied Mathematics (minor), and an MSc. in Control Science and Engineering from Tsinghua University in China.

His primary research interest lies in the control and optimization of multimodal transportation systems, with a particular focus on emerging technologies and shared mobility. Currently, he is working on various topics on Mobility-on-Demand systems, e.g., the coordination of a mixed fleet of autonomous and human-driven vehicles, integration of autonomous mobility-on-demand and public transport, etc.

In his free time, Kaidi enjoys traveling, hiking, cooking, and tennis.

Awards:

  • Best Paper Award in Omega: The International Journal of Management Science, 2019
  • Postdoc.Mobility fellowship funded by the Swiss National Science Foundation

ASL Publications

  1. K. Yang, M. Tsao, X. Xu, and M. Pavone, “Real-Time Control of Mixed Fleets in Mobility-on-Demand Systems,” in Proc. IEEE Int. Conf. on Intelligent Transportation Systems, Indianapolis, IN, USA, 2021. (In Press)

    Abstract: Automated vehicles (AVs) are expected to be beneficial for Mobility-on-Demand (MoD), thanks to their ability of being globally coordinated. To facilitate the steady transition towards full autonomy, we consider the transition period of AV deployment, whereby an MoD system operates a mixed fleet of automated vehicles (AVs) and human-driven vehicles (HVs). In such systems, AVs are centrally coordinated by the operator, and the HVs might strategically respond to the coordination of AVs. We devise computationally tractable strategies to coordinate mixed fleets in MoD systems. Specifically, we model an MoD system with a mixed fleet using a Stackelberg framework where the MoD operator serves as the leader and human-driven vehicles serve as the followers. We develop two models: 1) a steady-state model to analyze the properties of the problem and determine the planning variables (e.g., compensations, prices, and the fleet size of AVs), and 2) a time-varying model to design a real-time coordination algorithm for AVs. The proposed models are validated using a case study inspired by real operational data of a MoD service in Singapore. Results show that the proposed algorithms can significantly improve system performance.

    @inproceedings{YangTsaoEtAl2021,
      author = {Yang, K. and Tsao, M. and Xu, X. and Pavone, M.},
      title = {Real-Time Control of Mixed Fleets in Mobility-on-Demand Systems},
      booktitle = {{Proc. IEEE Int. Conf. on Intelligent Transportation Systems}},
      year = {2021},
      note = {{Extended Version, Available} at \url{https://arxiv.org/abs/2008.08131}},
      address = {Indianapolis, IN, USA},
      month = sep,
      url = {https://arxiv.org/pdf/2008.08131},
      keywords = {in press},
      owner = {ykd07},
      timestamp = {2021-06-15}
    }
    
  2. D. Gammelli, K. Yang, J. Harrison, F. Rodrigues, F. C. Pereira, and M. Pavone, “Graph Neural Network Reinforcement Learning for Autonomous Mobility-on-Demand Systems,” 2021. (Submitted)

    Abstract: Autonomous mobility-on-demand (AMoD) systems represent a rapidly developing mode of transportation wherein travel requests are dynamically handled by a coordinated fleet of robotic, self-driving vehicles. Given a graph representation of the transportation network - one where, for example, nodes represent areas of the city, and edges the connectivity between them - we argue that the AMoD control problem is naturally cast as a node-wise decision-making problem. In this paper, we propose a deep reinforcement learning framework to control the rebalancing of AMoD systems through graph neural networks. Crucially, we demonstrate that graph neural networks enable reinforcement learning agents to recover behavior policies that are significantly more transferable, generalizable, and scalable than policies learned through other approaches. Empirically, we show how the learned policies exhibit promising zero-shot transfer capabilities when faced with critical portability tasks such as inter-city generalization, service area expansion, and adaptation to potentially complex urban topologies.

    @inproceedings{GammelliYangEtAl2021,
      author = {Gammelli, D. and Yang, K. and Harrison, J. and Rodrigues, F. and Pereira, F. C. and Pavone, M.},
      title = {Graph Neural Network Reinforcement Learning for Autonomous Mobility-on-Demand Systems},
      year = {2021},
      note = {Submitted},
      url = {https://arxiv.org/abs/2104.11434},
      keywords = {sub},
      owner = {jh2},
      timestamp = {2021-03-23}
    }
    
  3. M. Tsao, K. Yang, S. Zoepf, and M. Pavone, “Trust but Verify: Cryptographic Data Privacy for Mobility Management,” 2021. (Submitted)

    Abstract: The era of Big Data has brought with it a richer understanding of user behavior through massive data sets, which can help organizations optimize the quality of their services. In the context of transportation research, mobility data can provide Municipal Authorities (MA) with insights on how to operate, regulate, or improve the transportation network. Mobility data, however, may contain sensitive information about end users and trade secrets of Mobility Providers (MP). Due to this data privacy concern, MPs may be reluctant to contribute their datasets to MA. Using ideas from cryptography, we propose an interactive protocol between a MA and a MP in which MA obtains insights from mobility data without MP having to reveal its trade secrets or sensitive data of its users. This is accomplished in two steps: a commitment step, and a computation step. In the first step, Merkle commitments and aggregated traffic measurements are used to generate a cryptographic commitment. In the second step, MP extracts insights from the data and sends them to MA. Using the commitment and zero-knowledge proofs, MA can certify that the information received from MP is accurate, without needing to directly inspect the mobility data. We also present a differentially private version of the protocol that is suitable for the large query regime. The protocol is verifiable for both MA and MP in the sense that dishonesty from one party can be detected by the other. The protocol can be readily extended to the more general setting with multiple MPs via secure multi-party computation.

    @article{TsaoYangZoepfPavone2021,
      author = {Tsao, M. and Yang, K. and Zoepf, S. and Pavone, M.},
      title = {Trust but Verify: Cryptographic Data Privacy for Mobility Management},
      year = {2021},
      note = {Submitted},
      keywords = {sub},
      url = {https://arxiv.org/abs/2104.07768}
    }