Ruolin Li

Contacts:

Email: ruolinli at stanford dot edu

Ruolin Li

Ruolin will join USC as a Gabilan Assistant Professor in the Department of Civil and Environmental Engineering in Fall 2024. She is currently a postdoctoral scholar in ASL at Stanford. She obtained a Ph.D. and a M.S. degree in Mechanical Engineering from UC Berkeley in 2023 and 2018. She is broadly interested in the design and control of future mobility systems, particularly involving automated agents such as autonomous vehicles. Her research lies at the intersection of human behavior modeling, modeling of multi-agent systems, and control and optimization, aiming to enhance the societal benefits of the intelligent transportation systems. She was selected as a Rising Star in Civil and Environmental Engineering by MIT in 2021 and a Rising Star in Mechanical Engineering by Stanford in 2022.


ASL Publications

  1. R. Dyro, M. Foutter, R. Li, L. Di Lillo, E. Schmerling, X. Zhou, and M. Pavone, “Realistic Extreme Behavior Generation for Improved AV Testing,” in Proc. IEEE Conf. on Robotics and Automation, 2025. (Submitted)

    [BibTeX] [Abstract]

    Abstract: This work introduces a framework to diagnose the strengths and shortcomings of Autonomous Vehicle (AV) collision avoidance technology with synthetic yet realistic potential collision scenarios adapted from real-world, collision-free data. Our framework generates counterfactual collisions with diverse crash properties, e.g., crash angle and velocity, between an adversary and a target vehicle by adding perturbations to the adversary’s predicted trajectory from a learned AV behavior model. Our main contribution is to ground these adversarial perturbations in realistic behavior as defined through the lens of data-alignment in the behavior model’s parameter space. Then, we cluster these synthetic counterfactuals to identify plausible and representative collision scenarios to form the basis of a test suite for downstream AV system evaluation. We demonstrate our framework using two state-of-the-art behavior prediction models as sources of realistic adversarial perturbations, and show that our scenario clustering evokes interpretable failure modes from a baseline AV policy under evaluation.

     
    @inproceedings{DyroFoutterEtAl2024,
  author = {Dyro, R. and Foutter, M. and Li, R. and Di Lillo, L. and Schmerling, E. and Zhou, X. and Pavone, M.},
  title = {Realistic Extreme Behavior Generation for Improved {AV} Testing},
  booktitle = {{Proc. IEEE Conf. on Robotics and Automation}},
  year = {2025},
  note = {Submitted},
  keywords = {sub},
  owner = {jthluke},
  timestamp = {2024-10-30},
  url = {/wp-content/papercite-data/pdf/Dyro.Foutter.Li.ea.ICRA2025.pdf}
}