Event
Future Leaders in Robotics and AI Seminar: Laura Zheng
Friday, April 18, 2025
2:30 p.m.-3:00 p.m.
https://go.umd.edu/FutureLeaders
Yancy Diaz-Mercado
301 405 6506
yancy@umd.edu
Who is Driving the Car? Modeling Driver Personas for Accurate Behavior Simulation in Autonomous Driving
Laura Zheng
PhD Student
University of Maryland
Abstract
Human factors such as driving style are rarely explicitly modeled in traffic behavior simulation, yet they play an important role in describing the multi-modal decisions in trajectory forecasting. The primary reason for the lack of explicit modeling is due to the abstract notion of driving style, whose observations have yet to be explored with respect to trajectories. In this talk, I will present two novel approaches in quantification of observable driving style. The first approach is human-driven; we begin with querying humans directly, assessing both their self-reported driving style in addition to their evaluated driving style based on the Multi-Dimensional Driving Style Inventory, then correlating their assessed driving style with their driving behavior captured in an immersive virtual driving simulator, allowing for driving style classification of driving trajectories. The second approach is data-driven, where we explore driving style as a time-consistent latent variable that is independent of both scenario context and route intent. We model driving style using a library of LoRA modules, each of which finetunes the trajectory decoder to accommodate a particular driving style, resulting in diverse and controllable predictions of forecast trajectories with respect to individual driving style. These techniques make it easier for self-driving cars to navigate in a mixed autonomy of human-driven and automatically controlled vehicles in traffic.
Biography
Laura Yu Zheng is a 5th-year Ph.D. student in Computer Science at the University of Maryland, College Park, where she is supervised by Professor Ming Lin at the GAMMA Group. Her research focuses on creating safer, more transparent, and generalizable autonomous driving systems by integrating simulation models with deep learning. Previously, she has interned with Waymo, Kitware, and NASA, working on projects involving both macroscopic and microscopic traffic mobility simulations, as well as document connectivity analysis. In the Fall of 2024, she received the Maryland Transportation Institute Fellowship, which has supported both projects presented in this talk.
