Marco Pavone

Marco Pavone

NVIDIA Research

Stanford University

Marco Pavone is Director of Autonomous Vehicle Research at NVIDIA. His main research interests are in the development of methodologies for the analysis, design, and control of autonomous systems, with an emphasis on self-driving cars, autonomous aerospace vehicles, and future mobility systems.

He is currently on partial leave from Stanford University, where he is an Associate Professor of Aeronautics and Astronautics. At Stanford, he is also the Director of the Autonomous Systems Laboratory and Co-Director of the Center for Automotive Research at Stanford. He received a Ph.D. degree in Aeronautics and Astronautics from the Massachusetts Institute of Technology in 2010.

He is a recipient of a number of awards, including a Presidential Early Career Award for Scientists and Engineers from President Barack Obama, an Office of Naval Research Young Investigator Award, a National Science Foundation Early Career (CAREER) Award, a NASA Early Career Faculty Award, and an Early-Career Spotlight Award from the Robotics Science and Systems Foundation. He was identified by the American Society for Engineering Education (ASEE) as one of America’s 20 most highly promising investigators under the age of 40. He is currently serving as an Associate Editor for the IEEE Control Systems Magazine.

23/">Tree-structured Policy Planning with Learned Behavior Models
  • Robust and Controllable Object-Centric Learning through Energy-based Models
  • Robust Trajectory Prediction against Adversarial Attacks
  • DiffStack: A Differentiable and Modular Control Stack for Autonomous Vehicles
  • Task-Relevant Failure Detection for Trajectory Predictors in Autonomous Vehicles
  • AdvDO: Realistic Adversarial Attacks for Trajectory Prediction
  • Towards Data-Driven Synthesis of Autonomous Vehicle Safety Concepts
  • Propagating State Uncertainty Through Trajectory Forecasting
  • ScePT: Scene-consistent, Policy-based Trajectory Predictions for Planning
  • Whose Track Is It Anyway? Improving Robustness to Tracking Errors with Affinity-Based Prediction
  • Interaction-Dynamics-Aware Perception Zones for Obstacle Detection Safety Evaluation
  • MTP: Multi-Hypothesis Tracking and Prediction for Reduced Error Propagation
  • Injecting Planning-Awareness into Prediction and Detection Evaluation
  • Rethinking Trajectory Forecasting Evaluation