DuckAI 2025 Fall

Ensuring the security and safety of autonomous driving systems is a critical challenge due to the inherent safety risks. Building safe and human-aligned autonomous driving systems in real-world scenarios remains unresolved due to the complex and varied nature of driving behaviors and semantics. In this talk, I will present recent research efforts aimed at rethinking the testing of AV systems. The discussion will focus on two key areas: 1) modeling diverse behaviors using advanced traffic models, and 2) leveraging foundation models to enhance simulation capabilities and accelerate the testing process.

Short Bio: Yulong Cao is a research scientist at the NVIDIA Autonomous Vehicle Research group directed by Marco Pavone. His research interest is in the intersection of security, privacy, and machine learning. His research goal is to build trustworthy machine learning systems that are aligned with human in the real world, with a focus on autonomous driving systems. He completed his Ph.D from the Computer Science and Engineering department of University of Michigan, advised by Morley Mao. and holds B.S. degrees from University of Michigan and Shanghai Jiao Tong University.