Yunpeng(Larry) Liu

I'm a fourth-year PhD student at University of British Columbia, where I'm advised by Frank Wood. I'm also an Mitacs intern at Invereted AI, where we are working on advancing autonomous driving with generative models. Previously, I completed my Bachelor of Applied Science in Electrical and Computer Engineering at the University of British Columbia.

My research focuses on autonomous driving with generative models like diffusion models, modelling realistic mult-agent behaviors and reasoning about the world.

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Refereed Publications

Diffusion models based closed-loop autoregressive multi-agent traffic scene simulation.

Improving online continual learning performance by optimizing the experience replay loss in a way that stabilizes neural network representations between parameter updates.

A Diffusion model based method of generating traffic scenarios (DJINN). we demonstrate how DJINN flexibly enables direct test-time sampling from a variety of valuable conditional distributions including goal-based sampling, behavior-class sampling, and scenario editing.

Existing multi-agent behavioral models rely on manually annotated HD maps which is expensive and time-consuming. We propose an aerial image-based map representation that requires minimal annotation and provides rich road context information for traffic agents like pedestrians and vehicles.