3 citations · 3 across the 6 of their papers we have counts for
6 papers
Traffic Scenario Orchestration from Language via Constraint Satisfaction
Frieda Rong, Chris Zhang, Kelvin Wong +1
Autonomous vehicles (AVs) require extensive testing in simulation, but test case generation for driving scenarios is laborious. The desired scenarios are often out-of-distribution…
Conditional Flow-VAE for Safety-Critical Traffic Scenario Generation
Zimu Gong, Brian Zhaoning Zhang, Chris Zhang +2
Safety-critical scenarios are essential for the development of autonomous vehicles (AVs) but are rare in real-world driving data. While simulation offers a way to generate such sce…
Efficient Equivariant Transformer for Self-Driving Agent Modeling
Scott Xu, Dian Chen, Kelvin Wong +3
Accurately modeling agent behaviors is an important task in self-driving. It is also a task with many symmetries, such as equivariance to the order of agents and objects in the sce…
Learning to Drive via Asymmetric Self-Play
Chris Zhang, Sourav Biswas, Kelvin Wong +5
Large-scale data is crucial for learning realistic and capable driving policies. However, it can be impractical to rely on scaling datasets with real data alone. The majority of dr…
Unraveling Attacks in Machine Learning-based IoT Ecosystems: A Survey and the Open Libraries Behind Them
Chao Liu, Boxi Chen, Wei Shao +3
The advent of the Internet of Things (IoT) has brought forth an era of unprecedented connectivity, with an estimated 80 billion smart devices expected to be in operation by the end…
Learning Realistic Traffic Agents in Closed-loop
Chris Zhang, James Tu, Lunjun Zhang +3
Realistic traffic simulation is crucial for developing self-driving software in a safe and scalable manner prior to real-world deployment. Typically, imitation learning (IL) is use…