806 citations · 4.7k across the 98 of their papers we have counts for
16 papers · 1 filter
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…
QuAD: Query-based Interpretable Neural Motion Planning for Autonomous Driving
Sourav Biswas, Sergio Casas, Quinlan Sykora +3
A self-driving vehicle must understand its environment to determine the appropriate action. Traditional autonomy systems rely on object detection to find the agents in the scene. H…
Adv3D: Generating Safety-Critical 3D Objects through Closed-Loop Simulation
Jay Sarva, Jingkang Wang, James Tu +3
Self-driving vehicles (SDVs) must be rigorously tested on a wide range of scenarios to ensure safe deployment. The industry typically relies on closed-loop simulation to evaluate h…