4 papers
Ctrl-Crash: Controllable Diffusion for Realistic Car Crashes
Anthony Gosselin, Ge Ya Luo, Luis Lara +5
Video diffusion techniques have advanced significantly in recent years; however, they struggle to generate realistic imagery of car crashes due to the scarcity of accident events i…
Scenario Dreamer: Vectorized Latent Diffusion for Generating Driving Simulation Environments
Luke Rowe, Roger Girgis, Anthony Gosselin +3
We introduce Scenario Dreamer, a fully data-driven generative simulator for autonomous vehicle planning that generates both the initial traffic scene - comprising a lane graph and…
Ctrl-V: Higher Fidelity Video Generation with Bounding-Box Controlled Object Motion
Ge Ya Luo, Zhi Hao Luo, Anthony Gosselin +2
Controllable video generation has attracted significant attention, largely due to advances in video diffusion models. In domains such as autonomous driving, it is essential to deve…
CtRL-Sim: Reactive and Controllable Driving Agents with Offline Reinforcement Learning
Luke Rowe, Roger Girgis, Anthony Gosselin +5
Evaluating autonomous vehicle stacks (AVs) in simulation typically involves replaying driving logs from real-world recorded traffic. However, agents replayed from offline data are…