Anthony Gosselin — accepted AI-conference papers

2025

Scenario Dreamer: Vectorized Latent Diffusion for Generating Driving Simulation Environments

Luke Rowe, Roger Girgis, Anthony Gosselin, Liam Paull, Christopher Pal, Felix Heide

CVPR 2025poster

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 agent bounding boxes--and closed-loop agent behaviours. Existing methods for generating driving simulation environments e…

Cited by 3 Source ↗PDF ↗Scholar ↗

2024

CtRL-Sim: Reactive and Controllable Driving Agents with Offline Reinforcement Learning

Luke Rowe, Roger Girgis, Anthony Gosselin, Bruno Carrez, Florian Golemo, Felix Heide +2

CoRL 2024poster

Evaluating autonomous vehicle stacks (AVs) in simulation typically involves replaying driving logs from real-world recorded traffic. However, agents replayed from offline data are not reactive and hard to intuitively control. Existing approaches address these challenges by proposing methods that rel…

Cited by 6 Source ↗Scholar ↗