4 research outputs found
Generating Driving Scenes with Diffusion
In this paper we describe a learned method of traffic scene generation
designed to simulate the output of the perception system of a self-driving car.
In our "Scene Diffusion" system, inspired by latent diffusion, we use a novel
combination of diffusion and object detection to directly create realistic and
physically plausible arrangements of discrete bounding boxes for agents. We
show that our scene generation model is able to adapt to different regions in
the US, producing scenarios that capture the intricacies of each region.Comment: Accepted to the ICRA Scalable Autonomous Driving Worksho
Scenario Diffusion: Controllable Driving Scenario Generation With Diffusion
Automated creation of synthetic traffic scenarios is a key part of validating
the safety of autonomous vehicles (AVs). In this paper, we propose Scenario
Diffusion, a novel diffusion-based architecture for generating traffic
scenarios that enables controllable scenario generation. We combine latent
diffusion, object detection and trajectory regression to generate distributions
of synthetic agent poses, orientations and trajectories simultaneously. To
provide additional control over the generated scenario, this distribution is
conditioned on a map and sets of tokens describing the desired scenario. We
show that our approach has sufficient expressive capacity to model diverse
traffic patterns and generalizes to different geographical regions.Comment: NeurIPS 202