HorizonDrive: Self-Corrective Autoregressive World Model for Long-horizon Driving Simulation
Researchers have developed HorizonDrive, a novel framework for autoregressive driving simulation that enables minute-scale rollouts with bounded memory. This approach trains a teacher model to recover from its own prediction errors, allowing it to provide stable, long-horizon supervision. The system significantly improves metrics like FID and FVD on the nuScenes dataset compared to existing long-horizon baselines. AI
IMPACT Enables more realistic and longer-duration driving simulations, potentially accelerating autonomous vehicle development.