Changhui Jing

Driving world action models explore candidate trajectories absent from logs during reinforcement-learning post-training, but logs store future video only for recorded trajectories, so those candidates lack matched visual supervision. SimVTA renders future video along candidate paths with a simulator that reconstructs real scenes via 3D Gaussian Splatting, giving prediction-independent targets, then selects high-reward, high-error futures by trajectory reward and video prediction error for flow-matching supervision while GRPO optimizes planning. On NAVSIM v1 it raises PDMS from 86.2 to 91.3, reaches 88.2 EPDMS on NAVSIM v2, and averages 38.8 HD-Score on HUGSIM.

Driving world action models explore candidate trajectories absent from logs during reinforcement-learning post-training, but logs store future video only for recorded trajectories, so those candidates lack matched visual supervision. SimVTA renders future video along candidate paths with a simulator that reconstructs real scenes via 3D Gaussian Splatting, giving prediction-independent targets, then selects high-reward, high-error futures by trajectory reward and video prediction error for flow-matching supervision while GRPO optimizes planning. On NAVSIM v1 it raises PDMS from 86.2 to 91.3, reaches 88.2 EPDMS on NAVSIM v2, and averages 38.8 HD-Score on HUGSIM.