Ziying Song

Command-inconsistent memory can lead to a 15.6% increase in collision rates during autonomous driving, but MomADv2 effectively filters this noise for safer long-horizon planning.

StableDrive slashes collision rates by over 23% while redefining how historical context is leveraged in autonomous driving.

PhyLatent slashes failure rates in JEPA world models while boosting predictive control success from 70% to 78%—and even up to 98% in complex environments.

Long-horizon planning in autonomous driving just got a major upgrade, with GraphWorld cutting collision rates and boosting performance in complex environments.

GraphBEV++ outperforms five existing baselines in addressing critical misalignment issues, significantly boosting performance in both perception and planning tasks for autonomous vehicles.

By fusing a world model with a vision-language model, MindDrive achieves state-of-the-art autonomous driving performance, suggesting a promising path toward interpretable and cognitively guided systems.