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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.
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.
Teaching VLMs to "look back" and "look ahead" with lightweight spatial reasoning tasks unlocks surprisingly strong navigation performance.
Latent reasoning can beat explicit Chain-of-Thought – but only if you force it to learn causal dynamics via a visual world model, not just language.
Image-goal navigation gets a boost from hierarchical reasoning, using vision-language models for high-level planning and online RL for low-level execution, significantly reducing wandering and improving success in complex environments.
Ditch language descriptions: this new driving model leverages dense 3D geometry for superior autonomous driving performance and cross-camera generalization.
