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D visual features, we introduce a plug-and-play Cross-View
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Ditch expensive, rendering-based RL for autonomous driving: PerlAD uses offline data to train agents in a fast, vector-space pseudo-simulation, outperforming prior methods by 10% on driving score.
Autonomous driving models can learn to avoid accidents *before* they happen by training on expert interventions and anticipating errors.
Ditch the planner-tracker hierarchy: RL can directly control spherical robots for efficient point-to-point navigation, even transferring from sim-to-real with high stability.
VLMs can now leverage the power of 3D geometric understanding for autonomous driving tasks thanks to a simple plug-and-play module.
Ditch the discrete anchors: MeanFuser achieves state-of-the-art autonomous driving trajectory generation by using a continuous Gaussian Mixture Noise representation and a mean-flow formulation for faster, more robust planning.
By decoupling generation and refinement experts within a masked diffusion VLA model, DriveFine achieves both flexible decoding and self-correction for autonomous driving.
