ConDenseDrive: Knowledge-Dense, Token-Sparse Visual Encoder for End-to-End Autonomous Driving
cs.CV, cs.RO
Submitted: 2026-10-12
Updated: 2026-10-12
Comments: 17 pages, 8 figures
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Sources
- CLOVER: Closed-Loop Value Estimation and Ranking for End-to-End Autonomous Driving Planning
- Token Merging: Your ViT But Faster
- Pseudo-Simulation for Autonomous Driving
- VADv2: End-to-End Vectorized Autonomous Driving via Probabilistic Planning
- RAD-2: Scaling Reinforcement Learning in a Generator-Discriminator Framework
- Distilling the Knowledge in a Neural Network
- LoRA: Low-Rank Adaptation of Large Language Models
- Hydra-MDP++: Advancing End-to-End Driving via Expert-Guided Hydra-Distillation
- UniDriveVLA: Unifying Understanding, Perception, and Action Planning for Autonomous Driving
- Hydra-MDP: End-to-end Multimodal Planning with Multi-target Hydra-Distillation
- Generalized Trajectory Scoring for End-to-end Multimodal Planning
- Not All Patches are What You Need: Expediting Vision Transformers via Token Reorganizations
- Planning-aligned Token Compression for Long-Context Autonomous Driving
- Depth Anything 3: Recovering the Visual Space from Any Views
- See Less, Drive Better: Generalizable End-to-End Autonomous Driving via Foundation Models Stochastic Patch Selection
- Theia: Distilling Diverse Vision Foundation Models for Robot Learning
- DINOv3
- Senna-2: Aligning VLM and End-to-End Driving Policy for Consistent Decision Making and Planning
- Learning to Drive is a Free Gift: Large-Scale Label-Free Autonomy Pretraining from Unposed In-The-Wild Videos
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