S2Planner: Multi-Scale Semantic Planner for End-to-End Autonomous Driving

arXiv:2609.29813 · cs.CV, cs.AI · Submitted 2026-09-24 · Read on arXiv

cs.CV, cs.AI

Submitted: 2026-09-24

Updated: 2026-09-24

Code: https://github.com/autonomousvision/navsim

Terminology

• Transformer — 23× in this paper · explained in 2 episodes
• Ablation — 8× in this paper · explained in 1 episode
• Generalization — 6× in this paper · explained in 13 episodes
• Spatial Tuning Adapter — 6× in this paper
• Calibration — 5× in this paper · explained in 7 episodes
• Spatial Cross Attention — 4× in this paper
• Self-Supervised Learning — 3× in this paper · explained in 8 episodes
• Consistency — 2× in this paper · explained in 3 episodes
• Diffusion Model — 2× in this paper · explained in 8 episodes
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• Foundation Model — 2× in this paper · explained in 3 episodes
• Foundation Models — 2× in this paper · explained in 7 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Actor — 1× in this paper · explained in 3 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Aggregation — 1× in this paper · explained in 1 episode
• Attention Mechanism — 1× in this paper · explained in 3 episodes
• Benchmark — 1× in this paper · explained in 2 episodes
• Benchmarking — 1× in this paper · explained in 3 episodes
• Closed-Loop System — 1× in this paper · explained in 4 episodes
• Contextual Understanding — 1× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Diffusion Models — 1× in this paper · explained in 6 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Feature Extraction — 1× in this paper · explained in 2 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• In Trajectory Self Attention — 1× in this paper
• Instance Segmentation — 1× in this paper · explained in 3 episodes
• Predictive Driver Model Score — 1× in this paper
• Representation Learning — 1× in this paper · explained in 2 episodes
• Steering — 1× in this paper · explained in 2 episodes
• Tokenization — 1× in this paper · explained in 2 episodes
• Transferability — 1× in this paper · explained in 4 episodes
• Verification — 1× in this paper · explained in 2 episodes

Sources

• Hydra-MDP: End-to-end Multimodal Planning with Multi-target Hydra-Distillation
• DINOv3
• Real-Time Object Detection Meets DINOv3
• DINOv2: Learning Robust Visual Features without Supervision
• BEiT: BERT Pre-Training of Image Transformers
• Deformable DETR: Deformable Transformers for End-to-End Object Detection
• DAB-DETR: Dynamic Anchor Boxes are Better Queries for DETR
• DiffRefiner: Coarse to Fine Trajectory Planning via Diffusion Refinement with Semantic Interaction for End to End Autonomous Driving
• End to End Learning for Self-Driving Cars
• BEVDet: High-performance Multi-camera 3D Object Detection in Bird-Eye-View
• BEVDet4D: Exploit Temporal Cues in Multi-camera 3D Object Detection
• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
• iPad: Iterative Proposal-centric End-to-End Autonomous Driving
• DRAMA: An Efficient End-to-end Motion Planner for Autonomous Driving with Mamba
• VADv2: End-to-End Vectorized Autonomous Driving via Probabilistic Planning

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