ML Systems Wiki Knowledge base for ML systems, autonomy, and embodied AI
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paper 114 autonomous-driving 92 foundation-model 55 transformer 53 vla 49 planning 42 robotics 41 computer-vision 36 perception 32 ilya-30 29 multimodal 29 nlp 26 end-to-end 24 language-modeling 24 llm 17 reasoning 17 imitation-learning 16 3d-occupancy 15 vlm 15 bev 14 diffusion 13 e2e 12 reinforcement-learning 12 world-model 12 chain-of-thought 10 benchmark 9 scaling 9 cross-embodiment 7 driving 6 gaussian-splatting 6 generative-models 6 image-classification 6 information-theory 6 questions 6 self-supervised 6 sources 6 alignment 5 attention 5 cnn 5 foundation 5 knowledge-distillation 5 language-model 5 prediction 5 simulation 5 evaluation 4 image-generation 4 instruction-tuning 4 mixture-of-experts 4 rnn 4 sequence-to-sequence 4 sparse-representation 4 video-prediction 4 explainability 3 flow-matching 3 lstm 3 map 3 occupancy 3 open-source 3 semantic-segmentation 3 sequence-modeling 3 trajectory-prediction 3 vectorized-representation 3 3d-detection 2 3d-perception 2 3d-reconstruction 2 action-representation 2 autonomy 2 autoregressive 2 bimanual 2 closed-loop 2 complexity-theory 2 dataset 2 deployment 2 distributed-training 2 efficient-inference 2 embodied 2 fine-tuning 2 foundation-models 2 foundational 2 gaussian-representation 2 generation 2 generative 2 human-interaction 2 humanoid 2 manipulation 2 memory-augmented-networks 2 ml 2 multi-camera 2 multilingual 2 object-detection 2 parameter-efficient-fine-tuning 2 prompting 2 real-time 2 regularization 2 relational-reasoning 2 residual-networks 2 rlhf 2 scaling-laws 2 segmentation 2 self-improvement 2 self-supervised-learning 2 state-space 2 systems 2 thermodynamics 2 vision-language-model 2 vision-transformer 2 visual-question-answering 2 zero-shot 2 3d 1 3d-scene 1 3d-semantic-occupancy 1 agenda 1 agentic 1 agi 1 algorithmic-information-theory 1 algorithmic-randomness 1 asynchronous 1 attention-mechanism 1 batch 1 bayesian-inference 1 behavior-forecasting 1 camera-fusion 1 classifier-guidance 1 combinatorial-optimization 1 comparison 1 compression 1 computability 1 concept 1 contrastive-learning 1 control 1 convolutional-neural-networks 1 corpus 1 course 1 data-collection 1 decoupled 1 deep-learning 1 denoising 1 depth-estimation 1 dexterous-manipulation 1 differentiable-programming 1 diffusion-policy 1 diffusion-transformer 1 dilated-convolutions 1 dropout 1 efficient 1 embodied-ai 1 embodiment 1 emergent-abilities 1 end-to-end-learning 1 evaluation-metric 1 few-shot 1 few-shot-learning 1 foundations 1 frontend 1 gaussian 1 gaussian-rendering 1 generalist-agent 1 generalization 1 gpu-training 1 graph-neural-networks 1 grounding 1 grpo 1 hierarchical 1 high-frequency-control 1 hosting 1 ilya 1 image-captioning 1 image-text-retrieval 1 in-context-learning 1 inductive-bias 1 intelligence-measurement 1 interactive-annotation 1 interactive-segmentation 1 knowledge-preservation 1 kolmogorov-complexity 1 lanegcn 1 locomotion 1 machine-translation 1 mamba 1 mdl 1 message-passing 1 minimum-description-length 1 model-parallelism 1 model-predictive-control 1 model-selection 1 modular 1 molecular-property-prediction 1 multi-embodiment 1 multi-task 1 natural-language 1 neural-radiance-fields 1 neuro-symbolic 1 obsidian 1 open-world 1 optimization 1 orchestration 1 parallel-architecture 1 parameter-efficient 1 permutation-invariance 1 personalization 1 physical-ai 1 pipeline-parallelism 1 pointer-mechanism 1 privileged-supervision 1 probabilistic-planning 1 proprioception 1 quantization 1 queue 1 radar 1 recurrent-neural-networks 1 representation-learning 1 scene-understanding 1 search 1 seminal 1 sensor-fusion 1 set-modeling 1 siamese-networks 1 simulator 1 source 1 sparse-models 1 spatial-reasoning 1 speech-recognition 1 survey 1 synthesis 1 taxonomy 1 temporal 1 temporal-modeling 1 thesis 1 tokenization 1 tool-use 1 training 1 uniad 1 unified-stack 1 vanishing-gradients 1 variational-autoencoders 1 video-generation 1 video-understanding 1 visual-traces 1 vit 1

Pages tagged e2e

DriveMoE: Mixture-of-Experts for Vision-Language-Action Model in End-to-End Autonomous Driving
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📄 **[Read on arXiv](https://arxiv.org/abs/2505.16278)** DriveMoE introduces a dual-level Mixture-of-Experts (MoE) architecture to driving Vision-Language-Action models. The key innovation is applying expert specializati…
DrivoR: Driving on Registers
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📄 **[Read on arXiv](https://arxiv.org/abs/2601.05083)** DrivoR is a full-transformer autonomous driving architecture that uses camera-aware register tokens to compress multi-camera Vision Transformer features into a com…
End to End Learning for Self-Driving Cars
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📄 **[Read on arXiv](https://arxiv.org/abs/1604.07316)** This paper from NVIDIA, commonly known as "DAVE-2" or the "NVIDIA end-to-end driving paper," demonstrates that a single convolutional neural network can learn to m…
End-to-End Architectures
concept

"End-to-end" is one of the most overloaded terms in autonomous driving. This page defines a clear taxonomy, traces the evolution of E2E systems, and maps the current landscape. The literature uses "end-to-end" to mean a…
End-to-end Driving via Conditional Imitation Learning
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📄 **[Read on arXiv](https://arxiv.org/abs/1710.02410)** This paper introduces conditional imitation learning for end-to-end autonomous driving, where a neural network policy is conditioned on a discrete high-level comma…
Lmdrive Closed Loop End To End Driving With Large Language Models
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📄 **[Read on arXiv](https://arxiv.org/abs/2312.07488)** LMDrive is the first system to demonstrate and benchmark LLM-based driving in closed-loop simulation, introducing the LangAuto benchmark with ~64K instruction-foll…
Open Questions: End-to-End Driving
query

Stream-specific open questions for the end-to-end autonomous driving pillar. See wiki/queries/open-questions for the full tree across all streams. 1. **Unified vs. decoupled VLA:** Will EMMA's "everything as language to…
Orion Holistic End To End Autonomous Driving By Vision Language Instructed Action Generation
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📄 **[Read on arXiv](https://arxiv.org/abs/2503.19755)** ORION bridges the reasoning-action gap in driving VLAs through a three-component architecture consisting of QT-Former (visual encoding), an LLM reasoning core, and…
Senna: Bridging Large Vision-Language Models and End-to-End Autonomous Driving
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📄 **[Read on arXiv](https://arxiv.org/abs/2410.22313)** Two dominant paradigms exist in autonomous driving: large vision-language models (LVLMs) with strong reasoning but poor trajectory precision, and end-to-end (E2E)…
SimLingo: Vision-Only Closed-Loop Autonomous Driving with Language-Action Alignment
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📄 **[Read on arXiv](https://arxiv.org/abs/2503.09594)** Many driving VLM efforts improve language understanding (VQA, scene descriptions) but sacrifice actual driving performance. A model can correctly answer questions…
TransFuser: Imitation with Transformer-Based Sensor Fusion for Autonomous Driving
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📄 **[Read on arXiv](https://arxiv.org/abs/2205.15997)** TransFuser (Chitta et al., 2022) is a foundational paper for transformer-based sensor fusion in end-to-end autonomous driving. The key problem it addresses is how…
WoTE: End-to-End Driving with Online Trajectory Evaluation via BEV World Model
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📄 **[Read on arXiv](https://arxiv.org/abs/2504.01941)** End-to-end driving models typically output a single trajectory and trust it entirely, with no mechanism to evaluate whether the predicted path is safe before execu…