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📄 **[Read on arXiv](https://arxiv.org/abs/2205.06175)** Reed et al., Transactions on Machine Learning Research (TMLR), 2022. - [Paper](https://arxiv.org/abs/2205.06175) Gato, developed by DeepMind, is a single transform…

Santoro, Raposo, Barrett, Malinowski, Pascanu, Battaglia, Lillicrap (DeepMind), NeurIPS, 2017. 📄 **[Read on arXiv](https://arxiv.org/abs/1706.01427)** Relation Networks (RNs) are a simple neural network module for relat…

📄 **[Read on arXiv](https://arxiv.org/abs/math/0406077)** Grünwald, arXiv math/0406077 / MIT Press, 2004. - [Paper](https://arxiv.org/abs/math/0406077) The Minimum Description Length (MDL) principle formalizes Occam's r…

📄 **[Read on arXiv](https://arxiv.org/abs/2311.10813)** Agent-Driver reframes autonomous driving as a cognitive agent problem, positioning a large language model as the central reasoning and planning engine rather than…

Yan Wang, Wenjie Luo, Junjie Bai, Yulong Cao, Marco Pavone + 37 co-authors (NVIDIA), arXiv, 2025. 📄 **[Read on arXiv](https://arxiv.org/abs/2511.00088)** Alpamayo-R1 is NVIDIA's production-grade Vision-Language-Action (…

Bo Jiang, Shaoyu Chen, Qian Zhang, Wenyu Liu, Xinggang Wang, arXiv, 2025. 📄 **[Read on arXiv](https://arxiv.org/abs/2503.07608)** AlphaDrive is the first application of GRPO (Group Relative Policy Optimization) reinforc…

📄 **[Read on arXiv](https://arxiv.org/abs/1706.03762)** Vaswani, Shazeer, Parmar, Uszkoreit, Jones, Gomez, Kaiser, Polosukhin, NeurIPS, 2017. - [Paper](https://arxiv.org/abs/1706.03762) - [The Annotated Transformer](htt…

📄 **[Read on arXiv](https://arxiv.org/abs/1810.04805)** Devlin, Chang, Lee, Toutanova (Google AI Language), NAACL, 2019. - [Paper](https://aclanthology.org/N19-1423/) - [arXiv](https://arxiv.org/abs/1810.04805) BERT (Bi…

**[Read on arXiv](https://arxiv.org/abs/2502.19694)** BEVDiffuser addresses a fundamental but under-explored problem in BEV-based perception: the inherent noise in BEV feature maps caused by sensor limitations and the l…

📄 **[Read on arXiv](https://arxiv.org/abs/2203.17270)** Li, Wang, Li, Xie, Sima, Lu, Yu, Dai (Shanghai AI Lab / Nanjing University / HKU), ECCV, 2022. - [Paper](https://arxiv.org/abs/2203.17270) BEVFormer generates a un…

📄 **[Read on arXiv](https://arxiv.org/abs/2503.14182)** BridgeAD tackles a critical limitation in end-to-end autonomous driving: the ineffective utilization of historical temporal information. Current systems either agg…

Dosovitskiy, Ros, Codevilla, Lopez, Koltun (Intel Labs / Toyota Research Institute / CVC Barcelona), CoRL, 2017. 📄 **[Read on arXiv](https://arxiv.org/abs/1711.03938)** CARLA (Car Learning to Act) is an open-source simu…

[Read on arXiv](https://arxiv.org/abs/2502.19908) CarPlanner (Zhejiang University + Cainiao Network, CVPR 2025) introduces a consistent autoregressive reinforcement learning planner that is the first RL-based planner to…

📄 **[Read on arXiv](https://arxiv.org/abs/2201.11903)** Wei et al., arXiv 2201.11903, 2022 (NeurIPS 2022). - [Paper](https://arxiv.org/abs/2201.11903) Chain-of-thought (CoT) prompting demonstrates that including interme…

📄 **[Read on arXiv](https://arxiv.org/abs/1812.03079)** Bansal, Krizhevsky, Ogale (Waymo Research), RSS, 2019. - [Paper](https://arxiv.org/abs/1812.03079) ChauffeurNet is Waymo's mid-level imitation learning system that…

📄 **[Course Website](https://cs231n.stanford.edu/)** Li, Karpathy, and Johnson, Stanford University, 2015 (ongoing). - [Course](https://cs231n.stanford.edu/) CS231n is a widely used Stanford deep learning for computer v…

📄 **[Read on arXiv](https://arxiv.org/abs/1512.03385)** He, Zhang, Ren, Sun (Microsoft Research), CVPR, 2016. - [Paper](https://arxiv.org/abs/1512.03385) Deep Residual Learning introduces skip connections that add the i…

Amodei et al., ICML, 2016. 📄 **[Read on arXiv](https://arxiv.org/abs/1512.02595)** Deep Speech 2 is an end-to-end speech recognition system where a single RNN trained with CTC loss on spectrograms replaces the entire tr…

📄 **[Read on arXiv](https://arxiv.org/abs/2006.11239)** Ho, Jain, and Abbeel, NeurIPS, 2020. - [Paper](https://arxiv.org/abs/2006.11239) Denoising Diffusion Probabilistic Models (DDPM) demonstrates that high-quality ima…

[Read on arXiv](https://arxiv.org/abs/2502.05855) DexVLA introduces a paradigm shift in VLA architecture by scaling the action generation component to 1 billion parameters using a diffusion-based expert, rather than foc…

[Read on arXiv](https://arxiv.org/abs/2411.15139) DiffusionDrive (HUST/Horizon Robotics, CVPR 2025 Highlight) proposes a truncated diffusion model for end-to-end autonomous driving that achieves real-time inference whil…

**[Read on arXiv](https://arxiv.org/abs/2503.19757)** Dita introduces a scalable framework that leverages full Transformer architectures to directly denoise continuous action sequences through a unified multimodal diffu…

📄 **[Read on arXiv](https://arxiv.org/abs/2309.10228)** Drive as You Speak (DAYS) proposes a framework for enabling natural language interaction between human passengers and autonomous vehicles using large language mode…

📄 **[Read on arXiv](https://arxiv.org/abs/2408.14197)** Drive-OccWorld introduces a vision-centric 4D occupancy forecasting world model that directly integrates with end-to-end planning. The core premise is that current…

[Read on arXiv](https://arxiv.org/abs/2309.09777) DriveDreamer (ECCV 2024) is the first world model built entirely from real-world driving data, addressing fundamental limitations of prior approaches that relied on simu…

Zhenhua Xu, Yujia Zhang, Enze Xie, Zhen Zhao, Yong Guo, Kwan-Yee K. Wong, Zhenguo Li, Hengshuang Zhao, IEEE Robotics and Automation Letters, 2024. 📄 **[Read on arXiv](https://arxiv.org/abs/2310.01412)** DriveGPT4 applie…

[Read on arXiv](https://arxiv.org/abs/2412.14415) DriveGPT (Cruise, ICML 2025) is the first work to systematically study scaling laws for autoregressive behavior models in autonomous driving. Drawing inspiration from th…

📄 **[Read on arXiv](https://arxiv.org/abs/2312.14150)** DriveLM formalizes driving reasoning as Graph Visual Question Answering (GVQA), where QA pairs are connected via logical dependencies forming a reasoning graph tha…

📄 **[Read on arXiv](https://arxiv.org/abs/2312.09245)** DriveMLM proposes using a multimodal LLM as a plug-and-play behavioral planning module within existing autonomous driving stacks (Apollo, Autoware), rather than re…

📄 **[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…

📄 **[Read on arXiv](https://arxiv.org/abs/2402.12289)** DriveVLM proposes a hierarchical approach to integrating Vision-Language Models into autonomous driving, emphasizing scene understanding and multi-level planning r…

[Read on arXiv](https://arxiv.org/abs/2310.01957) Driving with LLMs (Wayve, ICRA 2024) is one of the first concrete demonstrations of using a large language model as the decision-making "brain" for autonomous driving. T…

📄 **[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…

📄 **[Read on arXiv](https://arxiv.org/abs/2410.23262)** EMMA is Waymo's industry-scale demonstration of the "everything as language tokens" paradigm for autonomous driving. A single large multimodal foundation model uni…

📄 **[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…

📄 **[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…

[Read on arXiv](https://arxiv.org/abs/2501.09747) FAST (Frequency-space Action Sequence Tokenization) introduces a novel action tokenizer for VLA models that leverages signal processing to dramatically compress robot ac…

:page_facing_up: **[Read on arXiv](https://arxiv.org/abs/2502.17288)** GaussianFlowOcc (ICCV 2025) introduces a transformative approach to 3D semantic occupancy estimation for autonomous driving by replacing traditional…

**[Read on arXiv](https://arxiv.org/abs/2412.04384)** GaussianFormer-2 addresses 3D semantic occupancy prediction for vision-centric autonomous driving by rethinking how 3D Gaussians represent occupied space. The origin…

📄 **[Read on arXiv](https://arxiv.org/abs/2504.01957)** Bird's-Eye View (BEV) perception faces a fundamental trade-off between accuracy and computational efficiency. High-performing 3D projection methods like BEVFormer…

:page_facing_up: **[Read on arXiv](https://arxiv.org/abs/2408.11447)** GaussianOcc by Gan et al. (University of Tokyo / RIKEN / South China University of Technology / SIAT-CAS) is a systematic method that applies Gaussi…

:page_facing_up: **[Read on arXiv](https://arxiv.org/abs/2502.05040)** GaussRender by Chambon et al. (Valeo AI / Sorbonne, ICCV 2025) introduces a plug-and-play training-time module that improves 3D occupancy prediction…

**[Read on arXiv](https://arxiv.org/abs/2412.13193)** GaussTR is a Gaussian-based Transformer framework that achieves zero-shot semantic occupancy prediction without any 3D annotations. The key idea is to combine sparse…

[Read on arXiv](https://arxiv.org/abs/2402.11502) GenAD (ECCV 2024) reframes end-to-end autonomous driving as a generative modeling problem, simultaneously generating future trajectories for all traffic participants rat…

[Read on arXiv](https://arxiv.org/abs/2503.05689) GoalFlow (Horizon Robotics / HKU, CVPR 2025) introduces a goal-driven flow matching framework for multimodal trajectory generation in autonomous driving. The method achi…

📄 **[Read on arXiv](https://arxiv.org/abs/1811.06965)** GPipe introduces micro-batch pipeline parallelism as a practical method for training neural networks too large to fit on a single accelerator. The core idea is to…

📄 **[Read on arXiv](https://arxiv.org/abs/2310.01415)** GPT-Driver reformulates autonomous driving motion planning as a language modeling problem. Scene context (object positions, velocities, lane geometry) and ego vehi…

:page_facing_up: **[Read at Figure AI](https://www.figure.ai/news/helix)** Helix (Figure AI, Technical Report February 2025) is the first vision-language-action model to achieve high-rate continuous control of an entire…

📄 **[Read on arXiv](https://arxiv.org/abs/1603.05027)** This paper, a follow-up to the original ResNet work, provides both theoretical analysis and empirical evidence that the arrangement of operations within residual b…

📄 **[Read Paper](https://papers.nips.cc/paper/2012/hash/c399862d3b9d6b76c8436e924a68c45b-Abstract.html)** AlexNet, as this paper's architecture came to be known, is a deep convolutional neural network trained on GPUs th…

[Read on arXiv](https://arxiv.org/abs/2312.03031) This paper (CVPR 2024, NVIDIA / Nanjing University) delivers a "wake-up call" to the autonomous driving research community by demonstrating that simple baselines using o…

📄 **[Read Paper](https://www.cs.toronto.edu/~hinton/absps/colt93.pdf)** This paper by Hinton and van Camp bridges information theory and neural network generalization by proposing that model complexity should be measure…

[Read on arXiv](https://arxiv.org/abs/2505.23705) This paper from Physical Intelligence identifies and addresses a critical problem in VLA training: gradient interference causes the pre-trained VLM backbone to degrade w…

📄 **[AMS Book Page](https://bookstore.ams.org/surv-220)** This monograph by Shen, Uspensky, and Vereshchagin is the definitive modern reference on algorithmic information theory. The central concept is Kolmogorov comple…

📄 **[Read on arXiv](https://arxiv.org/abs/2005.14165)** GPT-3 is a 175 billion parameter autoregressive language model that demonstrated a remarkable emergent capability: in-context learning, where the model performs ne…

[Read on arXiv](https://arxiv.org/abs/2406.08481) LAW (CASIA, ICLR 2025) introduces a self-supervised latent world model that enhances end-to-end autonomous driving by learning to predict future latent states of the dri…

📄 **[Read on arXiv](https://arxiv.org/abs/1912.12294)** Learning by Cheating introduces a two-stage training paradigm for end-to-end autonomous driving that has become one of the most influential design patterns in the…

📄 **[Read on arXiv](https://arxiv.org/abs/2007.13732)** LaneGCN introduces a graph neural network architecture for motion forecasting in autonomous driving that operates directly on the lane graph structure of HD maps.…

📄 **[Read on arXiv](https://arxiv.org/abs/2008.05711)** Lift, Splat, Shoot (LSS) introduced a differentiable pipeline for transforming multi-camera images into a unified bird's-eye view (BEV) representation without requ…

📄 **[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…

📄 **[Read Thesis](https://www.vetta.org/documents/Machine_Super_Intelligence.pdf)** Shane Legg's 2008 PhD thesis provides perhaps the most rigorous mathematical definition of general intelligence, grounding informal int…

📄 **[Read on arXiv](https://arxiv.org/abs/1511.07122)** This paper introduced dilated (atrous) convolutions as a principled alternative to the downsample-then-upsample paradigm for dense prediction tasks. By inserting g…

📄 **[Read on arXiv](https://arxiv.org/abs/1409.0473)** This paper introduced the attention mechanism to deep learning, arguably the single most influential architectural innovation leading to modern transformers and LLM…

📄 **[Read on arXiv](https://arxiv.org/abs/1704.01212)** This paper provided the conceptual unification that the graph neural network field needed. By showing that seemingly different architectures -- GCN, GraphSAGE, Gat…

📄 **[Read on arXiv](https://arxiv.org/abs/1410.5401)** Neural Turing Machines (NTMs) augment neural networks with a differentiable external memory matrix and soft attention-based read/write heads, enabling them to learn…

📄 **[Read on arXiv](https://arxiv.org/abs/1903.11027)** nuScenes is a large-scale multimodal dataset for autonomous driving that provides synchronized data from 6 cameras (360-degree coverage), 1 LiDAR, 5 radars, GPS, a…

**[Read on arXiv](https://arxiv.org/abs/2408.09859)** OccMamba is the first Mamba-based network for semantic occupancy prediction, replacing transformer architectures' quadratic complexity with Mamba's linear complexity…

[Read on arXiv](https://arxiv.org/abs/2502.19645) OpenVLA-OFT presents a systematic empirical study of fine-tuning strategies for Vision-Language-Action models, identifying a recipe that boosts the original OpenVLA from…

📄 **[Read on arXiv](https://arxiv.org/abs/2406.09246)** OpenVLA is a 7-billion parameter open-source vision-language-action model that demonstrates generalist robotic manipulation by fine-tuning a pretrained vision-lang…

📄 **[Read on arXiv](https://arxiv.org/abs/1511.06391)** This paper by Samy Bengio, Oriol Vinyals, and Manjunath Kudlur challenges a core assumption in sequence modeling: that the order of input and output data is merely…

📄 **[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…

📄 **[Read on arXiv](https://arxiv.org/abs/2303.03378)** PaLM-E is a 562-billion parameter embodied multimodal language model created by Google that injects continuous sensor observations (images, point clouds, robot sta…

[Read on CVF Open Access](https://openaccess.thecvf.com/content/CVPR2024/html/Weng_PARA-Drive_Parallelized_Architecture_for_Real-time_Autonomous_Driving_CVPR_2024_paper.html) PARA-Drive (NVIDIA Research / USC / Stanford…

[Read on arXiv](https://arxiv.org/abs/2511.14759) pi*0.6 extends the pi0/pi0.5/pi0.6 VLA family with the ability to learn from autonomous deployment experience using reinforcement learning. While prior models learn prim…

[Read on arXiv](https://arxiv.org/abs/2504.16054) pi0.5 is the successor to pi0, developed by Physical Intelligence, and represents the first VLA model capable of performing 10-15 minute long-horizon tasks in previously…

[Read on arXiv](https://arxiv.org/abs/2410.24164) pi0 is a vision-language-action flow model developed by Physical Intelligence that represents a foundational step toward general-purpose robot control. The key innovatio…

📄 **[Read on arXiv](https://arxiv.org/abs/2212.10156)** UniAD (Unified Autonomous Driving) is a planning-oriented end-to-end framework that unifies perception, prediction, and planning into a single differentiable netwo…

📄 **[Read on arXiv](https://arxiv.org/abs/1506.03134)** Pointer Networks repurpose the attention mechanism as an output distribution, replacing the fixed output vocabulary of sequence-to-sequence models with attention w…

:page_facing_up: **[Read on arXiv](https://arxiv.org/abs/2506.04218)** Pseudo-Simulation by Cao, Hallgarten et al. (Tubingen / Shanghai AI Lab / NVIDIA / Stanford, CoRL 2025) introduces a novel evaluation paradigm for a…

📄 **[Read on arXiv](https://arxiv.org/abs/1405.6903)** This paper bridges thermodynamics and computational complexity to formalize a deep intuition: mixing cream into coffee produces increasingly complex patterns (swirl…

:page_facing_up: **[Read on arXiv](https://arxiv.org/abs/2412.12725)** RaCFormer by Chu et al. (USTC, CVPR 2025) addresses a fundamental problem in radar-camera fusion for 3D object detection: the image-to-BEV transform…

[Read on arXiv](https://arxiv.org/abs/2410.07864) RDT-1B (Tsinghua University, ICLR 2025) presents the largest diffusion transformer for bimanual robot manipulation, scaling to 1.2B parameters. Bimanual manipulation --…

📄 **[Read on arXiv](https://arxiv.org/abs/2210.03629)** Large language models had demonstrated two powerful capabilities in isolation: chain-of-thought reasoning for multi-step problem solving, and action generation for…

📄 **[Read on arXiv](https://arxiv.org/abs/2312.03661)** Reason2Drive provides the largest reasoning chain dataset for driving (>600K video-text pairs from nuScenes, Waymo, and ONCE) and introduces an aggregated evaluati…

📄 **[Read on arXiv](https://arxiv.org/abs/1409.2329)** This paper discovered that dropout can be successfully applied to LSTMs if it is restricted to non-recurrent (feedforward) connections only, preserving the LSTM's a…

📄 **[Read on arXiv](https://arxiv.org/abs/1806.01822)** Traditional RNNs (LSTMs, GRUs) compress all sequential information into a single fixed-size hidden vector, which fundamentally limits their ability to store and re…

[Read on arXiv](https://arxiv.org/abs/2407.08693) ECoT (UC Berkeley / Stanford / University of Warsaw, 2024) introduces Embodied Chain-of-Thought reasoning for Vision-Language-Action (VLA) models, demonstrating that gen…

📄 **[Read on arXiv](https://arxiv.org/abs/2212.06817)** RT-1 is a landmark paper from Google/Everyday Robots demonstrating that a 35M-parameter Transformer model, trained on a large and diverse dataset of real-robot dem…

📄 **[Read on arXiv](https://arxiv.org/abs/2307.15818)** RT-2 is the defining paper for the modern Vision-Language-Action (VLA) paradigm. It demonstrates that large vision-language models (VLMs) pretrained on internet-sc…

📄 **[Read on arXiv](https://arxiv.org/abs/2505.24139)** S4-Driver is a self-supervised framework that adapts Multimodal Large Language Models (MLLMs) for autonomous vehicle motion planning. The system processes multi-vi…

📄 **[Read on arXiv](https://arxiv.org/abs/2001.08361)** This is the canonical early scaling-law paper for language models, authored by Kaplan et al. at OpenAI. It demonstrated that neural language model cross-entropy lo…

📄 **[Read on arXiv](https://arxiv.org/abs/2509.15155)** This Google DeepMind paper addresses a fundamental limitation of Embodied Foundation Models (EFMs): while they demonstrate impressive semantic generalization (unde…

📄 **[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)…

📄 **[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…

**[Read on arXiv](https://arxiv.org/abs/2506.01844)** SmolVLA is a 450M-parameter open-source VLA model from Hugging Face that demonstrates competitive performance with models 10x larger while being trainable on a singl…

[Read on arXiv](https://arxiv.org/abs/2505.16805) SOLVE proposes a synergistic framework that combines a Vision-Language Model (VLM) reasoning branch (SOLVE-VLM) with an end-to-end (E2E) driving network (SOLVE-E2E), con…

:page_facing_up: **[Read on arXiv](https://arxiv.org/abs/2405.19620)** SparseDrive by Sun et al. (ICRA 2025) proposes a paradigm shift from dense BEV-based end-to-end driving to fully sparse scene representations. The c…

:page_facing_up: **[Read on arXiv](https://arxiv.org/abs/2603.29163)** SparseDriveV2 by Sun et al. (2026) pushes the performance boundary of scoring-based trajectory planning by demonstrating that "scoring is all you ne…

[Read on arXiv](https://arxiv.org/abs/2501.15830) SpatialVLA addresses a fundamental limitation of existing VLA models: they operate on 2D visual inputs despite robot manipulation requiring understanding of 3D spatial r…

📄 **[Read on arXiv](https://arxiv.org/abs/1909.10838)** For autonomous vehicles to be truly useful as personal transportation, passengers should be able to issue natural-language commands like "park behind that blue car…

📄 **[Read on arXiv](https://arxiv.org/abs/1807.11546)** End-to-end driving models produce control signals without any rationale, making them opaque and untrustworthy for safety-critical deployment. This paper by Kim et…

📄 **[Read Blog Post](https://scottaaronson.blog/?p=762)** Scott Aaronson's blog post highlights an asymmetry between entropy and complexity as a way of thinking about structure formation in physical and computational sy…

📄 **[Read Blog Post](https://karpathy.github.io/2015/05/21/rnn-effectiveness/)** Andrej Karpathy's 2015 blog post offers a vivid qualitative demonstration that character-level recurrent neural networks with LSTM cells c…

**[Read on arXiv](https://arxiv.org/abs/2505.05753)** This paper investigates whether increasing robot diversity during training improves generalization to unseen robots, analogous to how data scaling improves language…

📄 **[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…

📄 **[Read on arXiv](https://arxiv.org/abs/2305.10601)** Language models are typically used in a left-to-right token-generation mode, which limits their ability to explore alternative reasoning paths or backtrack from mi…

📄 **[Read Blog Post](https://colah.github.io/posts/2015-08-Understanding-LSTMs/)** Christopher Olah's 2015 blog post is a widely used pedagogical reference for understanding LSTM internals. The post explains why vanilla…

**[Read on arXiv](https://arxiv.org/abs/2501.10105)** UniAct addresses a critical challenge in embodied AI: robot action data suffers from severe heterogeneity across platforms, control interfaces, and physical embodime…

📄 **[Read on arXiv](https://arxiv.org/abs/2303.12077)** VAD (Vectorized Scene Representation for Efficient Autonomous Driving) by Jiang et al. (ICCV 2023) is a pivotal paper in the shift from dense rasterized scene repr…

📄 **[Read on arXiv](https://arxiv.org/abs/1611.02731)** The Variational Lossy Autoencoder (VLAE) by Chen, Kingma, Salimans, Duan, Dhariwal, Schulman, Sutskever, and Abbeel (2016) addresses the fundamental tension in VAE…

📄 **[Read on arXiv](https://arxiv.org/abs/2005.04259)** VectorNet (Gao et al., Waymo/Google, CVPR 2020) is a foundational paper that moved motion prediction and map encoding away from rasterized image-based representati…

:page_facing_up: **[Read on arXiv](https://arxiv.org/abs/2412.14803)** Video Prediction Policy (VPP) by Hu, Guo et al. (ICML 2025 Spotlight) proposes that video diffusion models (VDMs) are not just generators of future…

📄 **[Read on arXiv](https://arxiv.org/abs/2401.05577)** VLP (Vision Language Planning) by Pan et al. (CVPR 2024) represents a fundamentally different approach to using language in autonomous driving compared to instruct…

📄 **[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…