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📄 **[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 (…

📄 **[Read on arXiv](https://arxiv.org/abs/2406.14556)** AsyncDriver addresses the practical deployment problem of LLM-enhanced driving planners: LLMs are too slow for frame-by-frame planning. The key insight is that hig…

📄 **[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…

[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/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…

[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/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/2308.00398)** DriveAdapter (Jia et al., ICCV 2023) identifies and addresses a fundamental structural problem in end-to-end autonomous driving: the tight coupling between percept…

📄 **[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/2503.07656)** DriveTransformer represents a fundamental departure from existing end-to-end autonomous driving approaches. Rather than following sequential perception-prediction-…

📄 **[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/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/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 on arXiv](https://arxiv.org/abs/2406.06978)** Hydra-MDP addresses a fundamental limitation of imitation learning for autonomous driving: standard behavior cloning learns only to mimic human demo…

📄 **[Read on arXiv](https://arxiv.org/abs/2310.03026)** LanguageMPC addresses a fundamental limitation in autonomous driving: traditional planners (MPC, RL) struggle with complex scenarios that require high-level reason…

📄 **[Read on arXiv](https://arxiv.org/abs/2402.01817)** This paper by Subbarao Kambhampati and colleagues at Arizona State University addresses one of the most important questions in modern AI: can large language models…

📄 **[Read on arXiv](https://arxiv.org/abs/2503.03125)** End-to-end autonomous driving systems suffer from a critical limitation: temporal inconsistency. Current systems operate in a "one-shot" manner, making trajectory…

:page_facing_up: **[Read on arXiv](https://arxiv.org/abs/2406.15349)** Autonomous vehicle evaluation has long been split between two unsatisfying extremes: open-loop metrics that replay logged trajectories and compare p…

📄 **[Read on arXiv](https://arxiv.org/abs/2311.16038)** OccWorld introduces a generative world model that operates in 3D semantic occupancy space, jointly forecasting future scene evolution and ego vehicle trajectories.…

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…

Stream-specific open questions for LLM reasoning applied to driving and robotics. See wiki/queries/open-questions for the full tree across all streams. 1. **Language at maturity:** As driving VLAs improve, does language…

📄 **[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…

Planning converts scene understanding and future predictions into driving actions. It is the module closest to the physical world and the one where errors are most consequential. The field has evolved from rule-based st…

📄 **[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/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/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)…

: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/2312.09397)** Talk2Drive introduces an LLM-based framework for personalized autonomous driving through natural language interaction, demonstrated in real-world field experiments…

📄 **[Read on arXiv](https://arxiv.org/abs/2305.06242)** Think Twice (Jia et al., 2023) addresses a fundamental imbalance in end-to-end autonomous driving: while the community has invested heavily in sophisticated encode…

📄 **[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/2402.13243)** VADv2 by Chen et al. (2024) is the successor to VAD, addressing a fundamental limitation of deterministic planners in autonomous driving: they output a single traj…

📄 **[Read on arXiv](https://arxiv.org/abs/2405.17398)** Vista (NeurIPS 2024) is a generalizable driving world model that achieves high-fidelity video prediction at 10 Hz and 576x1024 resolution with versatile multi-moda…

📄 **[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/2307.05973)** VoxPoser addresses a fundamental bottleneck in robot manipulation: translating open-ended natural language instructions into precise physical actions without requi…

📄 **[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…