mlsanityData to 14 Oct 2026

LeAD: The LLM Enhanced Planning System Converged with End-to-end Autonomous Driving

arXiv 2507.05754 ↗First posted 8 Jul 2025cs.RO cs.AIcited by 5

On the map: End-to-end autonomous driving → Large language models for driving

Abstract (arXiv)

A principal barrier to large-scale deployment of urban autonomous driving systems lies in the prevalence of complex scenarios and edge cases. Existing systems fail to effectively interpret semantic information within traffic contexts and discern intentions of other participants, consequently generating decisions misaligned with skilled drivers' reasoning patterns. We present LeAD, a dual-rate autonomous driving architecture integrating imitation learning-based end-to-end (E2E) frameworks with large language model (LLM) augmentation. The high-frequency E2E subsystem maintains real-time perception-planning-control cycles, while the low-frequency LLM module enhances scenario comprehension through multi-modal perception fusion with HD maps and derives optimal decisions via chain-of-thought (CoT) reasoning when baseline planners encounter capability limitations. Our experimental evaluation in the CARLA Simulator demonstrates LeAD's superior handling of unconventional scenarios, achieving 71 points on Leaderboard V1 benchmark, with a route completion of 93%.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Placed in Large language models for driving, one of 9 directions in End-to-end autonomous driving. Builds on 34 of its 43 references that are in the map. First posted on arXiv 8 Jul 2025.

Builds on

34 of 43 references.

Closest ancestors · 6

Same direction, newest first

  1. 2025TeLL-Drive: Enhancing Autonomous Driving with Teacher LLM-Guided Deep Reinforcement Learning3 Feb
  2. 2024Driving Everywhere with Large Language Model Policy Adaptation8 Feb
  3. 2023LMDrive: Closed-Loop End-to-End Driving with Large Language Models12 Dec
  4. 2023LanguageMPC: Large Language Models as Decision Makers for Autonomous Driving4 Oct
  5. 2023Driving with LLMs: Fusing Object-Level Vector Modality for Explainable Autonomous Driving3 Oct
  6. 2023DriveGPT4: Interpretable End-to-end Autonomous Driving via Large Language Model2 Oct

Other references · 28

  1. 2017Attention Is All You Needcited by 60,361
  2. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  3. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  4. 2017CARLA: An Open Urban Driving Simulatorcited by 2,449
  5. 2018PointPillars: Fast Encoders for Object Detection from Point Cloudscited by 1,715
  6. 2022Planning-oriented Autonomous Drivingcited by 1,087
  7. 2022BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representationcited by 850
  8. 2023VoxPoser: Composable 3D Value Maps for Robotic Manipulation with Language Modelscited by 750
  9. 2023Eureka: Human-Level Reward Design via Coding Large Language Modelscited by 571
  10. 2023DriveLM: Driving with Graph Visual Question Answeringcited by 536
  11. 2024DriveVLM: The Convergence of Autonomous Driving and Large Vision-Language Modelscited by 490
  12. 2022TransFuser: Imitation with Transformer-Based Sensor Fusion for Autonomous Drivingcited by 485
16 more
  1. 2018ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worstcited by 397
  2. 2021Multi-Modal Fusion Transformer for End-to-End Autonomous Drivingcited by 377
  3. 2023GPT-Driver: Learning to Drive with GPTcited by 347
  4. 2021DenseTNT: End-to-end Trajectory Prediction from Dense Goal Setscited by 295
  5. 2022Trajectory-guided Control Prediction for End-to-end Autonomous Driving: A Simple yet Strong Baselinecited by 248
  6. 2022Safety-Enhanced Autonomous Driving Using Interpretable Sensor Fusion Transformercited by 212
  7. 2021End-to-End Urban Driving by Imitating a Reinforcement Learning Coachcited by 194
  8. 2019End-to-End Model-Free Reinforcement Learning for Urban Driving using Implicit Affordancescited by 149
  9. 2021Learning to drive from a world on railscited by 114
  10. 2023Hidden Biases of End-to-End Driving Modelscited by 96
  11. 2023ReasonNet: End-to-End Driving with Temporal and Global Reasoningcited by 93
  12. 2023FusionAD: Multi-modality Fusion for Prediction and Planning Tasks of Autonomous Drivingcited by 67
  13. 2021GRI: General Reinforced Imitation and its Application to Vision-Based Autonomous Drivingcited by 60
  14. 2024Cooperative Decision-Making for CAVs at Unsignalized Intersections: A MARL Approach with Attention and Hierarchical Game Priorscited by 14
  15. 2023Cooperative Driving of Connected Autonomous Vehicles in Heterogeneous Mixed Traffic: A Game Theoretic Approachcited by 8
  16. 2023Enhancing Social Decision-Making of Autonomous Vehicles: A Mixed-Strategy Game Approach With Interaction Orientation Identificationcited by 5

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 3.

5 papers.

  1. 2025A Knowledge-Driven Diffusion Policy for End-to-End Autonomous Driving Based on Expert Routingcited by 5
  2. 2026Bench2Drive-Robust: Benchmarking Closed-Loop Autonomous Driving under Deployment Perturbationscited by 3
  3. 2025LA-RL: Language Action-guided Reinforcement Learning with Safety Guarantees for Autonomous Highway Drivingcited by 0
  4. 2026Steins;Gate Drive: Semantic Safety Arbitration over Structured Futures for Latency-Decoupled LLM Planningcited by 0
  5. 2026Beyond Textual Chain-of-Thought: A Survey on Action-Grounded Reasoning in Autonomous Drivingcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Prompting Multi-Modal Tokens to Enhance End-to-End Autonomous Driving Imitation Learning with LLMs
  2. 2023LLM-Assist: Enhancing Closed-Loop Planning with Language-Based Reasoning
  3. 2026Planning-Oriented End-to-End Autonomous Driving: Architectures, Evaluation, and Emerging Paradigms
  4. 2024Making Large Language Models Better Planners with Reasoning-Decision Alignment
  5. 2025Drive-R1: Bridging Reasoning and Planning in VLMs for Autonomous Driving with Reinforcement Learning
  6. 2023DriveMLM: Aligning Multi-Modal Large Language Models with Behavioral Planning States for Autonomous Driving
  7. 2023A Language Agent for Autonomous Driving
  8. 2025ReCogDrive: A Reinforced Cognitive Framework for End-to-End Autonomous Driving