mlsanityData to 14 Oct 2026

Edge-Cloud Collaborative Motion Planning for Autonomous Driving with Large Language Models

arXiv 2408.09972 ↗First posted 19 Aug 2024cs.RO cs.AIcited by 5

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

Abstract (arXiv)

Integrating large language models (LLMs) into autonomous driving enhances personalization and adaptability in open-world scenarios. However, traditional edge computing models still face significant challenges in processing complex driving data, particularly regarding real-time performance and system efficiency. To address these challenges, this study introduces EC-Drive, a novel edge-cloud collaborative autonomous driving system with data drift detection capabilities. EC-Drive utilizes drift detection algorithms to selectively upload critical data, including new obstacles and traffic pattern changes, to the cloud for processing by GPT-4, while routine data is efficiently managed by smaller LLMs on edge devices. This approach not only reduces inference latency but also improves system efficiency by optimizing communication resource use. Experimental validation confirms the system's robust processing capabilities and practical applicability in real-world driving conditions, demonstrating the effectiveness of this edge-cloud collaboration framework. Our data and system demonstration will be released at https://sites.google.com/view/ec-drive.

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 27 of its 34 references that are in the map. First posted on arXiv 19 Aug 2024.

Builds on

27 of 34 references.

Closest ancestors · 6

Same direction, newest first

  1. 2024AgentsCoDriver: Large Language Model Empowered Collaborative Driving with Lifelong Learning9 Apr
  2. 2024LimSim++: A Closed-Loop Platform for Deploying Multimodal LLMs in Autonomous Driving2 Feb
  3. 2023DriveMLM: Aligning Multi-Modal Large Language Models with Behavioral Planning States for Autonomous Driving14 Dec
  4. 2023DriveGPT4: Interpretable End-to-end Autonomous Driving via Large Language Model2 Oct
  5. 2023DiLu: A Knowledge-Driven Approach to Autonomous Driving with Large Language Models28 Sep
  6. 2023Drive as You Speak: Enabling Human-Like Interaction with Large Language Models in Autonomous Vehicles19 Sep

Other references · 21

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  2. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  3. 2023GPT-4 Technical Reportcited by 18,396
  4. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 13,028
  5. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  6. 2023Visual Instruction Tuningcited by 7,869
  7. 2022Planning-oriented Autonomous Drivingcited by 1,087
  8. 2023One Fits All:Power General Time Series Analysis by Pretrained LMcited by 632
  9. 2024TinyLlama: An Open-Source Small Language Modelcited by 618
  10. 2023LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Modelcited by 602
  11. 2023DriveLM: Driving with Graph Visual Question Answeringcited by 536
  12. 2023GPT-Driver: Learning to Drive with GPTcited by 347
9 more
  1. 2024Automated Evaluation of Large Vision-Language Models on Self-driving Corner Casescited by 77
  2. 2024DME-Driver: Integrating Human Decision Logic and 3D Scene Perception in Autonomous Drivingcited by 43
  3. 2024PlanAgent: A Multi-modal Large Language Agent for Closed-loop Vehicle Motion Planningcited by 38
  4. 2024Fast and Slow Generating: An Empirical Study on Large and Small Language Models Collaborative Decodingcited by 13
  5. 2024Towards Robust and Efficient Cloud-Edge Elastic Model Adaptation via Selective Entropy Distillationcited by 11
  6. 2024Continual Learning with Diffusion-based Generative Replay for Industrial Streaming Datacited by 6
  7. 2024MSADM: Large Language Model (LLM) Assisted End-to-End Network Health Management Based on Multi-Scale Semanticizationcited by 4
  8. 2020Communication and Computing Resource Optimization for Connected Autonomous Drivingcited by 3
  9. 2023EdgeFD: An Edge-Friendly Drift-Aware Fault Diagnosis System for Industrial IoTcited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 4; 2026: 1.

5 papers.

  1. 2025Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Surveycited by 14
  2. 2025ReasonDrive: Efficient Visual Question Answering for Autonomous Vehicles with Reasoning-Enhanced Small Vision-Language Modelscited by 8
  3. 2025Generative AI for Autonomous Driving: Frontiers and Opportunitiescited by 7
  4. 2025Task-Oriented Semantic Communication in Large Multimodal Models-based Vehicle Networkscited by 5
  5. 2026Large Language Models in the Loop: A Stability- and Network-Aware Survey in Networked Control, Cyber-Physical, and Multi-Agent Systemscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Risk-Adaptive Edge--Cloud Visual Reasoning for Communication-Efficient Autonomous Driving
  2. 2024Efficient Driving Behavior Narration and Reasoning on Edge Device Using Large Language Models
  3. 2025FLAD: Federated Learning for LLM-based Autonomous Driving in Vehicle-Edge-Cloud Networks
  4. 2026SwarmDrive: Semantic V2V Coordination for Latency-Constrained Cooperative Autonomous Driving
  5. 2026Not All Uncertainty Matters: Simulation-in-the-Loop Fast-Slow Reasoning for Decision-Critical Autonomous Driving System
  6. 2025AdaDrive: Self-Adaptive Slow-Fast System for Language-Grounded Autonomous Driving
  7. 2024Testing Large Language Models on Driving Theory Knowledge and Skills for Connected Autonomous Vehicles
  8. 2025Towards Accurate and Efficient 3D Object Detection for Autonomous Driving: A Mixture of Experts Computing System on Edge