Driving with LLMs: Fusing Object-Level Vector Modality for Explainable Autonomous Driving-Reference-Cited by-同舟云学术

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Driving with LLMs: Fusing Object-Level Vector Modality for Explainable Autonomous Driving

Published:2024-05-13 Issue: Volume:23 Page:14093-14100
ISSN:
Container-title:2024 IEEE International Conference on Robotics and Automation (ICRA)
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Author:

Chen Long¹,Sinavski Oleg¹,Hünermann Jan¹,Karnsund Alice¹,Willmott Andrew James¹,Birch Danny¹,Maund Daniel¹,Shotton Jamie¹

Affiliation:

1. Wayve

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10609961/10609862/10611018.pdf?arnumber=10611018

Reference50 articles.

1. Sparks of artificial general intelligence: Early experiments with gpt-4;Bubeck,2023

2. Explainable AI and Reinforcement Learning—A Systematic Review of Current Approaches and Trends

3. Robot representation and reasoning with knowledge from reinforcement learning;Lu,2018

4. Reimagining an autonomous vehicle;Hawke,2021

5. What data do we need for training an AV motion planner?

Cited by 5 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Large models for intelligent transportation systems and autonomous vehicles: A survey;Advanced Engineering Informatics;2024-10

2. DrPlanner: Diagnosis and Repair of Motion Planners for Automated Vehicles Using Large Language Models;IEEE Robotics and Automation Letters;2024-10

3. Using Multimodal Large Language Models (MLLMs) for Automated Detection of Traffic Safety-Critical Events;Vehicles;2024-09-02

4. A survey on integration of large language models with intelligent robots;Intelligent Service Robotics;2024-08-13

5. Talk2BEV: Language-enhanced Bird’s-eye View Maps for Autonomous Driving;2024 IEEE International Conference on Robotics and Automation (ICRA);2024-05-13

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