Drive Anywhere: Generalizable End-to-end Autonomous Driving with Multi-modal Foundation Models-Reference-Cited by-同舟云学术

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Drive Anywhere: Generalizable End-to-end Autonomous Driving with Multi-modal Foundation Models

Published:2024-05-13 Issue: Volume:1 Page:6687-6694
ISSN:
Container-title:2024 IEEE International Conference on Robotics and Automation (ICRA)
language:
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Author:

Wang Tsun-Hsuan¹,Maalouf Alaa¹,Xiao Wei¹,Ban Yutong²,Amini Alexander¹,Rosman Guy³,Karaman Sertac⁴,Rus Daniela¹

Affiliation:

1. MIT CSAIL

2. Shanghai Jiao Tong University

3. TRI

4. MIT LIDS

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10609961/10609862/10611590.pdf?arnumber=10611590

Reference51 articles.

1. Learning transferable visual models from natural language supervision;Radford

2. Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models;Li,2023

3. ConceptFusion: Open-set multimodal 3D mapping

4. OpenScene: 3D Scene Understanding with Open Vocabularies

5. Alvinn: An autonomous land vehicle in a neural network;Pomerleau;Advances in neural information processing systems,1988

Cited by 2 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. An Object-Centric Hierarchical Pose Estimation Method Using Semantic High-Definition Maps for General Autonomous Driving;Sensors;2024-08-11

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