Huang et al., 2023 - Google Patents

Applications of large scale foundation models for autonomous driving

Huang et al., 2023

View PDF
Document ID
13349486374506067018
Author
Huang Y
Chen Y
Li Z
Publication year
Publication venue
arXiv preprint arXiv:2311.12144

External Links

Snippet

Since DARPA Grand Challenges (rural) in 2004/05 and Urban Challenges in 2007, autonomous driving has been the most active field of AI applications. Recently powered by large language models (LLMs), chat systems, such as chatGPT and PaLM, emerge and …
Continue reading at arxiv.org (PDF) (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/50Computer-aided design
    • G06F17/5009Computer-aided design using simulation
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30Information retrieval; Database structures therefor; File system structures therefor
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/20Handling natural language data
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00Subject matter not provided for in other groups of this subclass
    • G06N99/005Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T17/00Three dimensional [3D] modelling, e.g. data description of 3D objects
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00Computer systems utilising knowledge based models
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00Image analysis
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F3/00Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F9/00Arrangements for programme control, e.g. control unit
    • G06F9/06Arrangements for programme control, e.g. control unit using stored programme, i.e. using internal store of processing equipment to receive and retain programme

Similar Documents

Publication Publication Date Title
Chen et al. End-to-end autonomous driving: Challenges and frontiers
Huang et al. Autonomous driving with deep learning: A survey of state-of-art technologies
Jaegle et al. Perceiver io: A general architecture for structured inputs & outputs
Firoozi et al. Foundation models in robotics: Applications, challenges, and the future
Miao et al. Parallel learning: Overview and perspective for computational learning across Syn2Real and Sim2Real
Huang et al. Applications of large scale foundation models for autonomous driving
Qi et al. Shapellm: Universal 3d object understanding for embodied interaction
Chen et al. Deep learning for visual localization and mapping: A survey
Paz et al. Tridentnet: A conditional generative model for dynamic trajectory generation
Wu et al. Vision-language navigation: a survey and taxonomy
Kuo et al. Trajectory prediction with linguistic representations
Li et al. Towards knowledge-driven autonomous driving
Kadambi et al. Incorporating physics into data-driven computer vision
Joo et al. A realtime autonomous robot navigation framework for human like high-level interaction and task planning in global dynamic environment
Chen et al. Data-driven Traffic Simulation: A Comprehensive Review
Ma et al. When LLMs step into the 3D World: A Survey and Meta-Analysis of 3D Tasks via Multi-modal Large Language Models
Wang et al. NeRF in Robotics: A Survey
Luo et al. Delving into Multi-modal Multi-task Foundation Models for Road Scene Understanding: From Learning Paradigm Perspectives
Ming et al. Benchmarking neural radiance fields for autonomous robots: An overview
Mai et al. From Efficient Multimodal Models to World Models: A Survey
Wulfmeier On machine learning and structure for mobile robots
Gao et al. Vision-Language Navigation with Embodied Intelligence: A Survey
Huang et al. An Overview about Emerging Technologies of Autonomous Driving
Yuan et al. A survey of recent 3D scene analysis and processing methods
Luo et al. Transformer-based vision-language alignment for robot navigation and question answering