A Survey for Foundation Models in Autonomous Driving

• URL: http://arxiv.org/abs/2402.01105v1
• Date: Fri, 2 Feb 2024 02:44:59 GMT
• Title: A Survey for Foundation Models in Autonomous Driving
• Authors: Haoxiang Gao and Yaqian Li and Kaiwen Long and Ming Yang and Yiqing Shen
• Abstract summary: Large language models contribute to planning and simulation in autonomous driving. vision foundation models are increasingly adapted for critical tasks such as 3D object detection and tracking. Multi-modal foundation models, integrating diverse inputs, exhibit exceptional visual understanding and spatial reasoning.
• Score: 11.726604658478152
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The advent of foundation models has revolutionized the fields of natural language processing and computer vision, paving the way for their application in autonomous driving (AD). This survey presents a comprehensive review of more than 40 research papers, demonstrating the role of foundation models in enhancing AD. Large language models contribute to planning and simulation in AD, particularly through their proficiency in reasoning, code generation and translation. In parallel, vision foundation models are increasingly adapted for critical tasks such as 3D object detection and tracking, as well as creating realistic driving scenarios for simulation and testing. Multi-modal foundation models, integrating diverse inputs, exhibit exceptional visual understanding and spatial reasoning, crucial for end-to-end AD. This survey not only provides a structured taxonomy, categorizing foundation models based on their modalities and functionalities within the AD domain but also delves into the methods employed in current research. It identifies the gaps between existing foundation models and cutting-edge AD approaches, thereby charting future research directions and proposing a roadmap for bridging these gaps.

Related papers

• AI Foundation Models in Remote Sensing: A Survey [6.036426846159163]
  This paper provides a comprehensive survey of foundation models in the remote sensing domain. We categorize these models based on their applications in computer vision and domain-specific tasks. We highlight emerging trends and the significant advancements achieved by these foundation models.
  arXiv  Detail & Related papers  (2024-08-06T22:39:34Z)
• A Survey of Resource-efficient LLM and Multimodal Foundation Models [22.23967603206849]
  Large foundation models, including large language models (LLMs), vision transformers (ViTs), diffusion, and multimodal models, are revolutionizing the entire machine learning lifecycle. However, the substantial advancements in versatility and performance these models offer come at a significant cost in terms of hardware resources. This survey delves into the critical importance of such research, examining both algorithmic and systemic aspects.
  arXiv  Detail & Related papers  (2024-01-16T03:35:26Z)
• A Survey of Reasoning with Foundation Models [235.7288855108172]
  Reasoning plays a pivotal role in various real-world settings such as negotiation, medical diagnosis, and criminal investigation. We introduce seminal foundation models proposed or adaptable for reasoning. We then delve into the potential future directions behind the emergence of reasoning abilities within foundation models.
  arXiv  Detail & Related papers  (2023-12-17T15:16:13Z)
• Towards the Unification of Generative and Discriminative Visual Foundation Model: A Survey [30.528346074194925]
  Visual foundation models (VFMs) have become a catalyst for groundbreaking developments in computer vision. This review paper delineates the pivotal trajectories of VFMs, emphasizing their scalability and proficiency in generative tasks. A crucial direction for forthcoming innovation is the amalgamation of generative and discriminative paradigms.
  arXiv  Detail & Related papers  (2023-12-15T19:17:15Z)
• Foundational Models in Medical Imaging: A Comprehensive Survey and Future Vision [6.2847894163744105]
  Foundation models are large-scale, pre-trained deep-learning models adapted to a wide range of downstream tasks. These models facilitate contextual reasoning, generalization, and prompt capabilities at test time. Capitalizing on the advances in computer vision, medical imaging has also marked a growing interest in these models.
  arXiv  Detail & Related papers  (2023-10-28T12:08:12Z)
• Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook [95.32949323258251]
  Temporal data, notably time series andtemporal-temporal data, are prevalent in real-world applications. Recent advances in large language and other foundational models have spurred increased use in time series andtemporal data mining.
  arXiv  Detail & Related papers  (2023-10-16T09:06:00Z)
• A Systematic Survey in Geometric Deep Learning for Structure-based Drug Design [63.30166298698985]
  Structure-based drug design (SBDD) utilizes the three-dimensional geometry of proteins to identify potential drug candidates. Recent developments in geometric deep learning, focusing on the integration and processing of 3D geometric data, have greatly advanced the field of structure-based drug design.
  arXiv  Detail & Related papers  (2023-06-20T14:21:58Z)
• CHORUS: Foundation Models for Unified Data Discovery and Exploration [6.85448651843431]
  We show that foundation models are highly applicable to the data discovery and data exploration domain. We show that a foundation-model-based approach outperforms the task-specific models and so the state of the art. This suggests a future direction in which disparate data management tasks can be unified under foundation models.
  arXiv  Detail & Related papers  (2023-06-16T03:58:42Z)
• Foundation Models for Decision Making: Problems, Methods, and Opportunities [124.79381732197649]
  Foundation models pretrained on diverse data at scale have demonstrated extraordinary capabilities in a wide range of vision and language tasks. New paradigms are emerging for training foundation models to interact with other agents and perform long-term reasoning. Research at the intersection of foundation models and decision making holds tremendous promise for creating powerful new systems.
  arXiv  Detail & Related papers  (2023-03-07T18:44:07Z)
• Quantitatively Assessing the Benefits of Model-driven Development in Agent-based Modeling and Simulation [80.49040344355431]
  This paper compares the use of MDD and ABMS platforms in terms of effort and developer mistakes. The obtained results show that MDD4ABMS requires less effort to develop simulations with similar (sometimes better) design quality than NetLogo.
  arXiv  Detail & Related papers  (2020-06-15T23:29:04Z)
• A Comprehensive Study on Temporal Modeling for Online Action Detection [50.558313106389335]
  Online action detection (OAD) is a practical yet challenging task, which has attracted increasing attention in recent years. This paper aims to provide a comprehensive study on temporal modeling for OAD including four meta types of temporal modeling methods. We present several hybrid temporal modeling methods, which outperform the recent state-of-the-art methods with sizable margins on THUMOS-14 and TVSeries.
  arXiv  Detail & Related papers  (2020-01-21T13:12:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.