Autonomous Agents Modelling Other Agents: A Comprehensive Survey and Open Problems
The University of Edinburgh（爱丁堡大学）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）
Stefano V. Albrecht, Peter Stone
备注：
Final manuscript (46 pages), published in Artificial Intelligence Journal. The arXiv version also contains a table of contents after the abstract, but is otherwise identical to the AIJ version. Keywords: autonomous agents, multiagent systems, modelling other agents, opponent modelling
Much research in artificial intelligence is concerned with the development of autonomous agents that can interact effectively with other agents. An important aspect of such agents is the ability to reason about the behaviours of other agents, by constructing models which make predictions about various properties of interest (such as actions, goals, beliefs) of the modelled agents. A variety of modelling approaches now exist which vary widely in their methodology and underlying assumptions, catering to the needs of the different sub-communities within which they were developed and reflecting the different practical uses for which they are intended. The purpose of the present article is to provide a comprehensive survey of the salient modelling methods which can be found in the literature. The article concludes with a discussion of open problems which may form the basis for fruitful future research.
From LLM Reasoning to Autonomous AI Agents: A Comprehensive Review
Department of Computer and Network Engineering（计算机与网络工程系）；United Arab Emirates University（阿联酋大学）；Technology Innovation Institute（技术创新研究院）；Eötvös Loránd University（埃斯特哈齐·洛朗大学）；Research Institute for Digital Future（数字未来研究院）；Khalifa University（卡塔尔大学）
Autonomous computational catalysis through an agentic research system
Beijing Key Laboratory of Artificial Intelligence for Advanced Chemical Engineering Materials, State Key Laboratory of Chemical Engineering and Low- Carbon Technology, Department of Chemical Engineering, Tsinghua University（北京先进化工材料人工智能重点实验室、化学工程与低碳技术国家重点实验室、清华大学化学工程系）
Hierarchical generative modelling for autonomous robots
Intel Labs（英特尔实验室）；University College London（伦敦大学学院）；Wellcome Centre for Human Neuroimaging（惠康神经影像中心）；Queen Square Institute of Neurology（皇后广场神经学研究所）；UCL Robotics Institute（伦敦大学学院机器人研究所）
Assessing Drivers' Situation Awareness in Semi-Autonomous Vehicles: ASP based Characterisations of Driving Dynamics for Modelling Scene Interpretation and Projection
German Aerospace Center (DLR)（德国航空航天中心）；Institute for Systems Engineering for Future Mobility（未来出行系统工程研究所）
Emerging Threats in Deep Learning-Based Autonomous Driving: A Comprehensive Survey
Hubei University（湖北大学）
自2004年DARPA Grand Challenge以来，自动驾驶技术已经经历了近二十年的快速发展。特别是近年来，随着新型传感器和深度学习技术的应用向自动驾驶领域延伸，自动驾驶技术的发展不断取得突破。因此，许多汽车制造商和高科技巨头致力于自动驾驶的研究和系统开发。然而，作为自动驾驶的基础，深度学习技术面临着许多新的安全风险。学术界针对对抗性示例和AI后门提出了深度学习对策，并将其引入自动驾驶领域进行验证。深度学习安全关系到自动驾驶系统的安全，进而关系到人身安全，是一个值得关注和研究的问题，本文综述了自动驾驶中深度学习安全技术的概念、发展和最新研究。首先，我们简要介绍了自动驾驶系统中的深度学习框架和流水线，主要包括该领域常用的深度学习技术和算法。此外，我们还重点分析了基于深度学习的自动驾驶系统在各个功能层中的潜在安全威胁。本文回顾了自动驾驶领域深度学习攻击技术的发展历程，分析了当前自动驾驶领域的最新算法，揭示了其潜在的风险。最后，我们对自动驾驶领域的深度学习安全性进行了展望，并为构建安全可信的自动驾驶系统提出了建议。
3D Object Detection for Autonomous Driving: A Comprehensive Survey
The Chinese University of Hong Kong（香港中文大学）；Centre for Perceptual and Interactive Intelligence（感知与交互智能中心）；Max Planck Institute for Informatics（马克斯·普朗克信息学研究所）
Exploring Radar Data Representations in Autonomous Driving: A Comprehensive Review
School of Information Engineering, Yancheng Institute Technology（盐城理工学院信息工程学院）；Thrust of Artificial Intelligence and Thrust of Intelligent Transportation, The Hong Kong University of Science and Technology (Guangzhou)（香港科学与技术大学（广州）人工智能与智能交通研究所）；Faculty of Science and Engineering, University of Liverpool（利物浦大学科学与工程学院）；School of Advanced Technology, Xi’an Jiaotong-Liverpool University（西安交通大学利物浦大学高级技术学院）；School of Information Science and Technology, Nantong University（南通大学信息科学与技术学院）
Radar-Camera Fusion for Object Detection and Semantic Segmentation in Autonomous Driving: A Comprehensive Review
University of Liverpool（利物浦大学）；Xi’an Jiaotong-Liverpool University（西交利物浦大学）；Institute of Deep Perception Technology, JITRI（江苏省产业技术研究院深度感知技术研究所）；XJTLU-JITRI Academy of Industrial Technology（西交利物浦大学-江苏省产业技术研究院工业技术学院）
TaCarla: A comprehensive benchmarking dataset for end-to-end autonomous driving
Tuğrul Gorgülü *†（土耳其巴伊塞蒂大学）；Atakan Dağ †（土耳其巴伊塞蒂大学）；M. Esat Kalfaoğlu ‡（土耳其巴伊塞蒂大学）；Halil İbrahim Kuru †（土耳其巴伊塞蒂大学）；Barış Can Cam †（土耳其巴伊塞蒂大学）；Halil İbrahim Öztürk †（土耳其巴伊塞蒂大学）；Özsel Kılınç §（土耳其巴伊塞蒂大学）
V2X-QA: A Comprehensive Reasoning Dataset and Benchmark for Multimodal Large Language Models in Autonomous Driving Across Ego, Infrastructure, and Cooperative Views
Department of Civil and Environmental Engineering, University of Wisconsin–Madison（威斯康星大学麦迪逊分校土木与环境工程系）；Department of Civil and Architectural Engineering and Construction Management, University of Wyoming（怀俄明大学土木与建筑工程及施工管理系）；School of Transportation, Southeast University（东南大学交通学院）
Safety-Enhanced Autonomous Driving Using Interpretable Sensor Fusion Transformer
SenseTime Research（商汤科技研究院）；University of Toronto（多伦多大学）；The Chinese University of Hong Kong（香港中文大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
