Large language models (LLMs) as autonomous agents offer a novel avenue for tackling real-world challenges through a knowledge-driven manner. These LLM-enhanced methodologies excel in generalization and interpretability. However, the complexity of driving tasks often necessitates the collaboration of multiple, heterogeneous agents, underscoring the need for such LLM-driven agents to engage in cooperative knowledge sharing and cognitive synergy. Despite the promise of LLMs, current applications predominantly center around single agent scenarios. To broaden the horizons of knowledge-driven strategies and bolster the generalization capabilities of autonomous agents, we propose the KoMA framework consisting of multi-agent interaction, multi-step planning, shared-memory, and ranking-based reflection modules to enhance multi-agents' decision-making in complex driving scenarios. Based on the framework's generated text descriptions of driving scenarios, the multi-agent interaction module enables LLM agents to analyze and infer the intentions of surrounding vehicles, akin to human cognition. The multi-step planning module enables LLM agents to analyze and obtain final action decisions layer by layer to ensure consistent goals for short-term action decisions. The shared memory module can accumulate collective experience to make superior decisions, and the ranking-based reflection module can evaluate and improve agent behavior with the aim of enhancing driving safety and efficiency. The KoMA framework not only enhances the robustness and adaptability of autonomous driving agents but also significantly elevates their generalization capabilities across diverse scenarios. Empirical results demonstrate the superiority of our approach over traditional methods, particularly in its ability to handle complex, unpredictable driving environments without extensive retraining.
LightEMMA: A Longitudinal Evaluation of Vision-Language Models for Autonomous Driving
Department of Civil and Environmental Engineering, University of Michigan（土木与环境工程系，密歇根大学）；University of Michigan Transportation Research Institute（密歇根大学交通研究所）
Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；East China Normal University（华东师范大学）；Chinese University of Hong Kong（香港中文大学）；Zhejiang University（浙江大学）
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。
Towards Benchmarking and Assessing the Safety and Robustness of Autonomous Driving on Safety-critical Scenarios
Zhongguancun Laboratory（中关村实验室）；Beihang University（北京航空航天大学）；Institute of Data Space, Hefei Comprehensive National Science Center（合肥综合性国家科学中心数据空间研究院）；Institute of Information Science, Beijing Jiaotong University（北京交通大学信息科学研究所）；A*STAR（新加坡科技研究局）
Attack End-to-End Autonomous Driving through Module-Wise Noise
School of Computer Science and Engineering, Beihang University（北京航空航天大学计算机科学与工程学院）；State Key Lab of Software Development Environment, Beihang University（北京航空航天大学软件开发环境国家重点实验室）；Shen Yuan Honors College, Beihang University（北京航空航天大学沈元荣誉学院）；School of Software, Beihang University（北京航空航天大学软件学院）
Generative AI-empowered Simulation for Autonomous Driving in Vehicular Mixed Reality Metaverses
Nanyang Technological University（南洋理工大学）；Peking University（北京大学）；Guangdong University of Technology（广东工业大学）；Singapore University of Technology and Design（新加坡科技设计大学）；Auburn University（奥本大学）；University of Houston（休斯顿大学）；Kyung Hee University（庆熙大学）
Autonomous Driving Small-Scale Cars: A Survey of Recent Development
Chair of Econometrics and Statistics, esp. in the Transport Sector, Technische Universität Dresden（经济计量学与统计学教授席位（侧重交通领域））；Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI) Dresden/Leipzig（可扩展数据分析与人工智能中心（ScaDS.AI）德累斯顿/莱比锡）；Barkhausen Institut gGmbH（巴克豪森研究所）
Teaching Autonomous Driving Using a Modular and Integrated Approach
South China University of Technology（华南理工大学）；PerceptIn Inc（PerceptIn公司）；University of Shanghai for Science and Technology（上海理工大学）；Université de Cergy-Pontoise（塞尔吉-蓬图瓦兹大学）；Clarkson University（克拉克森大学）；Wayne State University（韦恩州立大学）；University of California, Irvine（加利福尼亚大学欧文分校）
Motion Comfort Optimization for Autonomous Vehicles: Concepts, Methods, and Techniques
University of North Texas（北德克萨斯大学）；Fordham University（福特汉姆大学）；Qatar University（卡塔尔大学）；University of Nebraska at Kearney（内布拉斯加大学卡尼尔分校）；Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI)（穆罕默德·本·扎耶德人工智能大学（MBZUAI））；Hamad Bin Khalifa University（哈马德·本·哈利法大学）
World Models for Autonomous Driving: An Initial Survey
University of Macau（澳门大学）；College of Transportation Engineering, Tongji University（同济大学交通运输工程学院）；Tsinghua University（清华大学）；Macau University of Science and Technology（澳门科技大学）；University of Hawaii at Manoa（夏威夷大学马诺阿分校）
Enabling Level-4 Autonomous Driving on a Single $1k Off-the-Shelf Card
Perceptin LLC（Perceptin有限责任公司）；Northeastern Univ（东北大学）；COCOPLE LLC（COCOPLE有限责任公司）；College of William and Mary（威廉玛丽学院）；North Carolina State University（北卡罗来纳州立大学）
