Autonomous vehicle navigation in complex environments such as dense and fast-moving highways and merging scenarios remains an active area of research. A key limitation of RL is its reliance on well-specified reward functions, which often fail to capture the full semantic and social complexity of diverse, out-of-distribution situations. As a result, a rapidly growing line of research explores using Large Language Models (LLMs) to replace or supplement RL for direct planning and control, on account of their ability to reason about rich semantic context. However, LLMs present significant drawbacks: they can be unstable in zero-shot safety-critical settings, produce inconsistent outputs, and often depend on expensive API calls with network latency. This motivates our investigation into whether small, locally deployed LLMs (< 14B parameters) can meaningfully support autonomous highway driving through reward shaping rather than direct control. We present a case study comparing RL-only, LLM-only, and hybrid approaches, where LLMs augment RL rewards by scoring state-action transitions during training, while standard RL policies execute at test time. Our findings reveal that RL-only agents achieve moderate success rates (73-89%) with reasonable efficiency, LLM-only agents can reach higher success rates (up to 94%) but with severely degraded speed performance, and hybrid approaches consistently fall between these extremes. Critically, despite explicit efficiency instructions, LLM-influenced approaches exhibit systematic conservative bias with substantial model-dependent variability, highlighting important limitations of current small LLMs for safety-critical control tasks.
AdvDiffuser: Generating Adversarial Safety-Critical Driving Scenarios via Guided Diffusion
School of Computer Science and Engineering, Sun Yat-Sen University（中山大学计算机科学与工程学院）；School of Computer Science, Wuhan University（武汉大学计算机学院）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Mechanical and Automotive Engineering, Qingdao University of Technology（青岛理工大学机械与汽车工程学院）
LD-Scene: LLM-Guided Diffusion for Controllable Generation of Adversarial Safety-Critical Driving Scenarios
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；School of Computer Science and Engineering, Sun Yat-sen University（中山大学计算机科学与工程学院）
RAPiD: Reward-Guided Consistency Distillation of Diffusion Planners for Real-Time Autonomous Driving
School of Information Technology, Monash University, Malaysia（墨尔本大学信息科技学院）；Faculty of Information Technology, Monash University, Australia（墨尔本大学信息技术学院）；TCS Research, India（印度TCS研究）
Confidence-Guided Human-AI Collaboration: Reinforcement Learning with Distributional Proxy Value Propagation for Autonomous Driving
Tianjin Key Laboratory of Intelligent Unmanned Swarm Technology and System, School of Electrical and Information Engineering, Tianjin University（天津智能无人群技术与系统重点实验室，电气与信息工程学院，天津大学）；Key Laboratory of System Control and Information Processing, Ministry of Education of China（系统控制与信息处理重点实验室，中华人民共和国教育部）；School of Mechanical Engineering, Shanghai Jiao Tong University（机械工程学院，上海交通大学）
Nav-EE: Navigation-Guided Early Exiting for Efficient Vision-Language Models in Autonomous Driving
Department of Computer Science, City University of Hong Kong（香港城市大学计算机科学系）；Department of Computer Science, McGill University（麦吉尔大学计算机科学系）；Department of Computer Science, Mohamed bin Zayed University of Artificial Intelligence（马尔代夫穆罕默德· bin·扎耶德人工智能大学计算机科学系）
S3PT: Scene Semantics and Structure Guided Clustering to Boost Self-Supervised Pre-Training for Autonomous Driving
Qualcomm Technologies International GmbH（高通技术国际有限公司）；Arriver Software AB（Arriver软件公司）；Linköping University（林雪平大学）；Qualcomm SARL（高通法国公司）；Qualcomm Technologies, Inc（高通技术公司）；KTH Royal Institute of Technology（皇家理工学院（瑞典））
DriveFuzz: Discovering Autonomous Driving Bugs through Driving Quality-Guided Fuzzing
Georgia Institute of Technology（佐治亚理工学院）；University of Texas at Dallas（德克萨斯大学达拉斯分校）；University of Central Oklahoma（中央俄克拉荷马大学）；UNIST（蔚山国立科学技术院）；University of Virginia（弗吉尼亚大学）
VLM-SAFE: Vision-Language Model-Guided Safety-Aware Reinforcement Learning with World Models for Autonomous Driving
Lyles School of Civil and Construction Engineering, Purdue University（普渡大学莱尔斯土木与建筑工程学院）；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）；Google（谷歌）
From Attacks to Curricula: Learnability-Guided Adversarial Training for Safe Autonomous Driving
College of Transportation & Key Laboratory of Road and Traffic Engineering of Ministry of Education, Tongji University（同济大学交通运输工程学院 & 道路与交通工程教育部重点实验室）；Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University（香港理工大学土木与环境工程学系）
TSC: Topology-Conditioned Stackelberg Coordination for Multi-Agent Reinforcement Learning in Interactive Driving
State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences（多模态人工智能系统国家重点实验室，自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Department of Natural Sciences, University of Durham（达勒姆大学自然科学系）；College of Civil and Transportation Engineering, Shenzhen University（深圳大学土木与交通工程学院）；Chair of Robotics, Artificial Intelligence and Realtime Systems, Technical University of Munich（慕尼黑技术大学机器人、人工智能与实时系统教授职位）
COVLM-RL: Critical Object-Oriented Reasoning for Autonomous Driving Using VLM-Guided Reinforcement Learning
School of Mechanical and Aerospace Engineering, Nanyang Technological University（南洋理工大学机械与航空航天工程学院）；National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center for Visual Information and Applications, Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University（西安交通大学人机混合增强智能国家重点实验室、视觉信息与应用国家工程研究中心、人工智能与机器人研究院）
From Words to Collisions: LLM-Guided Evaluation and Adversarial Generation of Safety-Critical Driving Scenarios
Professorship of Autonomous Vehicle Systems, TUM School of Engineering and Design, Technical University of Munich（自主车辆系统教授职位，技术大学慕尼黑工程与设计学院）；Munich Institute of Robotics and Machine Intelligence (MIRMI)（慕尼黑机器人与机器智能研究所）；TUM School of Computation, Information and Technology, Department of Computer Engineering, Technical University of Munich（技术大学慕尼黑计算、信息与技术学院，计算机工程系）
Saliency-Guided Domain Adaptation for Left-Hand Driving in Autonomous Steering
Centre for Accident Research and Road Safety-Queensland, Queensland University of Technology（昆士兰事故研究与道路安全中心、昆士兰理工大学）；QUT Centre for Robotics and School of Electrical Engineering and Computer Science, Queensland University of Technology（昆士兰大学机器人中心及电气工程与计算机科学学院、昆士兰理工大学）
A-MADiff: Attention-Guided Multi-Agent DRL with Diffusion Policies for Memory-Aware Task Orchestration in Mobile AIGC Networks
School of Automation, Guangdong University of Technology（广东工业大学自动化学院）；City University of Hong Kong（香港城市大学）；School of Information and Communication, Guilin University of Electronic Technology（桂林电子科技大学信息与通信学院）；Anhui Engineering Research Center for Agricultural Product Quality Safety Digital Intelligence, Fuyang Normal University（阜阳师范大学安徽省农产品质量安全数字智能工程研究中心）；School of Physics and Electronic Engineering, Fuyang Normal University（阜阳师范大学物理与电子工程学院）；University of Manitoba（曼尼托巴大学）
CritiqueDriveVLM: From Verifier-Guided Reinforcement Learning to Latent Thought Distillation for Autonomous Driving
State Key Laboratory of Networking and Switching Technology（网络与交换技术国家重点实验室）；School of Digital Media & Design Arts（数字媒体与设计艺术学院）；Beijing University of Posts and Telecommunications（北京邮电大学）
