Autonomous driving paper index
Agentic AI: Vision and challenges
One-line summary
Agentic AI systems are increasingly viewed as a viable response to the shortcomings of static, rigid, and human-in-the-loop Artificial Intelligence (AI) systems.
Engineering notes
Key topics: autonomous driving, large language model. See the paper for implementation details and experimental results.
Chinese explanation / 中文解读
中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。
Original abstract
Agentic AI systems are increasingly viewed as a viable response to the shortcomings of static, rigid, and human-in-the-loop Artificial Intelligence (AI) systems. This is because autonomous operation enables rapid adaptation to dynamic, complex problems with improved time-critical behaviour under real-world constraints. Despite significant progress, current agentic pipelines are still challenged by output instability, scalability gaps, and system integration issues. Addressing these limitations, this article presents a comprehensive conceptual framework unifying core AI functionality with implementation approaches across different system scales, including Agentic AI builds upon Large Language Models (LLMs). Furthermore, the popular applications of Agentic AI and areas for future investigation and open problems are systematically presented.
Links and sources
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