Autonomous driving paper index
Computational Goal Flow Dynamics (CGFD)
One-line summary
This paper introduces Computational Goal Flow Dynamics (CGFD), a novel framework for understanding and modeling complex systems where the driving force isn't a static goal, but rather a dynamic flow of goals.
Engineering notes
Key topics: autonomous driving. 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
This paper introduces Computational Goal Flow Dynamics (CGFD), a novel framework for understanding and modeling complex systems where the driving force isn't a static goal, but rather a dynamic flow of goals. CGFD posits that systems evolve through the formation, transfer, and dissipation of these goal-driven flows, represented mathematically as *G(t)*, where *t* denotes time. This approach offers a fundamentally different perspective compared to traditional goal-oriented modeling, potentially providing insights into the evolution of complex systems across diverse domains. The core of CGFD lies in developing computational methods to analyze and predict the behavior of *G(t)*, focusing on the dynamics governing its formation, transfer mechanisms, and eventual dissipation. This work lays the groundwork for a new methodology with implications for understanding autonomous systems and complex adaptive systems. ---
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