SVM增强型Frenet Frame Planner用于移动Agent间的安全导航
SVM Enhanced Frenet Frame Planner For Safe Navigation Amidst Moving Agents
摘要：
本文提出了一种支持向量机增强轨迹规划的动态场景，通常是那些在道路上遇到的设置。基于Frenet框架的轨迹生成在研究和工业中的自动驾驶背景下很受欢迎。我们采用了基于安全的最大保证金标准，使用SVM层，生成控制点，最大限度地从场景中的所有动态障碍物分离。然后，运动学一致的轨迹生成器计算通过这些航路点的路径。我们通过模拟以及在自动驾驶汽车上的真实世界实验展示了SVM增强规划器比常规的基于Frenet框架的轨迹生成提供了更大的动态障碍物偏移。因此，作者认为，这样的公式本质上适合于行人之间的导航。我们假设意图或轨迹预测模块的可用性，该模块预测场景中所有动态参与者的未来轨迹。
英文摘要：
This paper proposes an SVM Enhanced Trajectory Planner for dynamic scenes, typically those encountered in on road settings. Frenet frame based trajectory generation is popular in the context of autonomous driving both in research and industry. We incorporate a safety based maximal margin criteria using a SVM layer that generates control points that are maximally separated from all dynamic obstacles in the scene. A kinematically consistent trajectory generator then computes a path through these waypoints. We showcase through simulations as well as real world experiments on a self driving car that the SVM enhanced planner provides for a larger offset with dynamic obstacles than the regular Frenet frame based trajectory generation. Thereby, the authors argue that such a formulation is inherently suited for navigation amongst pedestrians. We assume the availability of an intent or trajectory prediction module that predicts the future trajectories of all dynamic actors in the scene.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
