用径向基函数网络学习自动驾驶运动原语，速度快精度高
MP-RBFN: Learning-based Vehicle Motion Primitives using Radial Basis Function Networks
- 用RBF网络拟合最优控制生成的运动原语，兼顾精度与效率
- 相比传统方法，生成精度提升7倍，推理速度极快
- 适合实时自动驾驶规划，开源可用
本研究提出MP-RBFN，一种基于径向基函数网络的新方法，用于高效学习由最优控制问题导出的自动驾驶运动原语。传统优化类方法虽精度高但计算开销大，采样方法虽快速但对轨迹几何形状有限制。MP-RBFN结合两者优势，将采样方法的高保真轨迹生成与精确车辆动力学描述相融合。实验表明，相较于现有半解析方法，其生成优化运动原语的精度提高七倍，且推理时间极低。通过集成至采样式轨迹规划器，验证了MP-RBFN在实际运动规划中的可行性。相关代码已在GitHub开源：https://github.com/TUM-AVS/RBFN-Motion-Primitives。
原文摘要 · Abstract (English)
This research introduces MP-RBFN, a novel formulation leveraging Radial Basis Function Networks for efficiently learning Motion Primitives derived from optimal control problems for autonomous driving. While traditional motion planning approaches based on optimization are highly accurate, they are often computationally prohibitive. In contrast, sampling-based methods demonstrate high performance but impose constraints on the geometric shape of trajectories. MP-RBFN combines the strengths of both by coupling the high-fidelity trajectory generation of sampling-based methods with an accurate description of vehicle dynamics. Empirical results show compelling performance compared to previous methods, achieving a precise description of motion primitives at low inference times. MP-RBFN yields a seven times higher accuracy in generating optimized motion primitives compared to existing semi-analytic approaches. We demonstrate the practical applicability of MP-RBFN for motion planning by integrating the method into a sampling-based trajectory planner. MP-RBFN is available as open-source software at https://github.com/TUM-AVS/RBFN-Motion-Primitives.
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