arXiv · cs.AI · Jasmine Ratchford, Raffaele Romagnoli · 8 小时前模型评分35
光谱网络与共形预测：校准轨迹预测与控制点区间的方法
Spectral and Conformal: Neural Network Methods for Calibrated Trajectory Prediction and Control-Point Intervals
导读
研究提出一套结合光谱骨干神经网络与改进共形分位数回归的框架，为自主系统提供带初始条件和稳定性约束的轨迹及分位数带预测，并给出校准的轨迹级联合覆盖保证。该方法用区间算术把无噪声覆盖保证扩展到控制点，四旋翼在闭环线性二次调节器下的轨迹推理耗时 3.45 ms（Raspberry Pi Zero 2 W），含推理的完整流程耗时 9.81 ms，约 100 Hz，与典型控制频率相当。
分类置信度
中 · 待人工复核
- 存在未解决的相邻类别：embodied-ai，需要人工复核。
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来源级别：媒体与个人
事件类型：研究发布
证据摘录
- “This work demonstrates a framework that uses (1) a neural network with a spectral backbone to learn trajectories and quantile bands with built-in constraints (initial conditions and stability); (2) a modified conformal quantile regression to provide calibrated, joint trajectory-level coverage; and (3) interval arithmetic to extend noise-free coverage guarantees to control points.”
- “After refactoring the network for inference, the trajectory inference time for a quadrotor under a closed-loop linear quadratic regulator is 3.45~ms on a Raspberry Pi Zero 2 W.”
- “The total pipeline time, including trajectory inference time, is 9.81~ms, or approximately 100~Hz, consistent with typical control rates.”
来源：arXiv · cs.AI · arxiv.org