让自动驾驶赛车的定位误差随环境自适应且平滑变化
Environment-Aware Learning of Smooth GNSS Covariance Dynamics for Autonomous Racing
- 用神经网络根据环境特征预测GNSS误差动态变化
- 在复杂环境下定位精度提升，误差估计更平滑稳定
- 适合高动态控制场景中的状态估计算法设计
在高速自动驾驶竞速等安全关键领域，准确稳定的轨迹估计至关重要，此时测量不确定性需同时适应环境变化并保持时间上的平滑性。本文提出基于学习的LACE框架，直接建模GNSS测量协方差的时间动态特性。将协方差演化建模为指数稳定的动力系统，通过注意力机制使深度神经网络从环境特征中学习过程噪声。利用收缩性理论并施加谱约束，形式化保证了协方差动态的指数稳定性与平滑性。在AV-24自主赛车平台上验证表明，在挑战性、GNSS信号弱化的环境中，该方法显著提升了定位性能，并获得更平滑的协方差估计结果。研究凸显了在与控制敏感性紧密耦合的状态估计问题中，动态建模感知不确定性的潜力。
原文摘要 · Abstract (English)
Ensuring accurate and stable state estimation is a challenging task crucial to safety-critical domains such as high-speed autonomous racing, where measurement uncertainty must be both adaptive to the environment and temporally smooth for control. In this work, we develop a learning-based framework, LACE, capable of directly modeling the temporal dynamics of GNSS measurement covariance. We model the covariance evolution as an exponentially stable dynamical system where a deep neural network (DNN) learns to predict the system's process noise from environmental features through an attention mechanism. By using contraction-based stability and systematically imposing spectral constraints, we formally provide guarantees of exponential stability and smoothness for the resulting covariance dynamics. We validate our approach on an AV-24 autonomous racecar, demonstrating improved localization performance and smoother covariance estimates in challenging, GNSS-degraded environments. Our results highlight the promise of dynamically modeling the perceived uncertainty in state estimation problems that are tightly coupled with control sensitivity.
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