Accurate localization is essential for autonomous driving, but GNSS-based methods struggle in challenging environments such as urban canyons. Cross-view pose optimization offers an effective solution for localization by directly estimating vehicle pose using satellite-view images. However, existing methods primarily rely on cross-view features at a given pose, neglecting fine-grained contexts for precision and global contexts for robustness against large initial pose errors. To overcome these limitations, we propose PIDLoc, a novel cross-view pose optimization approach inspired by the proportional-integral-derivative (PID) controller. Using RGB images and LiDAR data, the PIDLoc models cross-view feature relationships through the PID branches and estimates pose via the spatially aware pose estimator (SPE). To enhance localization accuracy, the PID branches leverage feature differences for local context (P), aggregated feature differences for global context (I), and gradients of feature differences for fine-grained context (D). Integrated with the PID branches, the SPE captures spatial relationships within the PID-branch features for consistent localization. Experimental results demonstrate that the PIDLoc achieves state-of-the-art performance in cross-view pose estimation for the KITTI dataset, reducing position error by 37.8% compared with the previous state-of-the-art. Our code is available at https://github.com/url-kaist/PIDLoc

In time-series forecasting, signals such as traffic volume collected in the real world are noisy and irregularly sampled due to sensor malfunctions, so it is difficult to make accurate prediction. To resolve such difficulty, downsampling can be used to reduce noise and allow capturing slow trend of signals. In addition, upsampling can fill the missing data of irregularly sampled signals to catch fine details. Although extracting multi-resolution temporal features such as down or upsampling can improve prediction accuracy, the existing time-series forecasting approaches have used the original and/or downsampled signals only, so they cannot detect fine details of upsampled one. Moreover, these methods merge multi-resolution inputs without carefully concern to chronological order of time-series, which is very important in the time-series. To overcome this challenge, we propose a framework that can fully utilize multi-resolution time-series signals in up, original, and downscale, and sequentially aggregate them, named multi-resolution sequence aggregation and model-agnostic (MAMA) framework. Note that i) MAMA aggregates the multi-resolution signals without breaking its sequential characteristics, whose effectiveness was verified by the experiment results, and ii) it can adopt any existing forecasting algorithms. From experiments with the real-world datasets, it was observed that the prediction accuracy of the well-known forecasting models (i.e., LSTNet, TCN, and Informer) were improved by 11.5% on average when the proposed architecture is used. In ablation study, we showed that a performance improvement of 1.5% was achieved with the help of sequential aggregation module.