Reliable Online Method

Reliable online methods are crucial for various applications, aiming to improve the accuracy and robustness of estimations and predictions in dynamic environments. Current research focuses on developing algorithms that enhance repeatability and reliability, addressing challenges like uncertainty calibration in deep learning models and mitigating the effects of noisy or incomplete data, often employing techniques like importance sampling and leveraging diverse sensor fusion strategies (e.g., camera-radar). These advancements are significant for improving the trustworthiness of machine learning systems and enabling safer and more efficient operation in robotics, autonomous driving, and other real-world applications.