Map Matching

Map matching aims to accurately align sensor data, such as GPS traces or point clouds, with a pre-existing map, overcoming inherent noise and uncertainties in the sensor readings. Current research emphasizes the use of deep learning models, particularly transformer networks and other sequence-to-sequence architectures, to improve the robustness and efficiency of map matching, especially for large-scale datasets and low-frequency data. These advancements are crucial for various applications, including autonomous navigation, traffic management, and urban planning, by enabling more precise and reliable location information extraction from diverse sensor modalities.