Result: Offline map updating and validation for autonomous driving using crowdsourced data

Autonomous driving promises safer and more comfortable transportation with less traffic congestion than human driving. Autonomous driving can be achieved using landmark-based maps, which allow for precise localization and collision-free path planning. Therefore, it is essential to keep the maps updated and validated. Traditional approaches towards map updating and validation often fail to robustly keep pace with environmental changes, causing localization errors. Current research addresses the map updating and validation problem using either graph-based methods or feature-based methods online, i.e. running while the vehicles are traversing the environment, which is computationally demanding and unscalable. In this paper, an offline map updating and validation framework is presented using crowdsourced data, which is abundantly available and ubiquitous. To integrate multiple observations and improve map accuracy and reliability, the framework couples data fusion techniques, including the density-based spatial clustering of applications with noise (DB-SCAN) algorithm, the K-D tree data structure, and Dempster-Shafer theory. The framework is validated through multiple test scenarios, including adding new landmarks and removing deleted ones. As a result, the map updating and validation framework effectively integrates crowdsourced data, enhancing the accuracy and reliability of map updating and validation. The findings highlight the potential of crowdsourced data to improve map validation processes in autonomous driving.