An Iterative Map-Trajectory Co-optimisation Framework Based on Map-Matching and Map Update

The digital map has long been suffering from low data quality issues caused by lengthy update period. Recent research on map inference/update shows the possibility of updating the map using vehicle trajectories. However, since trajectories are intrinsically inaccurate and sparse, the existing map correction methods are still inaccurate and incomplete. In this work, we propose an iterative map-trajectory co-optimisation framework that takes raw trajectories and the map as input and improves the quality of both datasets simultaneously. The map and map-matching qualities are quantified by our proposed measures. We also propose two scores to measure the credibility and influence of new road updates. Overall, our framework supports most of the existing map inference/update methods and can directly improve the quality of their updated map. We conduct extensive experiments on real-world datasets to demonstrate the effectiveness of our solution over other candidates.