High-definition (HD) maps, with their accurate and detailed road information, have become a core component of autonomous vehicles. These maps help vehicles with environment perception, precise localization, and path planning. However, outdated maps can compromise vehicle safety, making map updates a key research area in intelligent driving technology. Traditional surveying methods are accurate but expensive, making them unsuitable for large-scale and frequent updates. Most existing crowdsourced map update methods focus on matching perception data with map features. However, they lack sufficient analysis of the reliability and uncertainty of perception results, making it difficult to ensure the accuracy of map updates. To address this, this paper proposes an HD map change detection method that considers the uncertainty of single-source perception results. This method extracts road feature information using onboard camera and Global Navigation Satellite System (GNSS) data and improves matching accuracy by combining geometric proximity and consistency. Additionally, a probability-based change detection method is introduced, which evaluates the reliability of map changes by integrating observations from multi-source vehicles. To validate the effectiveness of the proposed method, experiments were conducted on both simulation data and real-world road data, and the detection results of single-source data were compared with those of multi-source fused data. The experimental results indicate that the probabilistic estimation method proposed in this study effectively identifies the three typical scenarios of addition, deletion, and modification in HD map change detection. Additionally, the method achieves more than a 10% improvement in both precision and recall compared to single-source data.
Zhang et al. (Mon,) studied this question.