A Novel of Improving the Accuracy of High-Definition Map for Autonomous Vehicle: A Realistic Case Study in Hanoi Area

This scientific paper presents a novel approach to enhance the accuracy of high-definition (HD) maps for autonomous vehicles, focusing on a realistic case study conducted in the Hanoi area. The study explores the significant impact of HD maps on autonomous vehicle operations, particularly on vehicle localization when constructing maps without proper data cleaning. The main objective of the research is to address the challenges arising from uncleaned data during HD map creation by utilizing advanced algorithms, such as Octree and Voxel, for data preprocessing before integrating it into the HD map building process. The study verifies the accuracy of the constructed HD maps through simulations and real-world scenarios, considering the complexities of urban environments like Hanoi. Additionally, the paper tests the constructed HD maps on actual autonomous vehicles and employs a matching index to compare localization accuracy with ground truth data. The comprehensive analysis reveals valuable insights into the importance of accurate HD maps for ensuring reliable and safe autonomous vehicle operation in dynamic urban settings. By employing state-of-the-art algorithms for data processing and conducting rigorous testing on real vehicles, this research contributes to the advancement of HD mapping technologies and their effective integration into autonomous vehicles’ navigation systems. The findings have practical implications for the successful deployment of autonomous vehicles in challenging urban environments, ultimately fostering the advancement of autonomous driving technology for future smart mobility solutions.