Divide and Conquer: A Systematic Approach for Industrial Scale High-Definition OpenDRIVE Generation from Sparse Point Clouds

Abstract

High-definition road maps play a crucial role in the functionality and verification of highly automated driving functions. These contain precise information about the road network, geometry, condition, as well as traffic signs. Despite their importance for the development and evaluation of driving functions, the generation of high-definition maps is still an ongoing research topic. While previous work in this area has primarily focused on the accuracy of road geometry, we present a novel approach for automated large-scale map generation for use in industrial applications. Our proposed method leverages a minimal number of external information about the road to process LiDAR data in segments. These segments are subsequently combined, enabling a flexible and scalable process that achieves high-definition accuracy. Additionally, we showcase the use of the resulting OpenDRIVE in driving function simulation.

arXiv comment8 pages, 5 figures, 1 table. arXiv admin note: text overlap with arXiv:2405.07544

References in corpus (5)

• High-Definition Map Generation Technologies For Autonomous Driving
• SuperFusion: Multilevel LiDAR-Camera Fusion for Long-Range HD Map Generation
• An Approach to Systematic Data Acquisition and Data-Driven Simulation for the Safety Testing of Automated Driving Functions
• Automatic lane change scenario extraction and generation of scenarios in OpenX format from real-world data
• Automatic Odometry-Less OpenDRIVE Generation From Sparse Point Clouds