Ontology-Based Map Data Quality Assurance

A lane-level, high-definition (HD) digital map is needed for autonomous cars to provide safety and security to the passengers. However, it continues to prove very difficult to produce error-free maps. To avoid the deactivation of autonomous driving (AD) mode caused by map errors, ensuring map data quality is a crucial task. We propose an ontology-based workflow for HD map data quality assurance, including semantic enrichment, violation detection, and violation handling. Evaluations show that our approach can successfully check the quality of map data and suggests that violation handling is even feasible on-the-fly in the car (on-board), avoiding the autonomous driving mode's deactivation.