EPAAMap: Enhanced Parallel Attention Aggregation HD Semantic Map in Complex Traffic Scenarios

This paper addresses the significant challenges posed by road safety due to rapid urbanization and increasing vehicular traffic. High-definition (HD) semantic maps are essential for improving decision-making and safety in autonomous vehicles and intelligent driver assistance systems, which require detailed road environment information. Current multi-camera perception systems face limitations in efficiently integrating data, resulting in inconsistent spatial understanding. To tackle these issues, this propose a novel multi-scale feature optimization mechanism EPPAMap, including the efficient depthwise separable feature network(EDSFN) to improve semantic understanding. Additionally, we introduce the parallel attention aggregation module (PAAM) to capture richer contextual information and enhance feature aggregation. Experimental results demonstrate superior adaptability and performance in complex road scenarios, providing a solid foundation for HD semantic map construction and real-time traffic perception.