Automatic Map Generation for Autonomous Driving System Testing

TL;DR

This paper introduces FEAT2MAP, an automated method for generating diverse high-definition maps for testing autonomous driving systems, addressing limitations of current maps in diversity and cost.

Contribution

FEAT2MAP automatically creates concise, diverse HD maps focusing on junctions, improving scenario variety and reducing costs compared to existing methods.

Findings

Generated maps maintain scenario diversity with reduced size.

Merging features increases scenario diversity.

Maps can be customized with user-defined inputs.

Abstract

High-definition (HD) maps are essential in testing autonomous driving systems (ADSs). HD maps essentially determine the potential diversity of the testing scenarios. However, the current HD maps suffer from two main limitations: lack of junction diversity in the publicly available HD maps and cost-consuming to build a new HD map. Hence, in this paper, we propose, FEAT2MAP, to automatically generate concise HD maps with scenario diversity guarantees. FEAT2MAP focuses on junctions as they significantly influence scenario diversity, especially in urban road networks. FEAT2MAP first defines a set of features to characterize junctions. Then, FEAT2MAP extracts and samples concrete junction features from a list of input HD maps or user-defined requirements. Each junction feature generates a junction. Finally, FEAT2MAP builds a map by connecting the junctions in a grid layout. To demonstrate the effectiveness of FEAT2MAP, we conduct experiments with the public HD maps from SVL and the open-source ADS Apollo. The results show that FEAT2MAP can (1) generate new maps of reduced size while maintaining scenario diversity in terms of the code coverage and motion states of the ADS under test, and (2) generate new maps of increased scenario diversity by merging intersection features from multiple maps or taking user inputs.