Enhancing Online Road Network Perception and Reasoning with Standard Definition Maps

TL;DR

This paper introduces the use of lightweight Standard Definition maps as priors to improve online HD map perception for autonomous driving, significantly enhancing speed and accuracy while reducing model complexity.

Contribution

It demonstrates that SD maps can be integrated into online mapping architectures as model-agnostic priors, boosting performance and efficiency in dynamic urban and highway environments.

Findings

SD maps accelerate convergence of online mapping models.

Performance of centerline perception improves by 30% (mAP) with SD maps.

Reduction in model parameters achieved while maintaining or improving accuracy.

Abstract

Autonomous driving for urban and highway driving applications often requires High Definition (HD) maps to generate a navigation plan. Nevertheless, various challenges arise when generating and maintaining HD maps at scale. While recent online mapping methods have started to emerge, their performance especially for longer ranges is limited by heavy occlusion in dynamic environments. With these considerations in mind, our work focuses on leveraging lightweight and scalable priors-Standard Definition (SD) maps-in the development of online vectorized HD map representations. We first examine the integration of prototypical rasterized SD map representations into various online mapping architectures. Furthermore, to identify lightweight strategies, we extend the OpenLane-V2 dataset with OpenStreetMaps and evaluate the benefits of graphical SD map representations. A key finding from designing SD map integration components is that SD map encoders are model agnostic and can be quickly adapted to new architectures that utilize bird's eye view (BEV) encoders. Our results show that making use of SD maps as priors for the online mapping task can significantly speed up convergence and boost the performance of the online centerline perception task by 30% (mAP). Furthermore, we show that the introduction of the SD maps leads to a reduction of the number of parameters in the perception and reasoning task by leveraging SD map graphs while improving the overall performance. Project Page: https://henryzhangzhy.github.io/sdhdmap/.