Augmenting Lane Perception and Topology Understanding with Standard Definition Navigation Maps
This work addresses the problem of scalable autonomous driving for the industry by offering an incremental improvement that enhances existing methods with SD maps.
The paper tackles the scalability issue of autonomous driving by using affordable Standard Definition (SD) maps instead of costly High Definition maps, resulting in up to a 60% boost in lane detection and topology prediction performance.
Autonomous driving has traditionally relied heavily on costly and labor-intensive High Definition (HD) maps, hindering scalability. In contrast, Standard Definition (SD) maps are more affordable and have worldwide coverage, offering a scalable alternative. In this work, we systematically explore the effect of SD maps for real-time lane-topology understanding. We propose a novel framework to integrate SD maps into online map prediction and propose a Transformer-based encoder, SD Map Encoder Representations from transFormers, to leverage priors in SD maps for the lane-topology prediction task. This enhancement consistently and significantly boosts (by up to 60%) lane detection and topology prediction on current state-of-the-art online map prediction methods without bells and whistles and can be immediately incorporated into any Transformer-based lane-topology method. Code is available at https://github.com/NVlabs/SMERF.