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Autonomous driving has traditionally relied heavily on costly andlabor-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 theeffect of SD maps for real-time lane-topology understanding. We propose a novelframework to integrate SD maps into online map prediction and propose aTransformer-based encoder, SD Map Encoder Representations from transFormers, toleverage priors in SD maps for the lane-topology prediction task. Thisenhancement consistently and significantly boosts (by up to 60%) lane detectionand topology prediction on current state-of-the-art online map predictionmethods without bells and whistles and can be immediately incorporated into anyTransformer-based lane-topology method. Code is available athttps://github.com/NVlabs/SMERF.

Augmenting Lane Perception and Topology Understanding with Standard Definition Navigation Maps