Augmenting Lane Perception and Topology Understanding with Standard
  Definition Navigation Maps
Katie Z Luo Xinshuo Weng Yan Wang Shuang Wu Jie Li Kilian Q Weinberger Yue Wang Marco Pavone
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.