Augmenting Lane Perception and Topology Understanding with Standard
Definition Navigation Maps
- URL: http://arxiv.org/abs/2311.04079v1
- Date: Tue, 7 Nov 2023 15:42:22 GMT
- Title: Augmenting Lane Perception and Topology Understanding with Standard
Definition Navigation Maps
- Authors: Katie Z Luo, Xinshuo Weng, Yan Wang, Shuang Wu, Jie Li, Kilian Q
Weinberger, Yue Wang, Marco Pavone
- Abstract summary: Standard Definition (SD) maps are more affordable and have worldwide coverage, offering a scalable alternative.
We propose a novel framework to integrate SD maps into online map prediction and propose a Transformer-based encoder, SD Map Representations from transFormers.
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.
- Score: 51.24861159115138
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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