Exploring Navigation Maps for Learning-Based Motion Prediction

• URL: http://arxiv.org/abs/2302.06195v1
• Date: Mon, 13 Feb 2023 09:06:27 GMT
• Title: Exploring Navigation Maps for Learning-Based Motion Prediction
• Authors: Julian Schmidt, Julian Jordan, Franz Gritschneder, Thomas Monninger, Klaus Dietmayer
• Abstract summary: We describe an approach to integrate navigation maps into learning-based motion prediction models. Our approach shows a significant improvement over not using a map at all. Our publicly available navigation map API for Argoverse enables researchers to develop and evaluate their own approaches using navigation maps.
• Score: 9.919575841909962
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The prediction of surrounding agents' motion is a key for safe autonomous driving. In this paper, we explore navigation maps as an alternative to the predominant High Definition (HD) maps for learning-based motion prediction. Navigation maps provide topological and geometrical information on road-level, HD maps additionally have centimeter-accurate lane-level information. As a result, HD maps are costly and time-consuming to obtain, while navigation maps with near-global coverage are freely available. We describe an approach to integrate navigation maps into learning-based motion prediction models. To exploit locally available HD maps during training, we additionally propose a model-agnostic method for knowledge distillation. In experiments on the publicly available Argoverse dataset with navigation maps obtained from OpenStreetMap, our approach shows a significant improvement over not using a map at all. Combined with our method for knowledge distillation, we achieve results that are close to the original HD map-reliant models. Our publicly available navigation map API for Argoverse enables researchers to develop and evaluate their own approaches using navigation maps.

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