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About this paper
Huilin Yin, Yiming Kan, Daniel Watzenig
A new end-to-end autonomous driving framework, MAP, systematically integrates semantic map features with ego-vehicle status for trajectory planning, achieving top performance in a V2X cooperation challenge and reducing L2 displacement error by 16.6% and off-road rate by 56.2% on the DAIR-V2X-seq-SPD dataset.
Key insights
- Directly leveraging semantic map features within an end-to-end trajectory planning framework significantly enhances planning accuracy and robustness, challenging the view of mapping as a merely auxiliary component.
- A simpler architectural design, avoiding complex multi-module dependencies like heavy tracking or motion prediction, can achieve state-of-the-art performance with increased efficiency.
- Dynamically fusing complementary information streams (map-guided context and ego-status-guided planning) through a learnable adapter provides a flexible and effective way to integrate diverse cues for improved decision-making.
Summary by alphaXiv.
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