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TopoStreamer is an end-to-end temporal perception model for lane segment topology inference. To address the limitations of consistent position embedding and temporal multi-attribute learning in existing methods, we introduce three major improvements: streaming attribute constraints, dynamic lane boundary position encoding, and lane segment denoising. Streaming attribute constraints enhance temporal consistency in both centerline and boundary coordinates and their classifications, dynamic lane boundary position encoding improves the learning of up-to-date position information in queries, and lane segment denoising captures various lane segment patterns to improve model performance. On the OpenLane-V2 dataset, we achieve +3.0% mAP in lane segment recognition and +1.7% OLS in centerline recognition compared to SOTA models. We evaluate the accuracy of existing models using the lane boundary classification metric, which is an important metric for lane-changing scenarios in autonomous driving.
Takeaways, Limitations
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Takeaways:
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We present TopoStreamer, an effective end-to-end temporal perception model for lane segment phase inference.
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Overcoming the limitations of existing methods through streaming property constraints, dynamic lane boundary location encoding, and lane segment noise removal.
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Achieving SOTA performance on OpenLane-V2 dataset.
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Performance evaluation using lane boundary classification metrics important for lane change scenarios.
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Limitations:
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Specific __T7635_____ is not explicitly mentioned in the paper. Additional datasets or validation of performance in more complex driving environments may be required.
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Analysis of the relative contributions of the proposed improvements may be lacking.
