Error code:   JWTInvalidSignature
Exception:    InvalidSignatureError
Message:      Signature verification failed
Traceback:    Traceback (most recent call last):
                File "/src/libs/libapi/src/libapi/jwt_token.py", line 286, in validate_jwt
                  decoded = jwt.decode(
                      jwt=token,
                  ...<2 lines>...
                      options=options,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 368, in decode
                  decoded = self.decode_complete(
                      jwt,
                  ...<8 lines>...
                      leeway=leeway,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 265, in decode_complete
                  decoded = self._jws.decode_complete(
                      jwt,
                  ...<3 lines>...
                      detached_payload=detached_payload,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 270, in decode_complete
                  self._verify_signature(
                  ~~~~~~~~~~~~~~~~~~~~~~^
                      signing_input,
                      ^^^^^^^^^^^^^^
                  ...<4 lines>...
                      options=merged_options,
                      ^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 417, in _verify_signature
                  raise InvalidSignatureError("Signature verification failed")
              jwt.exceptions.InvalidSignatureError: Signature verification failed

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Cross-View Urban Traffic Dataset

Dataset Summary

The Cross-View Urban Traffic Dataset (CVUTD) is a benchmark for cross-view urban traffic perception built from synchronized ego-centric bicycle videos and aerial drone videos recorded at real urban intersections in Regensburg, Germany.

The dataset is designed to support two linked tasks:

Cross-view identity matching between street-view and drone-view object tracks
Ego-to-BEV prediction using aerial supervision

The benchmark focuses on intersection-centric traffic analysis, where local interactions, identity preservation, and global spatial structure must be reasoned about jointly across views.

Supported Tasks and Leaderboards

Task 1: Cross-view identity matching

Given synchronized street-view and drone-view object tracks, predict which street-view track corresponds to which drone-view track.

Typical metrics:

Track Precision / Recall / F1
ID Precision / Recall / IDF1
Frame assignment accuracy
Near/Far breakdown
Stability
ID switches
Consistency

Task 2: Ego-to-BEV prediction

Given an ego-centric street-view image or sequence, predict the spatial arrangement of traffic participants in a shared bird’s-eye-view frame.

Typical metrics:

ADE
FDE
ALE
ALgE
PCK@1m / PCK@2m
mIoU / IoU@thresholds

Languages

This dataset is primarily visual and geometric. Language metadata is included only for the English documentation and labels.

Dataset Structure

A typical scene contains:

synchronized street-view video
synchronized drone-view video
street-view detection/tracking CSV
drone-view detection/tracking CSV
verified cross-view correspondences
processed wedge-filtered matching artifacts
alignment metadata for BEV evaluation

Example structure:


CrossViewUrbanTrafficDataset/

  README.md

  LICENSE

  scene_manifest.csv (found on github)

  scenes/

    scene_01_01/

      street_video.mp4

      drone_video.mp4

      street_detections.csv

      drone_detections.csv

      gt_pairs.csv

      gt_audit.csv

      outputs/

        wedge_export/

          street_wedge_manifest.csv

          drone_wedge_manifest.csv

          frame_matches.csv

          track_mapping.csv

Citation

If you use this dataset in your research, please cite:

@misc{bhardwaj2026crossviewurbantrafficdataset,
      title={Cross-View Urban Traffic Dataset: Drone-Supervised Ground Truth for Monocular Bird's-Eye View Localization}, 
      author={Prakhar Bhardwaj and Simone Weikl and Kilian Mang and Elia Jonas Sandtner},
      year={2026},
      eprint={2606.07708},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2606.07708}, 
}

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Paper for prakharbh/CrossViewUrbanTrafficDataset

Cross-View Urban Traffic Dataset: Drone-Supervised Ground Truth for Monocular Bird's-Eye View Localization

Paper • 2606.07708 • Published Jun 5