Paper Citation Record · LEDGER

Safety-Critical Learning for Long-Tail Events: The TUM Traffic Accident Dataset

As of 8 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 1 inbound Pith citation observation for arXiv:2508.14567.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2508.14567 v1

Coverage vector

measured 29 of 29 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T18:27:31.946410Z

measured 30 of 30 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-10T18:24:37.810179Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-11T00:41:03.190727Z

Reference resolution

29 of 29 outbound references displayed

verified exact4
verified fuzzy24
unresolved1
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 7c55d1d1-219f-4b47-b08f-f79af5dc59cb · outbound

This paper cites Planning with occluded traffic agents using bi-level variational occlu- sion models,.

Safety-Critical Learning for Long-Tail Events: The TUM Traffic Accident Dataset Planning with occluded traffic agents using bi-level variational occlu- sion models,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:27:36.532520Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation c0c8c0d2-be07-47ff-86db-ea4af042e05f · outbound

This paper cites Activeanno3d-an active learning framework for multi-modal 3d object detection,.

Safety-Critical Learning for Long-Tail Events: The TUM Traffic Accident Dataset Activeanno3d-an active learning framework for multi-modal 3d object detection,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:27:36.352375Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation ed42420f-4029-430e-91d6-31b377057be0 · outbound

This paper cites Create a large-scale video driving dataset with detailed attributes using amazon sagemaker ground truth,.

Safety-Critical Learning for Long-Tail Events: The TUM Traffic Accident Dataset Create a large-scale video driving dataset with detailed attributes using amazon sagemaker ground truth,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:27:36.274398Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 4a422b0f-6761-406e-b293-68cc736cfc79 · outbound

This paper cites Fingscheidt, H.

Safety-Critical Learning for Long-Tail Events: The TUM Traffic Accident Dataset Fingscheidt, H

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:27:36.156776Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 8f9ec8bb-4dde-4119-947d-afa607987d63 · outbound

This paper cites Drive video analysis for the detection of traffic near-miss incidents,.

Safety-Critical Learning for Long-Tail Events: The TUM Traffic Accident Dataset Drive video analysis for the detection of traffic near-miss incidents,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:27:35.954912Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 34e1aabc-449e-4233-a642-72a3072a4fcc · outbound

This paper cites Ips300+: a challenging multi-modal data sets for intersection per- ception system,.

Safety-Critical Learning for Long-Tail Events: The TUM Traffic Accident Dataset Ips300+: a challenging multi-modal data sets for intersection per- ception system,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:27:35.771426Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 2b6c7b24-24a9-4a6d-a776-76863d7609be · outbound

This paper cites The Why, When, and How to Use Active Learning in Large-Data-Driven 3D Object Detection for Safe Autonomous Driving: An Empirical Exploration.

Safety-Critical Learning for Long-Tail Events: The TUM Traffic Accident Dataset The Why, When, and How to Use Active Learning in Large-Data-Driven 3D Object Detection for Safe Autonomous Driving: An Empirical Exploration

Reference 7

Resolution: verified exact; local_arxiv, observed 2026-08-05T18:27:32.610750Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d788b683-b054-41f7-8828-69b42bbc783d · outbound

This paper cites GraphRelate3D: Context-Dependent 3D Object Detection with Inter-Object Relationship Graphs.

Safety-Critical Learning for Long-Tail Events: The TUM Traffic Accident Dataset GraphRelate3D: Context-Dependent 3D Object Detection with Inter-Object Relationship Graphs

Reference 8

Resolution: verified exact; local_arxiv, observed 2026-08-05T18:27:32.467804Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation dc6a64fb-16ff-49f8-b8f9-fe1ef960c621 · outbound

This paper cites Roadsense3d: A framework for roadside monocular 3d object detection,.

Safety-Critical Learning for Long-Tail Events: The TUM Traffic Accident Dataset Roadsense3d: A framework for roadside monocular 3d object detection,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:27:35.650530Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 2cff93dd-0af3-4b05-bc33-baa0b0c45768 · outbound

This paper cites Infradet3d: Multi-modal 3d object de- tection based on roadside infrastructure camera and lidar sensors,.

Safety-Critical Learning for Long-Tail Events: The TUM Traffic Accident Dataset Infradet3d: Multi-modal 3d object de- tection based on roadside infrastructure camera and lidar sensors,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:27:35.458497Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 1e427316-1d10-4282-bc7d-2dbcf2015da3 · outbound

This paper cites Real-time and robust 3d object detection with roadside lidars,.

Safety-Critical Learning for Long-Tail Events: The TUM Traffic Accident Dataset Real-time and robust 3d object detection with roadside lidars,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:27:35.279840Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation f74dcc17-2aa6-4d81-967e-d5b08cc1e17d · outbound

This paper cites A Survey of Robust 3D Object Detection Methods in Point Clouds.

Safety-Critical Learning for Long-Tail Events: The TUM Traffic Accident Dataset A Survey of Robust 3D Object Detection Methods in Point Clouds

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-08-05T18:27:32.276727Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 45e217c7-ad77-4dce-94b0-19efa0d2eece · outbound

This paper cites Real-Time and Robust 3D Object Detection Within Road-Side LiDARs Using Domain Adaptation.

Safety-Critical Learning for Long-Tail Events: The TUM Traffic Accident Dataset Real-Time and Robust 3D Object Detection Within Road-Side LiDARs Using Domain Adaptation

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-08-05T18:27:32.122091Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 07ababde-b430-4e85-b2db-123502654b01 · outbound

This paper cites Traffic light detection: A learning algorithm and evaluations on challenging dataset,.

Safety-Critical Learning for Long-Tail Events: The TUM Traffic Accident Dataset Traffic light detection: A learning algorithm and evaluations on challenging dataset,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:27:35.201069Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 76bd9898-71f6-4cc9-9e03-7001fcdd018e · outbound

This paper cites Laneaf: Robust multi-lane detection with affinity fields,.

Safety-Critical Learning for Long-Tail Events: The TUM Traffic Accident Dataset Laneaf: Robust multi-lane detection with affinity fields,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:27:35.046564Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 27f36a1e-d761-452b-af6f-d01cb2431742 · outbound

This paper cites Patterns of vehicle lights: Addressing complexities of camera-based vehicle light datasets and metrics,.

Safety-Critical Learning for Long-Tail Events: The TUM Traffic Accident Dataset Patterns of vehicle lights: Addressing complexities of camera-based vehicle light datasets and metrics,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:27:34.805399Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d4805a1b-877e-4da7-8185-8145a71374aa · outbound

This paper cites A digital twin for teleoper- ation of vehicles in urban environments,.

Safety-Critical Learning for Long-Tail Events: The TUM Traffic Accident Dataset A digital twin for teleoper- ation of vehicles in urban environments,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:27:34.659468Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 6abdbed0-4bdc-457e-bd7e-2c6151a95ee5 · outbound

This paper cites Safe control transitions: Machine vision based observable readiness index and data-driven takeover time prediction,.

Safety-Critical Learning for Long-Tail Events: The TUM Traffic Accident Dataset Safe control transitions: Machine vision based observable readiness index and data-driven takeover time prediction,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:27:34.484525Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 9c0f2475-6daf-4bf9-bb7f-a5d9669b4997 · outbound

This paper cites A survey on autonomous driving datasets: Statistics, annotation quality, and a future outlook,.

Safety-Critical Learning for Long-Tail Events: The TUM Traffic Accident Dataset A survey on autonomous driving datasets: Statistics, annotation quality, and a future outlook,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:27:34.359881Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 54a9baf2-5a2d-4a33-bd1a-704a184b6385 · outbound

This paper cites Deepaccident: A motion and accident prediction benchmark for v2x autonomous driving,.

Safety-Critical Learning for Long-Tail Events: The TUM Traffic Accident Dataset Deepaccident: A motion and accident prediction benchmark for v2x autonomous driving,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:27:34.247346Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 89f0973b-4705-4228-885f-258cc8d00582 · outbound

This paper cites A9-dataset: Multi-sensor infrastructure- based dataset for mobility research,.

Safety-Critical Learning for Long-Tail Events: The TUM Traffic Accident Dataset A9-dataset: Multi-sensor infrastructure- based dataset for mobility research,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:27:34.107241Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation a00ba48c-0de6-4ec2-86d4-28e203329184 · outbound

This paper cites Tumtraf intersection dataset: All you need for urban 3d camera-lidar roadside perception,.

Safety-Critical Learning for Long-Tail Events: The TUM Traffic Accident Dataset Tumtraf intersection dataset: All you need for urban 3d camera-lidar roadside perception,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:27:34.006370Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 68d8eba7-9d3d-473e-8651-97ad4b923611 · outbound

This paper cites Tumtraf event: Calibration and fusion resulting in a dataset for roadside event-based and rgb cameras,.

Safety-Critical Learning for Long-Tail Events: The TUM Traffic Accident Dataset Tumtraf event: Calibration and fusion resulting in a dataset for roadside event-based and rgb cameras,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:27:33.895916Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 864e751c-f375-4433-bc8c-e17197aee8f0 · outbound

This paper cites Tumtraf v2x cooperative perception dataset,.

Safety-Critical Learning for Long-Tail Events: The TUM Traffic Accident Dataset Tumtraf v2x cooperative perception dataset,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:27:33.742613Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation e15f50a6-296d-4ee2-a98c-65e42e106f7f · outbound

This paper cites 3d bat: A semi-automatic, web-based 3d annotation toolbox for full-surround, multi-modal data streams,.

Safety-Critical Learning for Long-Tail Events: The TUM Traffic Accident Dataset 3d bat: A semi-automatic, web-based 3d annotation toolbox for full-surround, multi-modal data streams,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:27:33.597384Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation ce7194af-bbcd-4960-97c5-af240cdaf074 · outbound

This paper cites Tum traffic datasets.

Safety-Critical Learning for Long-Tail Events: The TUM Traffic Accident Dataset Tum traffic datasets

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:27:33.492887Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 94c176d3-76e9-4a32-a1f0-de3a45d0f1d5 · outbound

This paper cites TUM traf- fic dataset development kit.

Safety-Critical Learning for Long-Tail Events: The TUM Traffic Accident Dataset TUM traf- fic dataset development kit

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:27:33.275851Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation fb49cc5e-3b68-4d8e-966e-a8a43cde0cff · outbound

This paper cites Towards Explainable, Safe Autonomous Driving with Language Embeddings for Novelty Identification and Active Learning: Framework and Experimental Analysis with Real-World Data Sets.

Safety-Critical Learning for Long-Tail Events: The TUM Traffic Accident Dataset Towards Explainable, Safe Autonomous Driving with Language Embeddings for Novelty Identification and Active Learning: Framework and Experimental Analysis with Real-World Data Sets

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-05T18:27:31.854242Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1b8fef97-13af-4464-8539-cf1102ce76ff · outbound

This paper cites Pedestrian behavior maps for safety advisories: Champ framework and real-world data analysis,.

Safety-Critical Learning for Long-Tail Events: The TUM Traffic Accident Dataset Pedestrian behavior maps for safety advisories: Champ framework and real-world data analysis,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:27:32.944671Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Pith citing papers

Observation 275ff94f-6d21-4ba7-a62c-306da9105ac6 · inbound

BIAS: A Biologically Inspired Algorithm for Video Saliency Detection cites this paper.

BIAS: A Biologically Inspired Algorithm for Video Saliency Detection Safety-Critical Learning for Long-Tail Events: The TUM Traffic Accident Dataset

Reference 72

Resolution: verified exact; arxiv_id, observed 2026-05-11T00:41:03.193800Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.