Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-09T19:11:16.964655Z
Paper Citation Record · LEDGER
Enhancing Highway Safety: Accident Detection on the A9 Test Stretch Using Roadside Sensors
As of 10 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 0 inbound Pith citation observations for arXiv:2502.00402.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
46 of 46 outbound references displayed
- verified exact0
- verified fuzzy37
- unresolved9
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 480d731b-4763-4631-a514-905b27b947f8 · outbound
This paper cites Planning with occluded traffic agents using bi-level variational occlusion models,.
Enhancing Highway Safety: Accident Detection on the A9 Test Stretch Using Roadside Sensors Planning with occluded traffic agents using bi-level variational occlusion models,
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T19:11:17.425119Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 16f2a95c-d276-4abb-ba7a-0f58658864d8 · outbound
This paper cites Activeanno3d-an active learning framework for multi-modal 3d object detection,.
Enhancing Highway Safety: Accident Detection on the A9 Test Stretch Using Roadside Sensors Activeanno3d-an active learning framework for multi-modal 3d object detection,
Reference 2
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T19:11:16.833249Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c4ce54a0-9e98-4b04-abcd-dc7522affd3a · outbound
This paper cites Create a large-scale video driving dataset with detailed attributes using amazon sagemaker ground truth,.
Enhancing Highway Safety: Accident Detection on the A9 Test Stretch Using Roadside Sensors Create a large-scale video driving dataset with detailed attributes using amazon sagemaker ground truth,
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T19:11:17.411169Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation bc6889df-9177-430c-8c61-0945b868b936 · outbound
This paper cites Fingscheidt, H.
Enhancing Highway Safety: Accident Detection on the A9 Test Stretch Using Roadside Sensors Fingscheidt, H
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T19:11:17.401837Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation cf313086-c66d-41d5-b84b-1cd02b615094 · outbound
This paper cites Ips300+: a challenging multi-modal data sets for intersection perception system,.
Enhancing Highway Safety: Accident Detection on the A9 Test Stretch Using Roadside Sensors Ips300+: a challenging multi-modal data sets for intersection perception system,
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T19:11:17.392392Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation d1b47696-314e-49fa-80a8-638cf7a709ad · 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.
Enhancing Highway Safety: Accident Detection on the A9 Test Stretch Using Roadside Sensors The Why, When, and How to Use Active Learning in Large-Data-Driven 3D Object Detection for Safe Autonomous Driving: An Empirical Exploration
Reference 6
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T19:11:16.847263Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6da2b26e-8473-4df2-b61d-9930453f00d3 · outbound
This paper cites GraphRelate3D: Context-Dependent 3D Object Detection with Inter-Object Relationship Graphs.
Enhancing Highway Safety: Accident Detection on the A9 Test Stretch Using Roadside Sensors GraphRelate3D: Context-Dependent 3D Object Detection with Inter-Object Relationship Graphs
Reference 7
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T19:11:16.850851Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a9dbe350-9062-433e-8aae-dfbd3258f3d8 · outbound
This paper cites Roadsense3d: A framework for roadside monocular 3d object detection,.
Enhancing Highway Safety: Accident Detection on the A9 Test Stretch Using Roadside Sensors Roadsense3d: A framework for roadside monocular 3d object detection,
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T19:11:17.382901Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation c94cc171-b25d-4e22-9d40-6f24495b73f3 · outbound
This paper cites Infradet3d: Multi-modal 3d object detection based on roadside infrastructure camera and lidar sensors,.
Enhancing Highway Safety: Accident Detection on the A9 Test Stretch Using Roadside Sensors Infradet3d: Multi-modal 3d object detection based on roadside infrastructure camera and lidar sensors,
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T19:11:17.373628Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 6bc9ea9d-23c5-4aeb-8e9f-0525ac112fcb · outbound
This paper cites Real-time and robust 3d object detection with roadside lidars,.
Enhancing Highway Safety: Accident Detection on the A9 Test Stretch Using Roadside Sensors Real-time and robust 3d object detection with roadside lidars,
Reference 10
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T19:11:16.859137Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 60768fdc-163f-465c-bee8-14d6d528e0e1 · outbound
This paper cites A Survey of Robust 3D Object Detection Methods in Point Clouds.
Enhancing Highway Safety: Accident Detection on the A9 Test Stretch Using Roadside Sensors A Survey of Robust 3D Object Detection Methods in Point Clouds
Reference 11
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T19:11:16.861349Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ff44083b-2914-4469-b5e7-e8f2b3cc8a92 · outbound
This paper cites Real-Time and Robust 3D Object Detection Within Road-Side LiDARs Using Domain Adaptation.
Enhancing Highway Safety: Accident Detection on the A9 Test Stretch Using Roadside Sensors Real-Time and Robust 3D Object Detection Within Road-Side LiDARs Using Domain Adaptation
Reference 12
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T19:11:16.863821Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 45cde37a-464c-4f63-a591-608567736289 · outbound
This paper cites Traffic light detection: A learning algorithm and evaluations on challenging dataset,.
Enhancing Highway Safety: Accident Detection on the A9 Test Stretch Using Roadside Sensors Traffic light detection: A learning algorithm and evaluations on challenging dataset,
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T19:11:17.360105Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation e51dea73-bc21-4755-9f99-3c2fcf2cdd7b · outbound
This paper cites Laneaf: Robust multi-lane detection with affinity fields,.
Enhancing Highway Safety: Accident Detection on the A9 Test Stretch Using Roadside Sensors Laneaf: Robust multi-lane detection with affinity fields,
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T19:11:17.349817Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation d9115b92-511d-47b0-a486-c8cfe6b479d4 · outbound
This paper cites PointCompress3d – a point cloud compression framework for roadside LiDARs in intelligent transportation systems.
Enhancing Highway Safety: Accident Detection on the A9 Test Stretch Using Roadside Sensors PointCompress3d – a point cloud compression framework for roadside LiDARs in intelligent transportation systems
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T19:11:17.341609Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation ae73bfea-bba8-4dd2-9c10-a18012ef9796 · outbound
This paper cites GraphRelate3d: Context-dependent 3d object detection with inter- object relationship graphs.
Enhancing Highway Safety: Accident Detection on the A9 Test Stretch Using Roadside Sensors GraphRelate3d: Context-dependent 3d object detection with inter- object relationship graphs
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T19:11:17.332523Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 33528d2a-ad36-44b3-91a0-1855bc7e903c · outbound
This paper cites Transfer learning from simulated to real scenes for monocular 3d object detection.
Enhancing Highway Safety: Accident Detection on the A9 Test Stretch Using Roadside Sensors Transfer learning from simulated to real scenes for monocular 3d object detection
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T19:11:17.325136Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation e957848c-34b5-496e-a0d9-2ad9ce6327a3 · outbound
This paper cites Patterns of vehicle lights: Addressing complexities of camera-based vehicle light datasets and metrics,.
Enhancing Highway Safety: Accident Detection on the A9 Test Stretch Using Roadside Sensors Patterns of vehicle lights: Addressing complexities of camera-based vehicle light datasets and metrics,
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T19:11:17.317314Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 2f5bb6cd-5939-46bb-b5fe-2caa8d29e087 · outbound
This paper cites Collaborative semantic occupancy prediction with hybrid feature fusion in connected automated vehicles,.
Enhancing Highway Safety: Accident Detection on the A9 Test Stretch Using Roadside Sensors Collaborative semantic occupancy prediction with hybrid feature fusion in connected automated vehicles,
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T19:11:17.308710Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 02306438-39a4-43a9-85ca-30e1ceddaf0a · outbound
This paper cites A digital twin for teleoperation of vehicles in urban environments,.
Enhancing Highway Safety: Accident Detection on the A9 Test Stretch Using Roadside Sensors A digital twin for teleoperation of vehicles in urban environments,
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T19:11:17.300254Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation cc800abb-87dc-453b-b592-841e468f668f · outbound
This paper cites Safe control transi- tions: Machine vision based observable readiness index and data-driven takeover time prediction,.
Enhancing Highway Safety: Accident Detection on the A9 Test Stretch Using Roadside Sensors Safe control transi- tions: Machine vision based observable readiness index and data-driven takeover time prediction,
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T19:11:17.291801Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 4562db87-7f2d-41e8-a8c8-2149edabd9ab · outbound
This paper cites TAD: A large-scale benchmark for traffic accidents detection from video surveillance.
Enhancing Highway Safety: Accident Detection on the A9 Test Stretch Using Roadside Sensors TAD: A large-scale benchmark for traffic accidents detection from video surveillance
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T19:11:17.283378Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation d403c3c5-3ccf-4871-84c5-81cd81605cac · outbound
This paper cites A survey on autonomous driving datasets: Statistics, annotation quality, and a future outlook,.
Enhancing Highway Safety: Accident Detection on the A9 Test Stretch Using Roadside Sensors A survey on autonomous driving datasets: Statistics, annotation quality, and a future outlook,
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T19:11:17.274840Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation a1fc7975-8506-44ec-a3df-6c09f0ff5777 · outbound
This paper cites Application of a rule-based approach in real-time crash risk prediction model devel- opment using loop detector data,.
Enhancing Highway Safety: Accident Detection on the A9 Test Stretch Using Roadside Sensors Application of a rule-based approach in real-time crash risk prediction model devel- opment using loop detector data,
Reference 24
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T19:11:16.899915Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 71f09cb6-63be-4a73-a1de-03736da23a1f · outbound
This paper cites A data-driven approach for road accident detection in surveillance videos,.
Enhancing Highway Safety: Accident Detection on the A9 Test Stretch Using Roadside Sensors A data-driven approach for road accident detection in surveillance videos,
Reference 25
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T19:11:17.264431Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 9223c359-2c29-4376-a4e9-8911b4e1c8fa · outbound
This paper cites Smart city transportation: Deep learning ensemble approach for traffic accident detection,.
Enhancing Highway Safety: Accident Detection on the A9 Test Stretch Using Roadside Sensors Smart city transportation: Deep learning ensemble approach for traffic accident detection,
Reference 26
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T19:11:17.255655Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 9439da82-717f-4dbe-89b3-737c426d047a · outbound
This paper cites DoTA: Unsupervised detection of traffic anomaly in driving videos,.
Enhancing Highway Safety: Accident Detection on the A9 Test Stretch Using Roadside Sensors DoTA: Unsupervised detection of traffic anomaly in driving videos,
Reference 27
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T19:11:17.247433Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 8531210e-b392-4167-ab56-df8a86e039a8 · outbound
This paper cites Freeway accident detec- tion and classification based on the multi-vehicle trajectory data and deep learning model,.
Enhancing Highway Safety: Accident Detection on the A9 Test Stretch Using Roadside Sensors Freeway accident detec- tion and classification based on the multi-vehicle trajectory data and deep learning model,
Reference 28
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T19:11:17.231773Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation dd38bd37-81ad-48ba-a3d6-b0c7e402a4ca · outbound
This paper cites Vision-based traffic accident detection and anticipation: A survey,.
Enhancing Highway Safety: Accident Detection on the A9 Test Stretch Using Roadside Sensors Vision-based traffic accident detection and anticipation: A survey,
Reference 29
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T19:11:17.222388Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 71c35d93-dcb8-4636-afd0-bcf30d4af487 · outbound
This paper cites Adaptive video- based algorithm for accident detection on highways,.
Enhancing Highway Safety: Accident Detection on the A9 Test Stretch Using Roadside Sensors Adaptive video- based algorithm for accident detection on highways,
Reference 30
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T19:11:17.213331Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation a9538f47-0d72-4157-8317-608147acc722 · outbound
This paper cites Deepacci- dent: A motion and accident prediction benchmark for v2x autonomous driving,.
Enhancing Highway Safety: Accident Detection on the A9 Test Stretch Using Roadside Sensors Deepacci- dent: A motion and accident prediction benchmark for v2x autonomous driving,
Reference 31
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T19:11:17.204610Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 247d0ca3-43a1-4a71-8f84-ac39f0d2bc99 · outbound
This paper cites CARLA: An open urban driving simulator,.
Enhancing Highway Safety: Accident Detection on the A9 Test Stretch Using Roadside Sensors CARLA: An open urban driving simulator,
Reference 32
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T19:11:17.194331Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 8a574ac9-56aa-491c-8d02-0b1150ed6921 · 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.
Enhancing Highway Safety: Accident Detection on the A9 Test Stretch Using Roadside Sensors Towards Explainable, Safe Autonomous Driving with Language Embeddings for Novelty Identification and Active Learning: Framework and Experimental Analysis with Real-World Data Sets
Reference 33
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T19:11:16.927354Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8b39a53b-72ac-4826-9acb-90165851ed6b · outbound
This paper cites Pedestrian behavior maps for safety advisories: Champ framework and real-world data analysis,.
Enhancing Highway Safety: Accident Detection on the A9 Test Stretch Using Roadside Sensors Pedestrian behavior maps for safety advisories: Champ framework and real-world data analysis,
Reference 34
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T19:11:17.185372Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation d1f0dcda-851e-4f40-a4cf-5b9c582c2c1b · outbound
This paper cites Ultralytics yolov8,.
Enhancing Highway Safety: Accident Detection on the A9 Test Stretch Using Roadside Sensors Ultralytics yolov8,
Reference 35
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T19:11:16.934256Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e867b3e6-0e06-4970-85e9-1b10a2ea4d39 · outbound
This paper cites Computer vision annotation tool (CV AT).
Enhancing Highway Safety: Accident Detection on the A9 Test Stretch Using Roadside Sensors Computer vision annotation tool (CV AT)
Reference 36
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T19:11:17.171880Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 26ba9588-fb65-417d-bf53-c772a1541e04 · outbound
This paper cites 3d bat: A semi-automatic, web-based 3d annotation toolbox for full-surround, multi-modal data streams,.
Enhancing Highway Safety: Accident Detection on the A9 Test Stretch Using Roadside Sensors 3d bat: A semi-automatic, web-based 3d annotation toolbox for full-surround, multi-modal data streams,
Reference 37
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T19:11:17.163929Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation af8986a9-075c-4728-9237-874e2c6e49a9 · outbound
This paper cites A9-dataset: Multi-sensor infrastructure-based dataset for mobility re- search,.
Enhancing Highway Safety: Accident Detection on the A9 Test Stretch Using Roadside Sensors A9-dataset: Multi-sensor infrastructure-based dataset for mobility re- search,
Reference 38
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T19:11:17.156656Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation fd6558f6-4912-4564-9162-3b235f4df6b6 · outbound
This paper cites TUMTraf intersection dataset: All you need for urban 3d camera-LiDAR roadside perception,.
Enhancing Highway Safety: Accident Detection on the A9 Test Stretch Using Roadside Sensors TUMTraf intersection dataset: All you need for urban 3d camera-LiDAR roadside perception,
Reference 39
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T19:11:17.147637Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation ca6926f8-7f7b-4dbd-9cf4-cff6ef4c1ec2 · outbound
This paper cites Tumtraf event: Calibration and fusion resulting in a dataset for roadside event-based and rgb cameras,.
Enhancing Highway Safety: Accident Detection on the A9 Test Stretch Using Roadside Sensors Tumtraf event: Calibration and fusion resulting in a dataset for roadside event-based and rgb cameras,
Reference 40
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T19:11:17.138893Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 7898ca56-da7c-4cb1-a337-77954b9ffaa0 · outbound
This paper cites Tumtraf v2x cooperative perception dataset,.
Enhancing Highway Safety: Accident Detection on the A9 Test Stretch Using Roadside Sensors Tumtraf v2x cooperative perception dataset,
Reference 41
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T19:11:17.129008Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 3e525cf9-4d09-4935-ad29-c268689eaffe · outbound
This paper cites W ARM-3d: A weakly-supervised sim2real domain adaptation framework for roadside monocular 3d object detection.
Enhancing Highway Safety: Accident Detection on the A9 Test Stretch Using Roadside Sensors W ARM-3d: A weakly-supervised sim2real domain adaptation framework for roadside monocular 3d object detection
Reference 42
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T19:11:17.120311Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 79614bdb-492f-4e4e-b719-d5083b12a337 · outbound
This paper cites TUM traffic dataset development kit.
Enhancing Highway Safety: Accident Detection on the A9 Test Stretch Using Roadside Sensors TUM traffic dataset development kit
Reference 43
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T19:11:17.111244Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 31b8e3f7-5324-4679-b4e5-da07d3beee71 · outbound
This paper cites Vision language models in autonomous driving: A survey and outlook,.
Enhancing Highway Safety: Accident Detection on the A9 Test Stretch Using Roadside Sensors Vision language models in autonomous driving: A survey and outlook,
Reference 44
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T19:11:17.101874Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation fe983a5f-2aef-4891-aa2f-b46dd6230396 · outbound
This paper cites AUTOtech.agil : Architecture and technologies for orchestrating automotive agility.
Enhancing Highway Safety: Accident Detection on the A9 Test Stretch Using Roadside Sensors AUTOtech.agil : Architecture and technologies for orchestrating automotive agility
Reference 45
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T19:11:17.091435Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 3faa8362-5d5d-4fe9-a415-2e637a20d2db · outbound
This paper cites Name: IEEE Trans.
Enhancing Highway Safety: Accident Detection on the A9 Test Stretch Using Roadside Sensors Name: IEEE Trans
Reference 459
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T19:11:17.239879Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Pith citing papers
No inbound Pith citation observations are available.