Paper Citation Record · LEDGER

NSF-HRPT: Neural Semantic Field meets Hierarchical Risk Perception Tree for Safety-Critical Scenario Assessment

As of 7 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 0 inbound Pith citation observations for arXiv:2608.04776.

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

pith.paper-citation-record.v1
2608.04776 v1

Coverage vector

measured 42 of 42 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T17:00:16.507996Z

measured 42 of 42 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-06T06:34:29.942622+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

42 of 42 outbound references displayed

verified exact2
verified fuzzy32
unresolved8
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 51cc5e56-d9d0-4d9a-bf84-c01f106598aa · outbound

This paper cites A survey on safety-critical driving scenario generation—a methodological perspective.IEEE Transactions on Intelligent Transportation Systems, 2023.

NSF-HRPT: Neural Semantic Field meets Hierarchical Risk Perception Tree for Safety-Critical Scenario Assessment A survey on safety-critical driving scenario generation—a methodological perspective.IEEE Transactions on Intelligent Transportation Systems, 2023

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:00:23.509876Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 6d0d0a0a-d969-45a0-b7be-f27be5ca76be · outbound

This paper cites Corner Cases for Visual Perception in Automated Driving: Some Guidance on Detection Approaches.

NSF-HRPT: Neural Semantic Field meets Hierarchical Risk Perception Tree for Safety-Critical Scenario Assessment Corner Cases for Visual Perception in Automated Driving: Some Guidance on Detection Approaches

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-06T17:00:12.731458Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 816134b1-4df0-46e5-ad5b-9d2fd29f83dd · outbound

This paper cites Scalability in perception for autonomous driving: Waymo open dataset.

NSF-HRPT: Neural Semantic Field meets Hierarchical Risk Perception Tree for Safety-Critical Scenario Assessment Scalability in perception for autonomous driving: Waymo open dataset

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:00:23.277765Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 68b259db-4e9f-447f-9463-cecd265bb832 · outbound

This paper cites Womd-lidar: Raw sensor dataset benchmark for motion forecasting.

NSF-HRPT: Neural Semantic Field meets Hierarchical Risk Perception Tree for Safety-Critical Scenario Assessment Womd-lidar: Raw sensor dataset benchmark for motion forecasting

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:00:23.110112Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 3effc527-f7e0-4b30-a17a-e6d4a4484def · outbound

This paper cites Nuscenes: A multimodal dataset for autonomous driving.

NSF-HRPT: Neural Semantic Field meets Hierarchical Risk Perception Tree for Safety-Critical Scenario Assessment Nuscenes: A multimodal dataset for autonomous driving

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:00:22.930961Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation d76a402f-f6d7-452c-b613-d734d234fc51 · outbound

This paper cites Anticipating accidents in dashcam videos.

NSF-HRPT: Neural Semantic Field meets Hierarchical Risk Perception Tree for Safety-Critical Scenario Assessment Anticipating accidents in dashcam videos

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:00:22.761164Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 558c8a96-18fb-4c33-8b26-6d7d3f06c93c · outbound

This paper cites Uncertainty-based traffic accident anticipation with spatio-temporal relational learning.

NSF-HRPT: Neural Semantic Field meets Hierarchical Risk Perception Tree for Safety-Critical Scenario Assessment Uncertainty-based traffic accident anticipation with spatio-temporal relational learning

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:00:22.572766Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 53fdd0da-134d-492e-9c22-487a0ebf795c · outbound

This paper cites Safety-critical scenario generation via reinforcement learning based editing.

NSF-HRPT: Neural Semantic Field meets Hierarchical Risk Perception Tree for Safety-Critical Scenario Assessment Safety-critical scenario generation via reinforcement learning based editing

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:00:22.273921Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 3897e8bc-4cba-4ea4-95ce-a7a4202a196b · outbound

This paper cites Diffscene: Diffusion-based safety-critical scenario generation for autonomous vehicles.

NSF-HRPT: Neural Semantic Field meets Hierarchical Risk Perception Tree for Safety-Critical Scenario Assessment Diffscene: Diffusion-based safety-critical scenario generation for autonomous vehicles

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:00:22.020139Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation f1a6e2ea-8d97-475c-8147-df5a89f1a8b0 · outbound

This paper cites Chatscene: Knowledge-enabled safety-critical scenario generation for autonomous vehicles.

NSF-HRPT: Neural Semantic Field meets Hierarchical Risk Perception Tree for Safety-Critical Scenario Assessment Chatscene: Knowledge-enabled safety-critical scenario generation for autonomous vehicles

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:00:21.788073Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 1c4954bb-e991-484d-8bcd-cd157009aded · outbound

This paper cites A dynamic spatial-temporal attention network for early anticipation of traffic accidents.IEEE Transactions on Intelligent Transportation Systems, 2022.

NSF-HRPT: Neural Semantic Field meets Hierarchical Risk Perception Tree for Safety-Critical Scenario Assessment A dynamic spatial-temporal attention network for early anticipation of traffic accidents.IEEE Transactions on Intelligent Transportation Systems, 2022

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:00:21.435069Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation d45e61d8-47ad-4d45-83b1-bd0b2fe5c4c6 · outbound

This paper cites World model- based end-to-end scene generation for accident anticipation in autonomous driving.Communications Engineering, 2025.

NSF-HRPT: Neural Semantic Field meets Hierarchical Risk Perception Tree for Safety-Critical Scenario Assessment World model- based end-to-end scene generation for accident anticipation in autonomous driving.Communications Engineering, 2025

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:00:21.205001Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 0ffb07d6-04df-445b-8abf-0d12c41c1be4 · outbound

This paper cites CARLA: An open urban driving simulator.

NSF-HRPT: Neural Semantic Field meets Hierarchical Risk Perception Tree for Safety-Critical Scenario Assessment CARLA: An open urban driving simulator

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:00:21.037978Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 19154ee5-3ebd-4f44-9016-16f1c9dcedd8 · outbound

This paper cites DiffRoad: Realistic and Diverse Road Scenario Generation for Autonomous Vehicle Testing.

NSF-HRPT: Neural Semantic Field meets Hierarchical Risk Perception Tree for Safety-Critical Scenario Assessment DiffRoad: Realistic and Diverse Road Scenario Generation for Autonomous Vehicle Testing

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T17:00:13.850822Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b58b07a3-b422-406c-a9fe-204beb34fbf8 · outbound

This paper cites Trafficgen: Learning to generate diverse and realistic traffic scenarios.

NSF-HRPT: Neural Semantic Field meets Hierarchical Risk Perception Tree for Safety-Critical Scenario Assessment Trafficgen: Learning to generate diverse and realistic traffic scenarios

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:00:20.782042Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation bf64d0a9-65cd-4e90-9da7-5b6a2e2290f5 · outbound

This paper cites AuthSim: Towards Authentic and Effective Safety-critical Scenario Generation for Autonomous Driving Tests.

NSF-HRPT: Neural Semantic Field meets Hierarchical Risk Perception Tree for Safety-Critical Scenario Assessment AuthSim: Towards Authentic and Effective Safety-critical Scenario Generation for Autonomous Driving Tests

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-06T17:00:13.976384Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1a5cc796-ac70-436a-bdd7-62b8c1fd06b7 · outbound

This paper cites Safety2Drive: Safety-Critical Scenario Benchmark for the Evaluation of Autonomous Driving.

NSF-HRPT: Neural Semantic Field meets Hierarchical Risk Perception Tree for Safety-Critical Scenario Assessment Safety2Drive: Safety-Critical Scenario Benchmark for the Evaluation of Autonomous Driving

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-08-06T17:00:16.991354Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 719bf5f3-72d4-411b-b528-9bb5245922ff · outbound

This paper cites Seeking to Collide: Online Safety-Critical Scenario Generation for Autonomous Driving with Retrieval Augmented Large Language Models.

NSF-HRPT: Neural Semantic Field meets Hierarchical Risk Perception Tree for Safety-Critical Scenario Assessment Seeking to Collide: Online Safety-Critical Scenario Generation for Autonomous Driving with Retrieval Augmented Large Language Models

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-06T17:00:14.076023Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e444c52a-e78a-4763-979a-1409a953c62e · outbound

This paper cites Adversarial Generation and Collaborative Evolution of Safety-Critical Scenarios for Autonomous Vehicles.

NSF-HRPT: Neural Semantic Field meets Hierarchical Risk Perception Tree for Safety-Critical Scenario Assessment Adversarial Generation and Collaborative Evolution of Safety-Critical Scenarios for Autonomous Vehicles

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-06T17:00:14.148454Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5f47f2de-ed21-479e-bd8a-8b014c54629a · outbound

This paper cites Egocentric vision-based future vehicle localization for intelligent driving assistance systems.

NSF-HRPT: Neural Semantic Field meets Hierarchical Risk Perception Tree for Safety-Critical Scenario Assessment Egocentric vision-based future vehicle localization for intelligent driving assistance systems

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:00:20.605227Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation d58b404c-9bfd-47a0-9c47-0243d5dfa562 · outbound

This paper cites Unsupervised traffic accident detection in first-person videos.

NSF-HRPT: Neural Semantic Field meets Hierarchical Risk Perception Tree for Safety-Critical Scenario Assessment Unsupervised traffic accident detection in first-person videos

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:00:20.455643Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 66a74c18-69d2-4d99-9271-f28e7ec8bc43 · outbound

This paper cites Anovox: A benchmark for multimodal anomaly detection in autonomous driving.

NSF-HRPT: Neural Semantic Field meets Hierarchical Risk Perception Tree for Safety-Critical Scenario Assessment Anovox: A benchmark for multimodal anomaly detection in autonomous driving

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:00:20.325943Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 149cfada-da57-4170-9e1e-66706a593be0 · outbound

This paper cites Spotting the unexpected (stu): A 3d lidar dataset for anomaly segmentation in autonomous driving.

NSF-HRPT: Neural Semantic Field meets Hierarchical Risk Perception Tree for Safety-Critical Scenario Assessment Spotting the unexpected (stu): A 3d lidar dataset for anomaly segmentation in autonomous driving

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:00:20.179935Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 0e5ba2e6-e5fb-4eb8-ab8e-195b96f2f01f · outbound

This paper cites UMAD: Unsupervised Mask-Level Anomaly Detection for Autonomous Driving.

NSF-HRPT: Neural Semantic Field meets Hierarchical Risk Perception Tree for Safety-Critical Scenario Assessment UMAD: Unsupervised Mask-Level Anomaly Detection for Autonomous Driving

Reference 24

Resolution: verified exact; local_arxiv, observed 2026-08-06T17:00:16.761916Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 069110c5-52a7-4a54-97fd-e24e99261e81 · outbound

This paper cites Jisam: Alleviate labeling burden and corner case problems in autonomous driving via minimal real-world data.

NSF-HRPT: Neural Semantic Field meets Hierarchical Risk Perception Tree for Safety-Critical Scenario Assessment Jisam: Alleviate labeling burden and corner case problems in autonomous driving via minimal real-world data

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:00:20.023761Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 743b3b46-6f3b-4ea4-a43b-0260128ec632 · outbound

This paper cites Graph(graph): A nested graph-based framework for early accident anticipation.

NSF-HRPT: Neural Semantic Field meets Hierarchical Risk Perception Tree for Safety-Critical Scenario Assessment Graph(graph): A nested graph-based framework for early accident anticipation

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:00:19.825734Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 76488997-750c-4769-b9c4-7c3cb2e0ae2f · outbound

This paper cites Crash: Crash recognition and anticipation system harnessing with context-aware and temporal focus attentions.

NSF-HRPT: Neural Semantic Field meets Hierarchical Risk Perception Tree for Safety-Critical Scenario Assessment Crash: Crash recognition and anticipation system harnessing with context-aware and temporal focus attentions

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:00:19.703189Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation d8de6142-3060-4ce7-a8e3-3b4b7ffd96f2 · outbound

This paper cites Latte: A real-time lightweight attention-based traffic accident anticipation engine.Information Fusion, 2025.

NSF-HRPT: Neural Semantic Field meets Hierarchical Risk Perception Tree for Safety-Critical Scenario Assessment Latte: A real-time lightweight attention-based traffic accident anticipation engine.Information Fusion, 2025

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:00:19.563393Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation b893acd5-6ea4-4997-b54f-3cd55e6bd953 · outbound

This paper cites Singulartrajectory: Universal trajectory predictor using diffusion model.

NSF-HRPT: Neural Semantic Field meets Hierarchical Risk Perception Tree for Safety-Critical Scenario Assessment Singulartrajectory: Universal trajectory predictor using diffusion model

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:00:19.404369Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 0db28adf-893d-4288-ba4e-e310c2161290 · outbound

This paper cites EgoNav: Egocentric Scene-aware Human Trajectory Prediction.

NSF-HRPT: Neural Semantic Field meets Hierarchical Risk Perception Tree for Safety-Critical Scenario Assessment EgoNav: Egocentric Scene-aware Human Trajectory Prediction

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-06T17:00:14.870591Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a3dd3739-e652-4990-a507-7f25b150a198 · outbound

This paper cites A novel benchmarking paradigm and a scale-and motion-aware model for egocentric pedestrian trajectory prediction.

NSF-HRPT: Neural Semantic Field meets Hierarchical Risk Perception Tree for Safety-Critical Scenario Assessment A novel benchmarking paradigm and a scale-and motion-aware model for egocentric pedestrian trajectory prediction

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:00:19.263745Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 3cf2a9e2-f15b-4052-ac3a-c9adb9203f05 · outbound

This paper cites DriveMRP: Enhancing Vision-Language Models with Synthetic Motion Data for Motion Risk Prediction.

NSF-HRPT: Neural Semantic Field meets Hierarchical Risk Perception Tree for Safety-Critical Scenario Assessment DriveMRP: Enhancing Vision-Language Models with Synthetic Motion Data for Motion Risk Prediction

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-06T17:00:14.978908Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 392a60f0-e682-4b49-9a3e-e9a9f83a40fb · outbound

This paper cites When, where, and what? a benchmark for accident anticipation and localization with large language models.

NSF-HRPT: Neural Semantic Field meets Hierarchical Risk Perception Tree for Safety-Critical Scenario Assessment When, where, and what? a benchmark for accident anticipation and localization with large language models

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:00:19.082072Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation ef9f203c-4616-4cb3-9f76-78423b08e46b · outbound

This paper cites Neat: Neural attention fields for end-to-end autonomous driving.

NSF-HRPT: Neural Semantic Field meets Hierarchical Risk Perception Tree for Safety-Critical Scenario Assessment Neat: Neural attention fields for end-to-end autonomous driving

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:00:18.930159Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 639ef2bf-fe77-4249-b2eb-a517a3ccda43 · outbound

This paper cites Deep residual learning for image recognition.

NSF-HRPT: Neural Semantic Field meets Hierarchical Risk Perception Tree for Safety-Critical Scenario Assessment Deep residual learning for image recognition

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:00:18.698257Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 7730c312-a685-4c9b-b285-908bbdefef91 · outbound

This paper cites Attention is all you need.

NSF-HRPT: Neural Semantic Field meets Hierarchical Risk Perception Tree for Safety-Critical Scenario Assessment Attention is all you need

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:00:18.501575Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation a6ddc089-d681-492b-8705-37c513027de7 · outbound

This paper cites Safebench: A benchmarking platform for safety evaluation of autonomous vehicles.

NSF-HRPT: Neural Semantic Field meets Hierarchical Risk Perception Tree for Safety-Critical Scenario Assessment Safebench: A benchmarking platform for safety evaluation of autonomous vehicles

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:00:18.304967Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 1b1ce964-29a1-436d-bd7f-503199a908ac · outbound

This paper cites Najm, John D.

NSF-HRPT: Neural Semantic Field meets Hierarchical Risk Perception Tree for Safety-Critical Scenario Assessment Najm, John D

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:00:18.090219Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 2524cc7c-4712-4b44-9638-fdeb1053ffc6 · outbound

This paper cites Bdd100k: A diverse driving dataset for heterogeneous multitask learning.

NSF-HRPT: Neural Semantic Field meets Hierarchical Risk Perception Tree for Safety-Critical Scenario Assessment Bdd100k: A diverse driving dataset for heterogeneous multitask learning

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:00:17.826462Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 801c5722-0797-4c6e-ae74-d39588b5a0ce · outbound

This paper cites Anticipating traffic accidents with adaptive loss and large-scale incident db.

NSF-HRPT: Neural Semantic Field meets Hierarchical Risk Perception Tree for Safety-Critical Scenario Assessment Anticipating traffic accidents with adaptive loss and large-scale incident db

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:00:17.565626Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation e9c3a304-f917-423f-94a6-b5e53c1e9e55 · outbound

This paper cites Berg, Wan-Yen Lo, Piotr Dollár, and Ross Girshick.

NSF-HRPT: Neural Semantic Field meets Hierarchical Risk Perception Tree for Safety-Critical Scenario Assessment Berg, Wan-Yen Lo, Piotr Dollár, and Ross Girshick

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-06T17:00:16.417738Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fdd4714e-6e17-421d-956e-0751bc0b50df · outbound

This paper cites Vision transformers for dense prediction.

NSF-HRPT: Neural Semantic Field meets Hierarchical Risk Perception Tree for Safety-Critical Scenario Assessment Vision transformers for dense prediction

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:00:17.281632Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Pith citing papers

No inbound Pith citation observations are available.