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Paper Citation Record · LEDGER

Adversarial Generation and Collaborative Evolution of Safety-Critical Scenarios for Autonomous Vehicles

As of 8 August 2026, this Paper Citation Record lists 63 of 63 outbound references and 2 inbound Pith citation observations for arXiv:2508.14527.

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

pith.paper-citation-record.v1
2508.14527 v2

Coverage vector

measured 63 of 63 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T18:34:46.520629Z

measured 65 of 65 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T17:00:14.148454Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-12T05:21:27.488184Z

Reference resolution

63 of 63 outbound references displayed

  • verified exact2
  • verified fuzzy45
  • unresolved15
  • parse uncertain1
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 32794372-b40f-49c5-accf-119d297e8ece · outbound

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

Adversarial Generation and Collaborative Evolution of Safety-Critical Scenarios for Autonomous Vehicles nuscenes: A multimodal dataset for autonomous driving

Reference 1

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unresolved
no resolver link, observed 2026-08-05T18:34:40.557021Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T18:34:40.557021Z digest=sha256:d0d22d5e60219f6bd7b89aa3dae80a40ba4f7a22f2c77d58320ec32b0eb924e3

Observation 2c712e1f-800e-4cf3-ae3a-4599a54eebab · outbound

This paper cites Text2Scenario: Text-Driven Scenario Generation for Autonomous Driving Test.

Adversarial Generation and Collaborative Evolution of Safety-Critical Scenarios for Autonomous Vehicles Text2Scenario: Text-Driven Scenario Generation for Autonomous Driving Test

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-05T18:34:40.774782Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T18:34:40.774782Z digest=sha256:47c0d16e07c573887445e3459e40e807ebceba698bdf16598f7afa5ad755b83b

Observation 33f580bc-61e0-4b26-93a8-6021888302a2 · outbound

This paper cites Advdo: Realistic adversarial attacks for trajectory prediction.

Adversarial Generation and Collaborative Evolution of Safety-Critical Scenarios for Autonomous Vehicles Advdo: Realistic adversarial attacks for trajectory prediction

Reference 3

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:34:55.394773Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T18:34:40.873066Z digest=sha256:6b1565f02ccca3f3557a9a8e0749b6574d7fa0044556c4d120fd3c1ce9594448

Observation 1ed37f6c-f34e-4e43-97b8-50e688abd526 · outbound

This paper cites Adversarial evaluation of autonomous vehicles in lane-change scenarios.

Adversarial Generation and Collaborative Evolution of Safety-Critical Scenarios for Autonomous Vehicles Adversarial evaluation of autonomous vehicles in lane-change scenarios

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:34:55.162405Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T18:34:40.992029Z digest=sha256:be59b63ff262d2512e78e3ac92c75d6d70314390b51c2bac02688fa5f9367e00

Observation 8c183e7f-0e14-4808-9d86-f12938f58896 · outbound

This paper cites Carla Scenario Runner.

Adversarial Generation and Collaborative Evolution of Safety-Critical Scenarios for Autonomous Vehicles Carla Scenario Runner

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:34:54.957799Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T18:34:41.057628Z digest=sha256:2e364724124e1b4ea8afa6213c3687df47cfb3b53a1728ad83ee0b055526403d

Observation 3fb9ad4d-75bf-41f2-ada9-09357ffe2a24 · outbound

This paper cites Learning to collide: An adaptive safety-critical scenarios generating method.

Adversarial Generation and Collaborative Evolution of Safety-Critical Scenarios for Autonomous Vehicles Learning to collide: An adaptive safety-critical scenarios generating method

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:34:54.797346Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T18:34:41.166860Z digest=sha256:92b57a06ffef4a4307ec842ddc06c1e3e30b3b094a53adf9d4f85f886a6be410

Observation f0abdc58-49f7-4bf4-a667-df55999d19a4 · outbound

This paper cites A survey on in-context learning.

Adversarial Generation and Collaborative Evolution of Safety-Critical Scenarios for Autonomous Vehicles A survey on in-context learning

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:34:54.606200Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T18:34:41.252426Z digest=sha256:1eac7a78576cf2d5f2731568761931b4f39255ed3ba19a65c0f5307424bc3296

Observation 2f2c4b0d-a15e-4c60-ba91-0c23c0ea4f4e · outbound

This paper cites CARLA: An open urban driving simulator.

Adversarial Generation and Collaborative Evolution of Safety-Critical Scenarios for Autonomous Vehicles CARLA: An open urban driving simulator

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:34:54.421040Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T18:34:41.314149Z digest=sha256:458e0d1b03d175a73a7f99901ec63f84f7bdf6f655410f5f91d5770171ebc6fd

Observation 947c87c9-573f-46ca-b10a-78d4217097ab · outbound

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

Adversarial Generation and Collaborative Evolution of Safety-Critical Scenarios for Autonomous Vehicles Trafficgen: Learning to generate diverse and realistic traffic scenarios

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:34:54.250757Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T18:34:41.394316Z digest=sha256:7fbd37c1d695f3c5d42498be84ba713d0fe0c9433a233c361b20a59ceda33f1b

Observation a94c6fe0-c925-4374-87b9-c17d0a2a303d · outbound

This paper cites Dense reinforcement learning for safety validation of autonomous vehicles.

Adversarial Generation and Collaborative Evolution of Safety-Critical Scenarios for Autonomous Vehicles Dense reinforcement learning for safety validation of autonomous vehicles

Reference 10

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unresolved
no resolver link, observed 2026-08-05T18:34:41.492943Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T18:34:41.492943Z digest=sha256:ccf1d7a60b387ce390c38a94c0ba5b907c164b3f8cf4685672232173d98203f7

Observation 3b11949a-2ca7-4c42-83a0-e65871ddfe3c · outbound

This paper cites Scenic: a language for scenario specification and scene generation.

Adversarial Generation and Collaborative Evolution of Safety-Critical Scenarios for Autonomous Vehicles Scenic: a language for scenario specification and scene generation

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:34:54.021964Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T18:34:41.584057Z digest=sha256:30f6c0e60a9c2f0f678b6a44e0235892cf49a1306baf1d744ff81268ecbe5349

Observation fe414130-ccad-442b-9187-c20fba07f9f9 · outbound

This paper cites Scenic: A language for scenario specification and data generation.

Adversarial Generation and Collaborative Evolution of Safety-Critical Scenarios for Autonomous Vehicles Scenic: A language for scenario specification and data generation

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:34:53.883974Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T18:34:41.663623Z digest=sha256:22fd057976be141297b1a8a6f1d6acce01a06eb4612fff14d922471ed3bffe81

Observation fdb9c040-b40f-4910-87cf-db00c85cb6ff · outbound

This paper cites Addressing function approximation error in actor-critic methods.

Adversarial Generation and Collaborative Evolution of Safety-Critical Scenarios for Autonomous Vehicles Addressing function approximation error in actor-critic methods

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:34:53.641330Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T18:34:41.754166Z digest=sha256:0b7e3c2c72b896d88f26971b95ff45fb16fc650ca99ba2b087c0915589b4bb28

Observation 5d9e1d33-2cc4-4a45-b0e6-a32a22b4f921 · outbound

This paper cites MagicDrive: Street view generation with diverse 3d geometry control.

Adversarial Generation and Collaborative Evolution of Safety-Critical Scenarios for Autonomous Vehicles MagicDrive: Street view generation with diverse 3d geometry control

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:34:53.449884Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T18:34:41.877257Z digest=sha256:9f990144402bef4b25faed727bbd693d2caf09ec3db6afe7ecb0b0ddc7d855fe

Observation e9ac83b0-2c41-49c5-98a2-876799f31b98 · outbound

This paper cites A comprehensive evaluation framework for deep model robustness.

Adversarial Generation and Collaborative Evolution of Safety-Critical Scenarios for Autonomous Vehicles A comprehensive evaluation framework for deep model robustness

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:34:53.214431Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T18:34:41.973451Z digest=sha256:b0061c9be16c6e782ad10a1bc72634e0319a37c0237ea21a4f62637017d916a7

Observation 6063e6f9-33ca-4957-879b-bd1e3ad4dcd5 · outbound

This paper cites Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor.

Adversarial Generation and Collaborative Evolution of Safety-Critical Scenarios for Autonomous Vehicles Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:34:53.029451Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T18:34:42.098942Z digest=sha256:708e8c51a80dd589871367592ca6067f39af472b16ede123665f61e401b93551

Observation 1a9c9748-6ee0-4453-9571-3a34470bc16b · outbound

This paper cites Planning-oriented autonomous driving.

Adversarial Generation and Collaborative Evolution of Safety-Critical Scenarios for Autonomous Vehicles Planning-oriented autonomous driving

Reference 17

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:34:52.796794Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T18:34:42.211317Z digest=sha256:b3344e050dcd647d4181d12d4b96073bd33b6e368019c5483def12dd6aa1b448

Observation 0856b3da-15d7-47cc-a489-b1fe6399b3ff · outbound

This paper cites MetaDrive: Composing Diverse Driving Scenarios for Generalizable Reinforcement Learning.

Adversarial Generation and Collaborative Evolution of Safety-Critical Scenarios for Autonomous Vehicles MetaDrive: Composing Diverse Driving Scenarios for Generalizable Reinforcement Learning

Reference 18

Resolution
verified exact
local_arxiv, observed 2026-08-05T18:34:47.023982Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T18:34:42.328398Z digest=sha256:0b8a3f3216725ab447fec2fc3addd2d8eb0732ce754457a3ce10af726f6a0e6c

Observation 1b06bca5-cef9-4364-88fb-52b3e4ca1c0c · outbound

This paper cites Chatsumo: Large language model for automating traffic scenario generation in simulation of urban mobility.

Adversarial Generation and Collaborative Evolution of Safety-Critical Scenarios for Autonomous Vehicles Chatsumo: Large language model for automating traffic scenario generation in simulation of urban mobility

Reference 19

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:34:52.638587Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T18:34:42.451089Z digest=sha256:9a1d44e7d19f0d82fd8a598c777d5b32a6a73d1df06e471304b35850283e8c36

Observation e217b643-f644-4e93-b7af-46cb8afa5f2c · outbound

This paper cites Bevformer: Learning bird’s-eye-view representation from multi-camera images via spatiotemporal transformers.

Adversarial Generation and Collaborative Evolution of Safety-Critical Scenarios for Autonomous Vehicles Bevformer: Learning bird’s-eye-view representation from multi-camera images via spatiotemporal transformers

Reference 20

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:34:52.488743Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T18:34:42.606530Z digest=sha256:3ad07472b790aa8a20dfdb8c30f2e43fa1da83a565ef3f1c82c5033d7a55e171

Observation 0fa0cbec-f492-4bd9-862e-f999f9389a06 · outbound

This paper cites A large-scale multiple-objective method for black-box attack against object detection.

Adversarial Generation and Collaborative Evolution of Safety-Critical Scenarios for Autonomous Vehicles A large-scale multiple-objective method for black-box attack against object detection

Reference 21

Resolution
unresolved
no resolver link, observed 2026-08-05T18:34:42.765110Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T18:34:42.765110Z digest=sha256:abf2a70413e42d7e4450211dffbad9ce0b91c836ddf5a66f4b5255c38aa04ed8

Observation 4b044707-7c6c-4b03-b638-2fa5a7b65624 · outbound

This paper cites Revisiting backdoor attacks against large vision-language models from domain shift.

Adversarial Generation and Collaborative Evolution of Safety-Critical Scenarios for Autonomous Vehicles Revisiting backdoor attacks against large vision-language models from domain shift

Reference 22

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:34:52.319262Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T18:34:42.909389Z digest=sha256:46d51bccd7bb8079e16946bd03097b61f3a4f8b110759c4781bdd310bf452039

Observation 56741293-d004-4a47-b283-30703f6bf38d · outbound

This paper cites Object Detectors in the Open Environment: Challenges, Solutions, and Outlook.

Adversarial Generation and Collaborative Evolution of Safety-Critical Scenarios for Autonomous Vehicles Object Detectors in the Open Environment: Challenges, Solutions, and Outlook

Reference 23

Resolution
unresolved
no resolver link, observed 2026-08-05T18:34:42.990682Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T18:34:42.990682Z digest=sha256:5e5be9d66cb7ab5e14730e1f8206c3d65018fdbf6acae7831fe26accf8c65629

Observation ee44f02f-b338-4ebc-947a-fd0e6f8bd147 · outbound

This paper cites Efficient adversarial attacks for visual object tracking.

Adversarial Generation and Collaborative Evolution of Safety-Critical Scenarios for Autonomous Vehicles Efficient adversarial attacks for visual object tracking

Reference 24

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:34:52.153549Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T18:34:43.077059Z digest=sha256:af7bc8560690d97a9e2a9dda5088813bd22d439a70496b9c1c5faddeeb5bbbff

Observation ede44c2b-e913-4bba-8e9e-c1a5d7c474ea · outbound

This paper cites Parallel Rectangle Flip Attack: A Query-based Black-box Attack against Object Detection.

Adversarial Generation and Collaborative Evolution of Safety-Critical Scenarios for Autonomous Vehicles Parallel Rectangle Flip Attack: A Query-based Black-box Attack against Object Detection

Reference 25

Resolution
unresolved
no resolver link, observed 2026-08-05T18:34:43.154882Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T18:34:43.154882Z digest=sha256:ec4ec036e9e1cc6496cc3c3e10a125610459aea9fe91f7fbd5d7d7f48c2620e6

Observation 57e167be-5ae0-4367-901a-c661b24b8ac4 · outbound

This paper cites BadCLIP: Dual-Embedding Guided Backdoor Attack on Multimodal Contrastive Learning.

Adversarial Generation and Collaborative Evolution of Safety-Critical Scenarios for Autonomous Vehicles BadCLIP: Dual-Embedding Guided Backdoor Attack on Multimodal Contrastive Learning

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-05T18:34:43.251072Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T18:34:43.251072Z digest=sha256:622c0d188270a6fd04bad1a005633a2b2bdc23bead7905c630fd50ba7ce91ceb

Observation b6d9318d-ab6d-4a97-a184-a3e883744964 · outbound

This paper cites X-adv: Physical adversarial object attacks against x-ray prohibited item detection.

Adversarial Generation and Collaborative Evolution of Safety-Critical Scenarios for Autonomous Vehicles X-adv: Physical adversarial object attacks against x-ray prohibited item detection

Reference 27

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:34:51.852783Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T18:34:43.374149Z digest=sha256:450a5539cb8e2492077c3c556681683d9ff00cf515afbbcf975a07a33d5c3728

Observation 93110e38-0e43-48a1-922e-2ef59bb2a762 · outbound

This paper cites Spatiotemporal attacks for embodied agents.

Adversarial Generation and Collaborative Evolution of Safety-Critical Scenarios for Autonomous Vehicles Spatiotemporal attacks for embodied agents

Reference 28

Resolution
unresolved
no resolver link, observed 2026-08-05T18:34:43.442629Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T18:34:43.442629Z digest=sha256:90ab59bab482ac7319d8cb36632079d14a0b92892adec55f945fe850c72f976e

Observation c37c3e2f-ab8e-4d62-9c8b-09b1b33d12d5 · outbound

This paper cites Perceptual-sensitive gan for generating adversarial patches.

Adversarial Generation and Collaborative Evolution of Safety-Critical Scenarios for Autonomous Vehicles Perceptual-sensitive gan for generating adversarial patches

Reference 29

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:34:51.559954Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T18:34:43.563730Z digest=sha256:d15999c63c06a16ed1e0a0e7e56b7cd01fe84e235f2eab3021b792b8e3dc61f7

Observation 27d6892e-090d-4dd7-8c14-ce1ce91350f5 · outbound

This paper cites Training robust deep neural networks via adversarial noise propagation.

Adversarial Generation and Collaborative Evolution of Safety-Critical Scenarios for Autonomous Vehicles Training robust deep neural networks via adversarial noise propagation

Reference 30

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:34:51.252404Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T18:34:43.657134Z digest=sha256:a8a9f0e5bcc93ef93201f2045dbe4cef00c27083057b31dd074513e767b59cf1

Observation 3cb4479c-3927-458a-81c2-54cdf437d9c9 · outbound

This paper cites Towards defending multiple lp-norm bounded adversarial perturbations via gated batch normalization.

Adversarial Generation and Collaborative Evolution of Safety-Critical Scenarios for Autonomous Vehicles Towards defending multiple lp-norm bounded adversarial perturbations via gated batch normalization

Reference 31

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:34:50.958862Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T18:34:43.769703Z digest=sha256:29920aee8ef49cb550dcbde255a3510dee5a14f76efc305e6e8b48f3eabece8b

Observation 62a55edc-c1a1-4d45-9ff8-403e6ca1cf04 · outbound

This paper cites Exploring the relationship between architecture and adversarially robust generalization.

Adversarial Generation and Collaborative Evolution of Safety-Critical Scenarios for Autonomous Vehicles Exploring the relationship between architecture and adversarially robust generalization

Reference 32

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:34:50.734870Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T18:34:43.913147Z digest=sha256:b15a28e4dcdbb53a7a7e1867ddfdff6146e26fc14977431cd713947786d1e524

Observation 21b87c03-de7e-4b1c-b81d-d99e8a78cf3c · outbound

This paper cites Bias- based universal adversarial patch attack for automatic check-out.

Adversarial Generation and Collaborative Evolution of Safety-Critical Scenarios for Autonomous Vehicles Bias- based universal adversarial patch attack for automatic check-out

Reference 33

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:34:50.528776Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T18:34:43.990814Z digest=sha256:a521b8c5db7f9f8e749692010e9395be5bd72a17bace7f7513a80ad01fc0d28d

Observation 590ecbd9-250c-4946-8985-5568127bbbfc · outbound

This paper cites Natural Reflection Backdoor Attack on Vision Language Model for Autonomous Driving.

Adversarial Generation and Collaborative Evolution of Safety-Critical Scenarios for Autonomous Vehicles Natural Reflection Backdoor Attack on Vision Language Model for Autonomous Driving

Reference 34

Resolution
unresolved
no resolver link, observed 2026-08-05T18:34:44.065462Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T18:34:44.065462Z digest=sha256:e0d715c09fde2bc41c398ecff3ca9949546e6c29c100be263eb9604fc04b0971

Observation 9d3d05a7-930b-410b-abef-1ff5e8c69d3c · outbound

This paper cites Harnessing perceptual adversarial patches for crowd counting.

Adversarial Generation and Collaborative Evolution of Safety-Critical Scenarios for Autonomous Vehicles Harnessing perceptual adversarial patches for crowd counting

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:34:50.399528Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T18:34:44.148533Z digest=sha256:bf48d698085478f39665ec3b95661c75aef868d9a981248b49de5576ceb371c1

Observation 016cbd1a-d3a4-4ee7-8e7b-2e2462f63294 · outbound

This paper cites Multimodal Large Language Model Driven Scenario Testing for Autonomous Vehicles.

Adversarial Generation and Collaborative Evolution of Safety-Critical Scenarios for Autonomous Vehicles Multimodal Large Language Model Driven Scenario Testing for Autonomous Vehicles

Reference 36

Resolution
unresolved
no resolver link, observed 2026-08-05T18:34:44.228032Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T18:34:44.228032Z digest=sha256:1c35e0a58d4c03077510cbf5316f2ef9ffe19c068afeb8f20c93df9afe3e9d26

Observation 24355c07-7267-4f70-ae63-66c96fb2cc30 · outbound

This paper cites Dolphins: Multimodal language model for driving.

Adversarial Generation and Collaborative Evolution of Safety-Critical Scenarios for Autonomous Vehicles Dolphins: Multimodal language model for driving

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:34:50.228547Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T18:34:44.319457Z digest=sha256:10bcbc4b4d8f1ae2b2d2e02b7f7d87f6e867417c7c0027993f7d9f2b6e6db90d

Observation b6d46351-55c8-41ba-9cf6-999f82f61f35 · outbound

This paper cites Pre-crash scenario typology for crash avoidance research.

Adversarial Generation and Collaborative Evolution of Safety-Critical Scenarios for Autonomous Vehicles Pre-crash scenario typology for crash avoidance research

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:34:50.060781Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T18:34:44.408702Z digest=sha256:f2f7417c2e9bffc7655aec8b9315011ec7f98d94ba187e35972830d9bd41c5fb

Observation cf331844-2591-4b7a-9687-95a09a379db9 · outbound

This paper cites Generating useful accident-prone driving scenarios via a learned traffic prior.

Adversarial Generation and Collaborative Evolution of Safety-Critical Scenarios for Autonomous Vehicles Generating useful accident-prone driving scenarios via a learned traffic prior

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:34:49.904962Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T18:34:44.499166Z digest=sha256:29e188fc6edf5e431f2deb9243a89ab757509c16a22512d52e73503b2c434863

Observation f6e31089-d345-4869-9511-0c9ecebc3794 · outbound

This paper cites Proximal Policy Optimization Algorithms.

Adversarial Generation and Collaborative Evolution of Safety-Critical Scenarios for Autonomous Vehicles Proximal Policy Optimization Algorithms

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-05T18:34:44.575395Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T18:34:44.575395Z digest=sha256:4bd3767272a886308b448dcb8d277e516b51d20eb88ebff5fef861e2685ba1c0

Observation db1b6c9e-0bd9-462f-ac57-66f913675918 · outbound

This paper cites Lmdrive: Closed-loop end-to-end driving with large language models.

Adversarial Generation and Collaborative Evolution of Safety-Critical Scenarios for Autonomous Vehicles Lmdrive: Closed-loop end-to-end driving with large language models

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:34:49.704422Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T18:34:44.666021Z digest=sha256:e520e2741b428d1b37bcb0a4d5f6db736ecf9207053968ec209cd23c9dbc3192

Observation e153b4c7-6e24-4ad5-99cd-e83f0196cbcc · outbound

This paper cites Trafficsim: Learning to simulate realistic multi-agent behaviors.

Adversarial Generation and Collaborative Evolution of Safety-Critical Scenarios for Autonomous Vehicles Trafficsim: Learning to simulate realistic multi-agent behaviors

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-05T18:34:44.771811Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T18:34:44.771811Z digest=sha256:983630d3c8c8d1359b0bfa919ae87adc106f347d497c9467e743a133cae59a45

Observation ebba10e7-508a-4637-867b-1713d490fab8 · outbound

This paper cites Scenegen: Learning to generate realistic traffic scenes.

Adversarial Generation and Collaborative Evolution of Safety-Critical Scenarios for Autonomous Vehicles Scenegen: Learning to generate realistic traffic scenes

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:34:49.558279Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T18:34:44.854906Z digest=sha256:5442fd540c74fbef9850d8fcf49c1a49232f4e3e1e4219b974992688f4795e9e

Observation ccbfbb1d-6aaf-4868-a6fe-68760617b5c7 · outbound

This paper cites Robustart: Benchmarking robustness on architecture design and training techniques.

Adversarial Generation and Collaborative Evolution of Safety-Critical Scenarios for Autonomous Vehicles Robustart: Benchmarking robustness on architecture design and training techniques

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:34:49.420454Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T18:34:44.919373Z digest=sha256:13dc3c469e3a97fda4c04d6c5bce0736decd8bd0435bfd96fc025bc43cfa3819

Observation 7c07cea6-7322-4926-a900-462b9d8c1989 · outbound

This paper cites Carla autonomous driving leaderboard.

Adversarial Generation and Collaborative Evolution of Safety-Critical Scenarios for Autonomous Vehicles Carla autonomous driving leaderboard

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:34:49.276363Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T18:34:45.010625Z digest=sha256:d5dd64798da4684fcba35c3d5baa1c7b61da20fd1d1b16e6887483af800ee92a

Observation f67ef720-305a-49c3-bbae-e54b0f0651ea · outbound

This paper cites Qwq-32b: Embracing the power of reinforcement learning, March 2025.

Adversarial Generation and Collaborative Evolution of Safety-Critical Scenarios for Autonomous Vehicles Qwq-32b: Embracing the power of reinforcement learning, March 2025

Reference 46

Resolution
unresolved
no resolver link, observed 2026-08-05T18:34:45.218141Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T18:34:45.218141Z digest=sha256:e40ac203e1b044102b5d3c96489fda42a39aba48b961195a31d275ea92daaceb

Observation 8f8550cb-2001-47c7-bcb7-fe8e86f7ca9c · outbound

This paper cites LMM-enhanced Safety-Critical Scenario Generation for Autonomous Driving System Testing From Non-Accident Traffic Videos.

Adversarial Generation and Collaborative Evolution of Safety-Critical Scenarios for Autonomous Vehicles LMM-enhanced Safety-Critical Scenario Generation for Autonomous Driving System Testing From Non-Accident Traffic Videos

Reference 47

Resolution
unresolved
no resolver link, observed 2026-08-05T18:34:45.310936Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T18:34:45.310936Z digest=sha256:55c3ea83548fc85ce3b6111925ae7a708fca4fbe97065da2c35a1cfffc1a673e

Observation 84a97bc4-6f8d-467b-bef7-d11228f028b2 · outbound

This paper cites Dual attention suppression attack: Generate adversarial camouflage in physical world.

Adversarial Generation and Collaborative Evolution of Safety-Critical Scenarios for Autonomous Vehicles Dual attention suppression attack: Generate adversarial camouflage in physical world

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:34:48.973917Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T18:34:45.403688Z digest=sha256:eb015d6113e815b64c732d89fdd9f89f8d84eaa3d95ee3aa19776870fe7bc9dc

Observation d80ae588-6063-45b1-b679-39b659eed6c8 · outbound

This paper cites Advsim: Generating safety-critical scenarios for self- driving vehicles.

Adversarial Generation and Collaborative Evolution of Safety-Critical Scenarios for Autonomous Vehicles Advsim: Generating safety-critical scenarios for self- driving vehicles

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:34:48.819552Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T18:34:45.502536Z digest=sha256:91f7d8b326ba801560bf3bc55710ecd826b625e041bda7f5a624809760f025d8

Observation 94b9ed52-ca35-4ef5-be22-7f9fdf307a18 · outbound

This paper cites Drive- dreamer: Towards real-world-drive world models for autonomous driving.

Adversarial Generation and Collaborative Evolution of Safety-Critical Scenarios for Autonomous Vehicles Drive- dreamer: Towards real-world-drive world models for autonomous driving

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:34:48.683097Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T18:34:45.595187Z digest=sha256:43259aef9daab7b522d4235420546560361aa0bc98c2376ba71847b5c8c803ea

Observation 53a7e110-388a-4e0d-b0e3-27a2a7d54a38 · outbound

This paper cites Driving into the future: Multiview visual forecasting and planning with world model for autonomous driving.

Adversarial Generation and Collaborative Evolution of Safety-Critical Scenarios for Autonomous Vehicles Driving into the future: Multiview visual forecasting and planning with world model for autonomous driving

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:34:48.545325Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T18:34:45.683098Z digest=sha256:563a8e1cb095b1e065fcfd044c5073e8264cbcde42bf258f78c04348568d39ae

Observation 91f2f2de-1a68-4351-ad9a-e4a49ef85d39 · outbound

This paper cites Limsim: A long-term interactive multi-scenario traffic simulator.

Adversarial Generation and Collaborative Evolution of Safety-Critical Scenarios for Autonomous Vehicles Limsim: A long-term interactive multi-scenario traffic simulator

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:34:48.406424Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T18:34:45.757024Z digest=sha256:455c30bd31c5515d3660db34625e0558cacada8bd8507884176bcc70db48414f

Observation 7edbdc4c-92c5-4bf3-a9aa-955b50fe278b · outbound

This paper cites Retrieval-Augmented Generation for Natural Language Processing: A Survey.

Adversarial Generation and Collaborative Evolution of Safety-Critical Scenarios for Autonomous Vehicles Retrieval-Augmented Generation for Natural Language Processing: A Survey

Reference 53

Resolution
unresolved
no resolver link, observed 2026-08-05T18:34:45.825269Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T18:34:45.825269Z digest=sha256:15c2cc220298452cac92df3d46e54fe8722b44e1288c1120d960bc6409e0e0a5

Observation e8e1dfe1-4a02-4b24-a2b2-bdee745c934d · outbound

This paper cites V2xp-asg: Generating adversarial scenes for vehicle-to-everything perception.

Adversarial Generation and Collaborative Evolution of Safety-Critical Scenarios for Autonomous Vehicles V2xp-asg: Generating adversarial scenes for vehicle-to-everything perception

Reference 54

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:34:48.263040Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T18:34:45.892869Z digest=sha256:f30775261e28799d057b66906a57a11e9719eb4c6b6f241b0fd220f9b6ec51f4

Observation 23f2a497-8778-4e1b-a9f1-32b09dc5c0cb · outbound

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

Adversarial Generation and Collaborative Evolution of Safety-Critical Scenarios for Autonomous Vehicles Safebench: A benchmarking platform for safety evaluation of autonomous vehicles

Reference 55

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:34:48.110163Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T18:34:45.962277Z digest=sha256:febb0d23b3e171be27375347a1f59a439939523d1ae3d64325e5581c51bd508b

Observation b5fdfb75-914d-438a-89b3-422770b84188 · outbound

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

Adversarial Generation and Collaborative Evolution of Safety-Critical Scenarios for Autonomous Vehicles Diffscene: Diffusion-based safety- critical scenario generation for autonomous vehicles

Reference 56

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:34:47.984983Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T18:34:46.061657Z digest=sha256:e76702fe91a1942255bb068dff0aeb3c4f9beece0e81f54fa685ad098535e8ba

Observation e0fcb0e7-fd11-4da1-9b91-3d54c59cdf51 · outbound

This paper cites Interpreting and improving adversarial robustness of deep neural networks with neuron sensitivity.

Adversarial Generation and Collaborative Evolution of Safety-Critical Scenarios for Autonomous Vehicles Interpreting and improving adversarial robustness of deep neural networks with neuron sensitivity

Reference 57

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:34:47.837972Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T18:34:46.132836Z digest=sha256:920eaa67ac52706ab9496d469692e4d6b475b3a8dc90dc666b73e149b02f17c7

Observation 038a94ff-2c71-427f-8ac9-728367880e31 · outbound

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

Adversarial Generation and Collaborative Evolution of Safety-Critical Scenarios for Autonomous Vehicles Chatscene: Knowledge-enabled safety-critical scenario generation for autonomous vehicles

Reference 58

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:34:47.684896Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T18:34:46.200648Z digest=sha256:d6a8c546ba52c9cd12bd4af6d50b24b32ddc6ea2f8438479eb071d5feda3961e

Observation 188336e0-407f-4b33-a64a-31fde142361d · outbound

This paper cites On Adversarial Robustness of Trajectory Prediction for Autonomous Vehicles.

Adversarial Generation and Collaborative Evolution of Safety-Critical Scenarios for Autonomous Vehicles On Adversarial Robustness of Trajectory Prediction for Autonomous Vehicles

Reference 59

Resolution
verified exact
local_arxiv, observed 2026-08-05T18:34:46.736784Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T18:34:46.264908Z digest=sha256:2864507458edea187c661c35f60b0adf67ed3cb67ee757270327a0961b21025b

Observation 99f4fd0b-3b4b-4269-8a6d-a7c862e6d578 · outbound

This paper cites Chat2scenario: Scenario extraction from dataset through utilization of large language model.

Adversarial Generation and Collaborative Evolution of Safety-Critical Scenarios for Autonomous Vehicles Chat2scenario: Scenario extraction from dataset through utilization of large language model

Reference 60

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:34:47.528510Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T18:34:46.354935Z digest=sha256:8507cdebe2dc493b3f1270d9e236c9253392f01c2012153dade1b8da65c39a77

Observation 121cd483-73b4-411e-b28a-92746592e748 · outbound

This paper cites Occworld: Learning a 3d occupancy world model for autonomous driving.

Adversarial Generation and Collaborative Evolution of Safety-Critical Scenarios for Autonomous Vehicles Occworld: Learning a 3d occupancy world model for autonomous driving

Reference 61

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:34:47.388613Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T18:34:46.442897Z digest=sha256:d53f463725d37669ad828dcac6880d604520902a56fe847d3380be68365994c5

Observation c462066c-5864-4057-8c74-555bcf247dd9 · outbound

This paper cites Language-guided traffic simulation via scene-level diffusion.

Adversarial Generation and Collaborative Evolution of Safety-Critical Scenarios for Autonomous Vehicles Language-guided traffic simulation via scene-level diffusion

Reference 62

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:34:47.205059Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T18:34:46.520629Z digest=sha256:036c8ab728615810a23fb309326500023a3b55c2ad668957981b62d41682977d

Observation 9190ca53-dbce-44de-82a1-36703a009ef2 · outbound

This paper cites an unresolved cited work.

Adversarial Generation and Collaborative Evolution of Safety-Critical Scenarios for Autonomous Vehicles Unresolved cited work

Reference 2020

Resolution
parse uncertain
raw_fallback, observed 2026-08-05T18:34:49.134908Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T18:34:45.101787Z digest=sha256:dce9438cf29c5cf228293f9980736ae73e8f2dcb5b597c55d6fb92bf67270ae1

Pith citing papers

Observation e00e8dcf-d17d-4c95-8ad6-540619a1568a · inbound

GuardAD: Safeguarding Autonomous Driving MLLMs via Markovian Safety Logic cites this paper.

GuardAD: Safeguarding Autonomous Driving MLLMs via Markovian Safety Logic Adversarial Generation and Collaborative Evolution of Safety-Critical Scenarios for Autonomous Vehicles

Reference 29

Resolution
verified exact
arxiv_id, observed 2026-05-12T05:21:27.493187Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-05-12T05:17:42.003708Z digest=sha256:04ad46b7d9e099c1a71ae15935a3e5cc6af95f9c0df3be9c5e0226493378cdb0

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

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

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.

source=pdf_text observed=2026-08-06T17:00:14.148454Z digest=sha256:04cc4b7345590c1636465b8661562e2332705c714b6bf9f26d753acfce7915d3