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Paper Citation Record · LEDGER

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

As of 9 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 2026-08-09T06:31:02.800959+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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-05T18:34:40.873066Z digest=sha256:31e64f9cdafd962198783be50680b481b33272747b40fa33cbd662a5e355af3c

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-05T18:34:40.992029Z digest=sha256:1a9d41a4153b039e9a975a0757ac52d5636049041d78940acf13ec300627fae2

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-05T18:34:41.057628Z digest=sha256:0ce6bbe0232baa2d297d23759a3ca04157dfdc36e96d94117e93c74f5e560c46

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-05T18:34:41.166860Z digest=sha256:f110714fbc71631a3774f081559fdeefaccad652a7fe6287e90bff5baebffa0c

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-05T18:34:41.252426Z digest=sha256:735fbd2b6eff72e8dceb4679a38d1c8fa9975964e3187ed100375d4908057d20

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-05T18:34:41.314149Z digest=sha256:b2fc7cd3ff75a29dc822a226da55e45e2058320cf94181f161913676a83cffdd

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-05T18:34:41.394316Z digest=sha256:a7c5d183264c0214395af14439f0f1c426a6ce538553ea2d8e83d1e178afe8a1

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-05T18:34:41.584057Z digest=sha256:b1b5fc29bb2aa5f3293a8de155ee3dafb00b4222e5c941c21454f3a1d2a96b98

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-05T18:34:41.663623Z digest=sha256:3c39cfa0589bce12eea78ac5ba38cb0aba94d5b714bebb8dfcaebc4ceadc31ea

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-05T18:34:41.754166Z digest=sha256:64a2323b20776e3c05a6ab85ef1a6b7357ded62bb1744e0e25ee8fcbe2cd5a88

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-05T18:34:41.877257Z digest=sha256:fcbf2e1ee674ed11b37ceb45644695c06884fba71d9812041539c5b79d4b044f

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-05T18:34:41.973451Z digest=sha256:bdab9ed12760493f4f227368689139dc5a848bc1c84adef051b0807f6e8cdbde

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-05T18:34:42.098942Z digest=sha256:d1f63c04f0ea998160ae2cfabb1753df024c13f6171c992a3977e39f2a33809d

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-05T18:34:42.211317Z digest=sha256:d17cb95eb92baad93411bd0e94d764e8503e602cfde12ccf4c5328fb2a3f36bc

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-05T18:34:42.328398Z digest=sha256:ef99ec4c4e15ef0b6b963765891da84f1cf6ca55d44fe94354528970d2007a16

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-05T18:34:42.451089Z digest=sha256:e7332f69edaa335dafa0e69aa6d17b5ae2b56725a2a09c014a72707b8e5c1287

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-05T18:34:42.606530Z digest=sha256:2c8b43db3bd9fde2e81feb86ec680968099f9e91fa54eedd43bde628338ca171

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-05T18:34:42.909389Z digest=sha256:7bb0c96482eebf8376e23ad4358b4a6e204088cdafe1aa0588ef41b9c6b3aa04

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-05T18:34:43.077059Z digest=sha256:0ddebc33c7e628d4ae8e14745b9d4a321214be90f8201096a9800b6132387f03

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-05T18:34:43.374149Z digest=sha256:65e95a7423d791b0a714fc6e96470c1977cdaac4b8f3bf7b8ed6429e7f48d148

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-05T18:34:43.563730Z digest=sha256:5e60d2ec32f369970854f915c186324a05cb4ca3ce523003402ff5188fade773

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-05T18:34:43.657134Z digest=sha256:833add1596cedbbc1f62d5601f50e27d096e36bfd625170569d8c41cafc9db45

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-05T18:34:43.769703Z digest=sha256:9d90dab8c8e6d2783d8a6feb3d58606f5facfd71d68984701ef4f7cc626f6c6b

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-05T18:34:43.913147Z digest=sha256:a0b2a580d825da2f1e97f5e132b697af4f759b034b142740ffbe512d14f6a2bc

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-05T18:34:43.990814Z digest=sha256:9eac59a3314c36346bd2c5c8195dcfb530de1ee402aa4037e8d83c79771b0cd4

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-05T18:34:44.148533Z digest=sha256:43d523609a79f41d7ebbc50c3ce1bda9b5d29c9058bd4ad40ed6fda3a195e931

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-05T18:34:44.319457Z digest=sha256:8654ed0f628718ee48ea2d0aab8d75144466e0ab33a5fefa2970cc6c068a69aa

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-05T18:34:44.408702Z digest=sha256:3889ff044c7995f8be0b757a34d23e90ce1feb02c9277ebc50a6706c76c977af

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-05T18:34:44.499166Z digest=sha256:05b907248a9f186f6917a4eb23d7eec681d82937684055b00875874860c701e1

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-05T18:34:44.666021Z digest=sha256:c597626e788524c73f22dd05509be5c720c3e25a0e2e266d2ccc2f8bedca1445

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-05T18:34:44.854906Z digest=sha256:dfa420290587be37da8d5a46fa7b6daf22c0ee8482076e1f9acf7f74c342cf55

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-05T18:34:44.919373Z digest=sha256:20a294a5c7ec7ca4344c64525fa3184a9310ba286136424fa32a0d66bca9e924

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-05T18:34:45.010625Z digest=sha256:f7ab5cb52879542496161d8be980647b18aa7edc0c663f3a1eb2d7afb08a383b

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-05T18:34:45.403688Z digest=sha256:3fdf1511d398ece5931430cd3c3e70fd59bff60d1b92563b1c2ff780ef2469b2

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-05T18:34:45.502536Z digest=sha256:9d3dfeea25cf63e602e46bce6edc8dbe8146240f55f3c93c8fe1ee9f843bb387

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-05T18:34:45.595187Z digest=sha256:6b1677c0164f8fcb6c0e8554af5cc8b28d8fdf14f7755a9d41d893f345a7abe9

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-05T18:34:45.683098Z digest=sha256:037646f438cd36273449bed25f04829f10747381285a22d28697928324d58e29

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-05T18:34:45.757024Z digest=sha256:95ec48bf09885285e01f34e2420731588ae88fa6103ed1a01f344e3948c0b6de

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-05T18:34:45.892869Z digest=sha256:3b7c7d9c69250386383e5ea15373f69f5941d5ae6cd32fca6ef2a8c43f36cd0a

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-05T18:34:45.962277Z digest=sha256:a0b57e0243ae9c37fff5087edd25d102fff0694e05cc9df7729ea56274022048

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-05T18:34:46.061657Z digest=sha256:b711a3a85097800cf52253cbdf1856e0c13f73734053e25f103b04bd58506cdc

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-05T18:34:46.132836Z digest=sha256:dcd8cc38c1ac8680c5f52ccd899d00a431a2e711ceec64856f6546e48fec39d1

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-05T18:34:46.200648Z digest=sha256:3bfbb5afad4048412907884e6470da7e8267b50a391346416e5c46929c0d45b7

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-05T18:34:46.264908Z digest=sha256:0279cc79cec1397d143f66d9d81e0df733193424aa20af908eea04e88d27a4b1

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-05T18:34:46.354935Z digest=sha256:39d4d2fe22bb90e51ff2cc4a72d3c7ef10674fd7be3e9a6b1ade84ba12628f55

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-05T18:34:46.442897Z digest=sha256:6a8c1f1a6ee93902da07073a7d71cfdf540a5dbe9c93606c82516c4ca9a6f8d3

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-05T18:34:46.520629Z digest=sha256:3950a8ee50b7d1039a5d3fd316dfdde5077c1d869fd7784e3be316261cfc3023

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-05T18:34:45.101787Z digest=sha256:fcbe1551badcf8be904f83a964d2227762d7d2b66064c3899de7b78cd5a42f57

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 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-05-12T05:17:42.003708Z digest=sha256:731565ceaa8007ec4d8f42472510423e5c004ae25a2f403bd4414941060b644b

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:aac78500840d3d847e5715ba1c4705df388b7b8edd4579165c455f868fba4738