Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-07-09T00:16:03.334057Z
Paper Citation Record · LEDGER
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective
As of 20 August 2026, this Paper Citation Record lists 100 of 131 outbound references and 0 inbound Pith citation observations for arXiv:2607.05783.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
100 of 131 outbound references displayed
- verified exact21
- verified fuzzy78
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch1
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation c7d4f10f-d292-4b9b-ae58-23423af10244 · outbound
This paper cites Death of elaine herzberg,.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective Death of elaine herzberg,
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T00:25:49.106447Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 79740f27-3160-4769-ae63-e73bd4a7480e · outbound
This paper cites Ultra fast structure-aware deep lane detection,.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective Ultra fast structure-aware deep lane detection,
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T00:25:49.030912Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 34ad5a45-aa58-44e6-9003-fe4a59447eb0 · outbound
This paper cites Resa: Recurrent feature-shift aggregator for lane detection,.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective Resa: Recurrent feature-shift aggregator for lane detection,
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T00:25:49.032611Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 7db4cf07-2753-4fc1-a947-060b65a97a1d · outbound
This paper cites Ultra fast deep lane detection with hybrid anchor driven ordinal classification,.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective Ultra fast deep lane detection with hybrid anchor driven ordinal classification,
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T00:25:49.034412Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation f886eb4d-76f7-4d64-92fb-a0199deb4369 · outbound
This paper cites Recent progress in road and lane detection: a survey,.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective Recent progress in road and lane detection: a survey,
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T00:25:49.041490Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation a1dae4a1-34a7-4710-bee4-c951447d0334 · outbound
This paper cites Spatial as deep: Spatial cnn for traffic scene understanding,.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective Spatial as deep: Spatial cnn for traffic scene understanding,
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T00:25:49.023614Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation f423535d-9efb-4286-873d-030a941f543d · outbound
This paper cites Drivelm: Driving with graph visual question answering,.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective Drivelm: Driving with graph visual question answering,
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T00:25:49.020117Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation b83e37f0-e67b-4135-a1d4-4bf6214e8b79 · outbound
This paper cites Benchmarking Neural Network Robustness to Common Corruptions and Perturbations.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective Benchmarking Neural Network Robustness to Common Corruptions and Perturbations
Reference 8
- Resolution
- verified exact
- local_arxiv, observed 2026-07-09T00:25:48.512680Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 65d5b2b5-36b7-4c58-9ad0-ad78c06d21b4 · outbound
This paper cites Benchmarking robustness of 3d object detection to common corruptions,.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective Benchmarking robustness of 3d object detection to common corruptions,
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T00:25:49.018403Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 22411542-b9fb-419e-b3a9-e70acb0ac5c9 · outbound
This paper cites Autonomous vehicle eval- uation: A comprehensive survey on modeling and simulation approaches,.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective Autonomous vehicle eval- uation: A comprehensive survey on modeling and simulation approaches,
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T00:25:49.021831Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 61c44197-f542-4585-9d41-1f78b1b85a83 · outbound
This paper cites A systematic review of perception system and simulators for au- tonomous vehicles research,.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective A systematic review of perception system and simulators for au- tonomous vehicles research,
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T00:25:49.038079Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 27e118c9-082e-4b75-8b0f-3ef539093566 · outbound
This paper cites Formal scenario-based testing of autonomous vehicles: From simulation to the real world,.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective Formal scenario-based testing of autonomous vehicles: From simulation to the real world,
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T00:25:49.046860Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation f1cdde7a-2cbc-4932-aaf8-66d3727c154f · outbound
This paper cites Metadrive: Composing diverse driving scenarios for general- izable reinforcement learning,.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective Metadrive: Composing diverse driving scenarios for general- izable reinforcement learning,
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T00:25:49.048615Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 335e80fe-be51-45cb-bd33-c18f4be15561 · outbound
This paper cites Towards benchmarking and assessing visual naturalness of physical world adversarial attacks,.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective Towards benchmarking and assessing visual naturalness of physical world adversarial attacks,
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T00:25:48.965872Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation dc79a598-617b-48ac-bf75-2b94e22668f7 · outbound
This paper cites Dual attention suppression attack: Generate adversarial camouflage in physical world,.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective Dual attention suppression attack: Generate adversarial camouflage in physical world,
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T00:25:48.981490Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 8aab66f7-ce98-42dd-967b-9c4a2fcf5d49 · outbound
This paper cites Playing for data: Ground truth from computer games,.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective Playing for data: Ground truth from computer games,
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T00:25:48.997928Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 8c5f5af9-e322-4ef2-b27d-e821c0be8563 · outbound
This paper cites Playing for bench- marks,.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective Playing for bench- marks,
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T00:25:48.957185Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 73350eda-6d4b-41ce-90e2-55cc1756f3ce · outbound
This paper cites The omnis- cape dataset,.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective The omnis- cape dataset,
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T00:25:48.952211Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 6b2f2757-3a45-4a06-a6a2-c7335c0b4dbf · outbound
This paper cites Carla: An open urban driving simulator,.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective Carla: An open urban driving simulator,
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T00:25:49.001159Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation b6d587dd-e635-40d1-a15a-ada7a847420b · outbound
This paper cites openpilot,.
Reference 20
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 24b942ec-674b-4c53-9651-191a02a35e85 · outbound
This paper cites Lmdrive: Closed-loop end-to- end driving with large language models,.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective Lmdrive: Closed-loop end-to- end driving with large language models,
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T00:25:49.043208Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 10a3a1f6-cbde-47d9-beaf-e50366d6771a · outbound
This paper cites BadCLIP: Dual-Embedding Guided Backdoor Attack on Multimodal Contrastive Learning.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective BadCLIP: Dual-Embedding Guided Backdoor Attack on Multimodal Contrastive Learning
Reference 22
- Resolution
- verified exact
- local_arxiv, observed 2026-07-09T00:25:48.587433Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 5e72ef86-1e08-4f35-8a3d-0cd74bf40dbd · outbound
This paper cites Pre-trained Trojan Attacks for Visual Recognition.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective Pre-trained Trojan Attacks for Visual Recognition
Reference 23
- Resolution
- verified exact
- local_arxiv, observed 2026-07-09T00:25:48.583592Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation a9142320-e0b2-4a11-8e65-e89c15d62997 · outbound
This paper cites Does Few-shot Learning Suffer from Backdoor Attacks?.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective Does Few-shot Learning Suffer from Backdoor Attacks?
Reference 24
- Resolution
- verified exact
- local_arxiv, observed 2026-07-09T00:25:48.567307Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation d02a7a5e-3402-4179-94ae-e8f50d65d7a7 · outbound
This paper cites Poisoned Forgery Face: Towards Backdoor Attacks on Face Forgery Detection.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective Poisoned Forgery Face: Towards Backdoor Attacks on Face Forgery Detection
Reference 25
- Resolution
- verified exact
- local_arxiv, observed 2026-07-09T00:25:48.559140Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 1ad4bbb2-2fc3-4d56-b3e3-9000442d0959 · outbound
This paper cites VL-Trojan: Multimodal Instruction Backdoor Attacks against Autoregressive Visual Language Models.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective VL-Trojan: Multimodal Instruction Backdoor Attacks against Autoregressive Visual Language Models
Reference 26
- Resolution
- verified exact
- local_arxiv, observed 2026-07-09T00:25:48.570818Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation b071516e-cb8c-4243-b072-58bc25835ee4 · outbound
This paper cites Semantic Mirror Jailbreak: Genetic Algorithm Based Jailbreak Prompts Against Open-source LLMs.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective Semantic Mirror Jailbreak: Genetic Algorithm Based Jailbreak Prompts Against Open-source LLMs
Reference 27
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 87b9e154-7557-46a0-b0cf-3355fdd4edfb · outbound
This paper cites Lanevil: Benchmarking the robustness of lane detection to environmental illusions,.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective Lanevil: Benchmarking the robustness of lane detection to environmental illusions,
Reference 28
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T00:25:49.108216Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation c0030406-8c76-4054-8d98-a111336833ca · outbound
This paper cites Towards end-to-end lane detection: an instance segmentation approach,.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective Towards end-to-end lane detection: an instance segmentation approach,
Reference 29
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T00:25:49.036187Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 46c9de85-f237-48fb-993a-6169862fe456 · outbound
This paper cites Heatmap-based Vanishing Point boosts Lane Detection.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective Heatmap-based Vanishing Point boosts Lane Detection
Reference 30
- Resolution
- verified exact
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 2cbf5fae-5aed-4ee4-81ff-97516535b180 · outbound
This paper cites Key points estimation and point instance segmentation approach for lane detection,.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective Key points estimation and point instance segmentation approach for lane detection,
Reference 31
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T00:25:48.996070Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 6dbb2dff-3fb5-4661-ac60-c8c73a2215b6 · outbound
This paper cites Focus on local: Detecting lane marker from bottom up via key point,.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective Focus on local: Detecting lane marker from bottom up via key point,
Reference 32
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T00:25:49.050372Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 12a29628-91dd-4020-a71c-02f44e61ef3b · outbound
This paper cites A keypoint-based global association network for lane detec- tion,.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective A keypoint-based global association network for lane detec- tion,
Reference 33
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T00:25:48.985562Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation f4e46255-f080-4b36-9e50-d91c5456dcbf · outbound
This paper cites Line-cnn: End-to-end traffic line detection with line proposal unit,.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective Line-cnn: End-to-end traffic line detection with line proposal unit,
Reference 34
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation ae8059ae-e589-445d-aea6-6a63bb87d8f9 · outbound
This paper cites Keep your eyes on the lane: Real-time attention-guided lane detection,.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective Keep your eyes on the lane: Real-time attention-guided lane detection,
Reference 35
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T00:25:48.990726Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation edb7415d-e102-477e-b481-e25a2acda255 · outbound
This paper cites Structure Guided Lane Detection.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective Structure Guided Lane Detection
Reference 36
- Resolution
- verified exact
- local_arxiv, observed 2026-07-09T00:25:48.593511Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation d2e6fd77-44c7-48ca-9890-70a51bb8d73d · outbound
This paper cites Laneformer: Object-aware row-column transformers for lane detection,.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective Laneformer: Object-aware row-column transformers for lane detection,
Reference 37
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T00:25:49.011406Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 2c0b74de-1942-4caf-8494-801503aee2fd · outbound
This paper cites Clrnet: Cross layer refinement network for lane detection,.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective Clrnet: Cross layer refinement network for lane detection,
Reference 38
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T00:25:49.016617Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 5970a6a2-f7f4-4fc1-b52c-952da7ca3e2b · outbound
This paper cites End-to-end lane marker detection via row-wise classification,.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective End-to-end lane marker detection via row-wise classification,
Reference 39
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T00:25:48.999558Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 201ee832-4fb2-4780-b25a-e1e4ca128196 · outbound
This paper cites Inter-region affinity distillation for road marking segmentation,.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective Inter-region affinity distillation for road marking segmentation,
Reference 40
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T00:25:49.109919Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation cd9db20f-286f-4013-ba6b-c5dd6b5e29a9 · outbound
This paper cites Polylanenet: Lane estimation via deep polynomial regression,.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective Polylanenet: Lane estimation via deep polynomial regression,
Reference 41
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T00:25:49.111679Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 33f80527-a0d2-427c-b08c-8055af80dc65 · outbound
This paper cites End-to-end lane shape prediction with transformers,.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective End-to-end lane shape prediction with transformers,
Reference 42
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T00:25:49.097910Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation cbdc9a83-4395-47b2-8deb-dc6d2b2a295c · outbound
This paper cites BSNet: Lane Detection via Draw B-spline Curves Nearby.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective BSNet: Lane Detection via Draw B-spline Curves Nearby
Reference 43
- Resolution
- verified exact
- local_arxiv, observed 2026-07-09T00:25:48.596296Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 546ece54-12d7-4e2e-8b95-f8d90e4b7a7d · outbound
This paper cites Rethinking efficient lane detection via curve modeling,.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective Rethinking efficient lane detection via curve modeling,
Reference 44
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T00:25:49.092512Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 19626401-86d4-4718-a3f6-703a9b2f02b6 · outbound
This paper cites Drive like a human: Rethink- ing autonomous driving with large language models,.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective Drive like a human: Rethink- ing autonomous driving with large language models,
Reference 45
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T00:25:49.080599Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation fca79014-42ae-4580-9ce8-d5807244433f · outbound
This paper cites Gpt-driver: Learning to drive with gpt,.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective Gpt-driver: Learning to drive with gpt,
Reference 46
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T00:25:49.073702Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 32b6eb04-b714-47f0-89c0-0e9230009924 · outbound
This paper cites A Language Agent for Autonomous Driving.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective A Language Agent for Autonomous Driving
Reference 47
- Resolution
- verified exact
- local_arxiv, observed 2026-07-09T00:25:48.579915Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 54b8373b-c512-4c22-a796-7e9bc83e4a95 · outbound
This paper cites Reason2drive: Towards inter- pretable and chain-based reasoning for autonomous driving,.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective Reason2drive: Towards inter- pretable and chain-based reasoning for autonomous driving,
Reference 48
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T00:25:49.066793Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation f3fab79f-6476-475d-8f5b-fec3bbbc51c8 · outbound
This paper cites LingoQA: Visual Question Answering for Autonomous Driving.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective LingoQA: Visual Question Answering for Autonomous Driving
Reference 49
- Resolution
- verified exact
- local_arxiv, observed 2026-07-09T00:25:48.549738Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 97a45ece-37bc-4606-8e29-9b600956a5c2 · outbound
This paper cites Dolphins: Multimodal language model for driving,.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective Dolphins: Multimodal language model for driving,
Reference 50
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T00:25:49.062173Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 0c27a44a-ec9f-4e9b-a832-285881ab7ecf · outbound
This paper cites Driving with llms: Fusing object-level vector modality for explainable autonomous driving,.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective Driving with llms: Fusing object-level vector modality for explainable autonomous driving,
Reference 51
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T00:25:49.075510Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation aef514c8-17f0-4ee6-8cd6-b6d95d9bb664 · outbound
This paper cites Mtd-gpt: A multi-task decision- making gpt model for autonomous driving at unsignalized inter- sections,.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective Mtd-gpt: A multi-task decision- making gpt model for autonomous driving at unsignalized inter- sections,
Reference 52
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T00:25:49.084135Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 5ae456d6-19d3-436b-aa96-a6f83ba4f077 · outbound
This paper cites Drivemlm: Aligning multi-modal large language models with behavioral planning states for au- tonomous driving.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective Drivemlm: Aligning multi-modal large language models with behavioral planning states for au- tonomous driving
Reference 53
- Resolution
- verified exact
- arxiv_id, observed 2026-07-09T00:25:48.515354Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 8a575ea0-be0f-4f07-a596-abe92d5c9226 · outbound
This paper cites Omnidrive: A holistic vision-language dataset for autonomous driving with counterfactual reasoning,.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective Omnidrive: A holistic vision-language dataset for autonomous driving with counterfactual reasoning,
Reference 54
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T00:25:49.058953Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation f66c125c-c424-4aec-a940-d576f1c2dca7 · outbound
This paper cites Drivegpt4: Interpretable end-to-end autonomous driving via large language model,.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective Drivegpt4: Interpretable end-to-end autonomous driving via large language model,
Reference 55
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T00:25:49.099652Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 953fb723-bf4d-401a-b55b-c81cf95b08c1 · outbound
This paper cites Robo3d: Towards robust and reliable 3d perception against corruptions,.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective Robo3d: Towards robust and reliable 3d perception against corruptions,
Reference 56
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T00:25:49.002869Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation a6d3ece2-7ac3-42a2-882b-ee3f6cf7254c · outbound
This paper cites Robodepth: Robust out-of- distribution depth estimation under corruptions,.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective Robodepth: Robust out-of- distribution depth estimation under corruptions,
Reference 57
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T00:25:49.013156Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation e3fd2feb-7aff-4575-9542-6807eb97c02e · outbound
This paper cites Benchmarking and improving bird’s eye view perception robustness in autonomous driving,.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective Benchmarking and improving bird’s eye view perception robustness in autonomous driving,
Reference 58
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T00:25:48.987308Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 59f55f5a-6e58-4dd8-8800-272c098491ea · outbound
This paper cites Real time detection of lane markers in urban streets,.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective Real time detection of lane markers in urban streets,
Reference 59
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T00:25:48.992390Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 3c2a24aa-f82d-4f6b-b9ec-a3e7c5e4b47f · outbound
This paper cites Vpgnet: Vanishing point guided network for lane and road marking detection and recognition,.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective Vpgnet: Vanishing point guided network for lane and road marking detection and recognition,
Reference 60
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T00:25:48.994208Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 76ebf681-8dcf-4c7b-b735-a6007c102bc2 · outbound
This paper cites Tusimple benchmark,.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective Tusimple benchmark,
Reference 61
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T00:25:49.008051Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation afb51683-08a2-4aac-a7c4-05c042d1e8f1 · outbound
This paper cites Bdd100k: A diverse driving dataset for heteroge- neous multitask learning,.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective Bdd100k: A diverse driving dataset for heteroge- neous multitask learning,
Reference 62
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T00:25:49.051993Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 6619c00f-6be0-4557-8f39-0f67d8f45cb0 · outbound
This paper cites Unsupervised labeled lane markers using maps,.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective Unsupervised labeled lane markers using maps,
Reference 63
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T00:25:48.974618Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 83ba2dde-9874-41c9-a920-750411c68a4a · outbound
This paper cites Curvelane-nas: Unifying lane-sensitive architecture search and adaptive point blending.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective Curvelane-nas: Unifying lane-sensitive architecture search and adaptive point blending
Reference 64
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T00:25:48.978100Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 73eefb7a-6c14-4b99-9612-122df9255715 · outbound
This paper cites In- terpreting and improving adversarial robustness of deep neural networks with neuron sensitivity,.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective In- terpreting and improving adversarial robustness of deep neural networks with neuron sensitivity,
Reference 65
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T00:25:48.969462Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 738b2e37-803f-45db-827b-c29a56ed28b6 · outbound
This paper cites A comprehensive evaluation framework for deep model robustness,.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective A comprehensive evaluation framework for deep model robustness,
Reference 66
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T00:25:48.971392Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 94d1652f-46c6-425e-aaa2-608bb0f7a6bd · outbound
This paper cites Spatiotemporal attacks for embodied agents,.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective Spatiotemporal attacks for embodied agents,
Reference 67
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T00:25:48.976253Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation b96db683-5603-4286-82bc-ffc2a3ac79b4 · outbound
This paper cites Robustmq: benchmarking robustness of quantized models,.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective Robustmq: benchmarking robustness of quantized models,
Reference 68
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T00:25:49.053701Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 461ff36f-b19e-43e2-a566-a5de7547de7c · outbound
This paper cites Benchmarking the Robustness of Quantized Models.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective Benchmarking the Robustness of Quantized Models
Reference 69
- Resolution
- verified exact
- local_arxiv, observed 2026-07-09T00:25:48.518094Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation ca26122e-f6eb-49fc-97a0-a1751bb751c9 · outbound
This paper cites Ex- ploring the relationship between architecture and adversarially robust generalization,.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective Ex- ploring the relationship between architecture and adversarially robust generalization,
Reference 70
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T00:25:48.962447Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation e2b0c0cd-acd8-43ac-ad1b-a5a2527a4bfd · outbound
This paper cites Harnessing perceptual adversarial patches for crowd counting,.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective Harnessing perceptual adversarial patches for crowd counting,
Reference 71
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T00:25:48.964105Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 2fd39dea-f8ae-45e2-a04a-cd025ae592a6 · outbound
This paper cites Training robust deep neural networks via adversarial noise propagation,.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective Training robust deep neural networks via adversarial noise propagation,
Reference 72
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T00:25:48.967659Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation e0f8a075-150f-4d2e-b1a8-27e0d4ba7246 · outbound
This paper cites Robustart: Benchmarking robustness on architecture design and training techniques,.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective Robustart: Benchmarking robustness on architecture design and training techniques,
Reference 73
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T00:25:48.960692Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 4bed271e-24fc-4dfa-a387-74f529072331 · outbound
This paper cites Benchmarking the Physical-world Adversarial Robustness of Vehicle Detection.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective Benchmarking the Physical-world Adversarial Robustness of Vehicle Detection
Reference 74
- Resolution
- verified exact
- local_arxiv, observed 2026-07-09T00:25:48.599055Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 63ff69fd-b191-4642-bcba-5872ac64c189 · outbound
This paper cites Exploring the physical-world adversarial robustness of vehicle detection,.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective Exploring the physical-world adversarial robustness of vehicle detection,
Reference 75
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T00:25:48.983221Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 17385217-f8ef-4b80-a6be-95214150c2ee · outbound
This paper cites Towards Robust Physical-world Backdoor Attacks on Lane Detection.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective Towards Robust Physical-world Backdoor Attacks on Lane Detection
Reference 76
- Resolution
- verified exact
- local_arxiv, observed 2026-07-09T00:25:48.528297Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 3f3833b3-b7d7-4f7e-bd39-a3f002605f27 · outbound
This paper cites Attack end-to-end autonomous driving through module-wise noise,.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective Attack end-to-end autonomous driving through module-wise noise,
Reference 77
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T00:25:48.973024Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation a256dd3e-08de-4de2-a74c-fe36eadfb71e · outbound
This paper cites Enhancing the transferability of adversarial attacks with stealth preservation,.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective Enhancing the transferability of adversarial attacks with stealth preservation,
Reference 78
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T00:25:48.953939Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation f82e8e2d-7d6c-4544-bc3f-8af557db7b7a · outbound
This paper cites Generate more imperceptible adversarial examples for object detection,.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective Generate more imperceptible adversarial examples for object detection,
Reference 79
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T00:25:48.955621Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation b9cc6b03-74f5-47f3-883a-55754e5c27b9 · outbound
This paper cites Efficient adversarial attacks for visual object tracking,.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective Efficient adversarial attacks for visual object tracking,
Reference 80
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T00:25:49.068507Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation c4e8d21e-ade7-498d-8918-ef1db1fe391d · outbound
This paper cites Transferable Adversarial Attacks for Image and Video Object Detection.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective Transferable Adversarial Attacks for Image and Video Object Detection
Reference 81
- Resolution
- verified exact
- local_arxiv, observed 2026-07-09T00:25:48.505150Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 95e8162c-6f7f-424b-9b70-e310c96c74a6 · outbound
This paper cites Parallel Rectangle Flip Attack: A Query-based Black-box Attack against Object Detection.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective Parallel Rectangle Flip Attack: A Query-based Black-box Attack against Object Detection
Reference 82
- Resolution
- verified exact
- local_arxiv, observed 2026-07-09T00:25:48.523417Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 4803a7ad-13b6-4856-888d-6e5df6726daf · outbound
This paper cites A large- scale multiple-objective method for black-box attack against ob- ject detection,.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective A large- scale multiple-objective method for black-box attack against ob- ject detection,
Reference 83
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T00:25:48.947058Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 39387ead-628b-4577-b642-37e0c5178ff8 · outbound
This paper cites Diversifying the High-level Features for better Adversarial Transferability.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective Diversifying the High-level Features for better Adversarial Transferability
Reference 84
- Resolution
- verified exact
- local_arxiv, observed 2026-07-09T00:25:48.576297Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 5caa2291-0031-4f82-95bb-7421a27f5ef6 · outbound
This paper cites X-adv: Physical adversarial object attacks against x-ray prohibited item detection,.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective X-adv: Physical adversarial object attacks against x-ray prohibited item detection,
Reference 85
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T00:25:49.009711Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 76bdd345-d8e7-40a9-b24c-30e46693c7df · outbound
This paper cites Generating transferable 3d adversarial point cloud via random perturbation factorization,.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective Generating transferable 3d adversarial point cloud via random perturbation factorization,
Reference 86
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T00:25:49.101394Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 5125cfc2-c200-413d-bfc6-07a0a4c98293 · outbound
This paper cites Improving Adversarial Transferability by Stable Diffusion.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective Improving Adversarial Transferability by Stable Diffusion
Reference 87
- Resolution
- verified exact
- local_arxiv, observed 2026-07-09T00:25:48.520637Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 571cc4c3-5b68-4a4a-8560-666cf13937f5 · outbound
This paper cites SA-Attack: Improving Adversarial Transferability of Vision-Language Pre-training Models via Self-Augmentation.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective SA-Attack: Improving Adversarial Transferability of Vision-Language Pre-training Models via Self-Augmentation
Reference 88
- Resolution
- verified exact
- local_arxiv, observed 2026-07-09T00:25:48.525831Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 1fdac6e8-1edc-432d-9034-cc36ba452e51 · outbound
This paper cites Adversarial instance attacks for interactions between human and object,.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective Adversarial instance attacks for interactions between human and object,
Reference 89
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T00:25:49.096272Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 8bf11ab4-fcea-40c2-9fc7-84715d23b38c · outbound
This paper cites Hide in Thicket: Generating Imperceptible and Rational Adversarial Perturbations on 3D Point Clouds.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective Hide in Thicket: Generating Imperceptible and Rational Adversarial Perturbations on 3D Point Clouds
Reference 90
- Resolution
- verified exact
- local_arxiv, observed 2026-07-09T00:25:48.539959Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation cd43d974-ffe7-4d99-bf59-03ccb6279c90 · outbound
This paper cites Robo3d: Towards robust and reliable 3d perception against corruptions,.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective Robo3d: Towards robust and reliable 3d perception against corruptions,
Reference 91
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T00:25:49.089175Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation f6717075-5fe8-4991-8255-986457da254e · outbound
This paper cites Safebench: A safety evaluation framework for mul- timodal large language models,.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective Safebench: A safety evaluation framework for mul- timodal large language models,
Reference 92
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T00:25:49.116741Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 97ff9322-c7c6-44a6-a333-c51ab2ae3e0c · outbound
This paper cites Detoxi- fying large language models via autoregressive reward guided representation editing,.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective Detoxi- fying large language models via autoregressive reward guided representation editing,
Reference 93
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T00:25:49.057222Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation cc42733f-5ff8-4c9a-9d2e-7179de983d54 · outbound
This paper cites Jail- break vision language models via bi-modal adversarial prompt,.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective Jail- break vision language models via bi-modal adversarial prompt,
Reference 94
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T00:25:49.087521Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation cf8e44a3-0acf-4a15-a8b7-e1d847a644c7 · outbound
This paper cites Pre-trained trojan attacks for visual recogni- tion,.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective Pre-trained trojan attacks for visual recogni- tion,
Reference 95
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T00:25:48.979765Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation b191a20a-5f79-4be3-a861-f55550665027 · outbound
This paper cites Compromising llm driven embodied agents with contextual backdoor attacks,.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective Compromising llm driven embodied agents with contextual backdoor attacks,
Reference 96
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T00:25:49.014890Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation caa3032a-49dc-4cdf-8d5a-a501de11360b · outbound
This paper cites Dirty road can attack: Security of deep learning based automated lane centering under{Physical-World}attack,.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective Dirty road can attack: Security of deep learning based automated lane centering under{Physical-World}attack,
Reference 97
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T00:25:48.948848Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation defc363f-908e-4124-a8e8-fef23862431b · outbound
This paper cites Attacking vision-based perception in end-to-end au- tonomous driving models,.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective Attacking vision-based perception in end-to-end au- tonomous driving models,
Reference 98
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T00:25:48.945366Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 13687ad1-4a9e-4fc9-87bd-bb5416c4e1bf · outbound
This paper cites Scalability in per- ception for autonomous driving: Waymo open dataset,.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective Scalability in per- ception for autonomous driving: Waymo open dataset,
Reference 99
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T00:25:48.940268Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation d7298167-430f-4439-a215-0b0d90e4bba8 · outbound
This paper cites Nuscenes-qa: A multi-modal visual question answering benchmark for autonomous driving scenario,.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective Nuscenes-qa: A multi-modal visual question answering benchmark for autonomous driving scenario,
Reference 100
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T00:25:48.941984Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Pith citing papers
No inbound Pith citation observations are available.