Paper Citation Record · LEDGER

Driving the Wrong Way: Leveraging Interpretability in End2End Autonomous Driving Models

As of 20 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 0 inbound Pith citation observations for arXiv:2607.06328.

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

pith.paper-citation-record.v1
2607.06328 v1

Coverage vector

measured 40 of 40 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-08T09:50:33.546013Z

measured 40 of 40 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

40 of 40 outbound references displayed

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verified fuzzy40
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f0eca81d-0a6f-4042-be75-49d3b9bcb894 · outbound

This paper cites End-to-end autonomous driving: Challenges and frontiers.

Driving the Wrong Way: Leveraging Interpretability in End2End Autonomous Driving Models End-to-end autonomous driving: Challenges and frontiers

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T09:54:51.093360Z

Source-reported events for the cited work

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Observation 82d7a028-36eb-416c-8c31-24fb93701f00 · outbound

This paper cites Navsim: Data-driven non-reactive autonomous vehicle simulation and benchmarking,.

Driving the Wrong Way: Leveraging Interpretability in End2End Autonomous Driving Models Navsim: Data-driven non-reactive autonomous vehicle simulation and benchmarking,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T09:54:51.099395Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7708c0ee-7d6b-457c-a08e-d20b17038c69 · outbound

This paper cites Pseudo-simulation for autonomous driving,.

Driving the Wrong Way: Leveraging Interpretability in End2End Autonomous Driving Models Pseudo-simulation for autonomous driving,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T09:54:51.208193Z

Source-reported events for the cited work

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Observation 9da10b6c-0633-42c3-9180-0b97610f9d69 · outbound

This paper cites Explainable ai for safe and trustworthy autonomous driving: A system- atic review,.

Driving the Wrong Way: Leveraging Interpretability in End2End Autonomous Driving Models Explainable ai for safe and trustworthy autonomous driving: A system- atic review,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T09:54:51.158874Z

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Observation fb598be9-1cdf-4e69-b6dc-c8c9d8cfd9f2 · outbound

This paper cites Safety implications of explainable artificial intelligence in end-to-end autonomous driving,.

Driving the Wrong Way: Leveraging Interpretability in End2End Autonomous Driving Models Safety implications of explainable artificial intelligence in end-to-end autonomous driving,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T09:54:51.144696Z

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Observation c4308ca5-9407-4999-a374-cf4f7968b36e · outbound

This paper cites Hydra-mdp: End-to-end multimodal planning with multi-target hydra-distillation,.

Driving the Wrong Way: Leveraging Interpretability in End2End Autonomous Driving Models Hydra-mdp: End-to-end multimodal planning with multi-target hydra-distillation,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T09:54:51.199609Z

Source-reported events for the cited work

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Observation fb313d47-303c-4f92-bee1-4d7a36886dd9 · outbound

This paper cites EMMA: End-to-end multimodal model for autonomous driving,.

Driving the Wrong Way: Leveraging Interpretability in End2End Autonomous Driving Models EMMA: End-to-end multimodal model for autonomous driving,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T09:54:51.167384Z

Source-reported events for the cited work

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Observation c3527ec6-e0a0-446a-9e66-0db9bf367e67 · outbound

This paper cites Vadv2: End-to-end vectorized autonomous driving via probabilistic planning,.

Driving the Wrong Way: Leveraging Interpretability in End2End Autonomous Driving Models Vadv2: End-to-end vectorized autonomous driving via probabilistic planning,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T09:54:51.152124Z

Source-reported events for the cited work

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Observation b5fb47d4-3c9f-4c0a-9b13-9b8f242443e2 · outbound

This paper cites Generalized trajectory scoring for end-to-end multimodal planning,.

Driving the Wrong Way: Leveraging Interpretability in End2End Autonomous Driving Models Generalized trajectory scoring for end-to-end multimodal planning,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T09:54:51.225396Z

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 2fb45d95-4f6a-4404-91c5-17ad55c6459a · outbound

This paper cites ipad: Iterative proposal- centric end-to-end autonomous driving,.

Driving the Wrong Way: Leveraging Interpretability in End2End Autonomous Driving Models ipad: Iterative proposal- centric end-to-end autonomous driving,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T09:54:51.222218Z

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 9a1c97d9-61b1-450c-9674-0eeb263968bf · outbound

This paper cites Explaining how a deep neural network trained with end-to-end learning steers a car,.

Driving the Wrong Way: Leveraging Interpretability in End2End Autonomous Driving Models Explaining how a deep neural network trained with end-to-end learning steers a car,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T09:54:51.119586Z

Source-reported events for the cited work

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Observation 72b54914-8f6a-457e-add9-d2a22104173c · outbound

This paper cites Visualbackprop: Efficient visual- ization of cnns for autonomous driving,.

Driving the Wrong Way: Leveraging Interpretability in End2End Autonomous Driving Models Visualbackprop: Efficient visual- ization of cnns for autonomous driving,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T09:54:51.086124Z

Source-reported events for the cited work

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Observation ba4d9b21-abb7-4bad-abaa-872589784c17 · outbound

This paper cites Interpretable learning for self-driving cars by visualizing causal attention,.

Driving the Wrong Way: Leveraging Interpretability in End2End Autonomous Driving Models Interpretable learning for self-driving cars by visualizing causal attention,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T09:54:51.089556Z

Source-reported events for the cited work

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Observation f4115ffa-436c-4618-8adb-1c978feb3bf4 · outbound

This paper cites Conditional affordance learn- ing for driving in urban environments,.

Driving the Wrong Way: Leveraging Interpretability in End2End Autonomous Driving Models Conditional affordance learn- ing for driving in urban environments,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T09:54:51.116685Z

Source-reported events for the cited work

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Observation 427f885b-af9d-435e-828e-10f584ebf441 · outbound

This paper cites Interpretable self-attention temporal reasoning for driving behavior understanding,.

Driving the Wrong Way: Leveraging Interpretability in End2End Autonomous Driving Models Interpretable self-attention temporal reasoning for driving behavior understanding,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T09:54:51.163290Z

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 ac9b7f76-508e-4dff-ad8d-9ac11b692372 · outbound

This paper cites Leveraging driver attention for an end-to-end explainable decision-making from frontal images,.

Driving the Wrong Way: Leveraging Interpretability in End2End Autonomous Driving Models Leveraging driver attention for an end-to-end explainable decision-making from frontal images,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T09:54:51.194628Z

Source-reported events for the cited work

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Observation 4d490ff2-e6bc-4340-b4ac-c8bd11f67a57 · outbound

This paper cites Attentional bottleneck: Towards an interpretable deep driving network,.

Driving the Wrong Way: Leveraging Interpretability in End2End Autonomous Driving Models Attentional bottleneck: Towards an interpretable deep driving network,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T09:54:51.122582Z

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Observation 4ccf2c80-c7b4-48a8-93db-2ef83a9c780f · outbound

This paper cites What matters for scalable and robust learning in end-to-end driving planners?.

Driving the Wrong Way: Leveraging Interpretability in End2End Autonomous Driving Models What matters for scalable and robust learning in end-to-end driving planners?

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T09:54:51.125912Z

Source-reported events for the cited work

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Observation 719f5a21-9f78-4375-962e-6c2793f9dbb3 · outbound

This paper cites Interpretable decision-making for end- to-end autonomous driving,.

Driving the Wrong Way: Leveraging Interpretability in End2End Autonomous Driving Models Interpretable decision-making for end- to-end autonomous driving,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T09:54:51.102850Z

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 7ee47700-39d7-4b7f-b2e4-b32c8560691a · outbound

This paper cites Simultaneous policy learning and latent state inference for imitating driver behavior,.

Driving the Wrong Way: Leveraging Interpretability in End2End Autonomous Driving Models Simultaneous policy learning and latent state inference for imitating driver behavior,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T09:54:51.077550Z

Source-reported events for the cited work

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Observation d5738756-719d-4565-9fa7-03db50efd649 · outbound

This paper cites Deeptest: automated testing of deep-neural-network-driven autonomous cars,.

Driving the Wrong Way: Leveraging Interpretability in End2End Autonomous Driving Models Deeptest: automated testing of deep-neural-network-driven autonomous cars,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T09:54:51.174036Z

Source-reported events for the cited work

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Observation ad538a4c-40e6-40e4-9ec3-06cb4b65ff2f · outbound

This paper cites St-p3: End-to-end vision-based autonomous driving via spatial-temporal feature learning,.

Driving the Wrong Way: Leveraging Interpretability in End2End Autonomous Driving Models St-p3: End-to-end vision-based autonomous driving via spatial-temporal feature learning,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T09:54:51.112574Z

Source-reported events for the cited work

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Observation 77e55bbe-3962-4919-927e-d3f59535d72c · outbound

This paper cites Autovla: A vision-language-action model for end-to-end autonomous driving with adaptive reasoning and reinforcement fine-tuning,.

Driving the Wrong Way: Leveraging Interpretability in End2End Autonomous Driving Models Autovla: A vision-language-action model for end-to-end autonomous driving with adaptive reasoning and reinforcement fine-tuning,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T09:54:51.148095Z

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 79a3bed7-77b3-47d6-86ce-2b3e90d00f3a · outbound

This paper cites Opendrivevla: Towards end-to-end autonomous driving with large vision language action model.

Driving the Wrong Way: Leveraging Interpretability in End2End Autonomous Driving Models Opendrivevla: Towards end-to-end autonomous driving with large vision language action model

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T09:54:51.141274Z

Source-reported events for the cited work

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Observation b35c88e8-302c-41da-aa28-ee91c5ba11b3 · outbound

This paper cites Scaling monosemanticity: Extracting interpretable features from claude 3 sonnet.

Driving the Wrong Way: Leveraging Interpretability in End2End Autonomous Driving Models Scaling monosemanticity: Extracting interpretable features from claude 3 sonnet

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T09:54:51.138312Z

Source-reported events for the cited work

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Observation 65bee496-e1b1-4961-8739-13195e17e67c · outbound

This paper cites Daujotas.

Driving the Wrong Way: Leveraging Interpretability in End2End Autonomous Driving Models Daujotas

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T09:54:51.095892Z

Source-reported events for the cited work

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Observation 36920360-07e6-4bac-8f20-7ceb996839dd · outbound

This paper cites Sparse autoencoders reveal selective remapping of visual concepts during adaptation.

Driving the Wrong Way: Leveraging Interpretability in End2End Autonomous Driving Models Sparse autoencoders reveal selective remapping of visual concepts during adaptation

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T09:54:51.212711Z

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Observation 35974e11-0717-43f9-b53c-eeaaef98fc8f · outbound

This paper cites Can i trust my trajectory prediction model?.

Driving the Wrong Way: Leveraging Interpretability in End2End Autonomous Driving Models Can i trust my trajectory prediction model?

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T09:54:51.217355Z

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Observation a40769ea-b606-46a0-8b82-1c98a6eff528 · outbound

This paper cites Toy models of superposition,.

Driving the Wrong Way: Leveraging Interpretability in End2End Autonomous Driving Models Toy models of superposition,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T09:54:51.128812Z

Source-reported events for the cited work

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Observation 006c12cb-1308-410b-b757-39672bfd3caa · outbound

This paper cites Towards monosemanticity: Decomposing language mod- els with dictionary learning,.

Driving the Wrong Way: Leveraging Interpretability in End2End Autonomous Driving Models Towards monosemanticity: Decomposing language mod- els with dictionary learning,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T09:54:51.191066Z

Source-reported events for the cited work

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Observation a5bfc634-8372-40a0-809a-fba8cb2f7a33 · outbound

This paper cites Sparse autoencoders find highly interpretable features in language models.

Driving the Wrong Way: Leveraging Interpretability in End2End Autonomous Driving Models Sparse autoencoders find highly interpretable features in language models

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T09:54:51.132392Z

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Observation 9231ea36-fdda-4332-ac28-4ca225abec0f · outbound

This paper cites Scaling and evaluating sparse autoencoders.

Driving the Wrong Way: Leveraging Interpretability in End2End Autonomous Driving Models Scaling and evaluating sparse autoencoders

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T09:54:51.135303Z

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Observation 6eb8f50d-419d-4a73-aafe-9143a96ce707 · outbound

This paper cites Learning multi-level features with matryoshka sparse autoencoders,.

Driving the Wrong Way: Leveraging Interpretability in End2End Autonomous Driving Models Learning multi-level features with matryoshka sparse autoencoders,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T09:54:51.183885Z

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 f9aa4402-542a-41ea-bb51-21074194025f · outbound

This paper cites Archetypal SAE: Adaptive and stable dictionary learning for concept extraction in large vision models,.

Driving the Wrong Way: Leveraging Interpretability in End2End Autonomous Driving Models Archetypal SAE: Adaptive and stable dictionary learning for concept extraction in large vision models,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T09:54:51.178193Z

Source-reported events for the cited work

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Observation b6ce5983-b61c-476d-ab7e-c8054705f130 · outbound

This paper cites Mechanistic?.

Driving the Wrong Way: Leveraging Interpretability in End2End Autonomous Driving Models Mechanistic?

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T09:54:51.155784Z

Source-reported events for the cited work

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Observation 4e641e74-cf4a-4680-afb7-8ffe6f8f56a5 · outbound

This paper cites Sparse feature circuits: Discovering and editing interpretable causal graphs in language models.

Driving the Wrong Way: Leveraging Interpretability in End2End Autonomous Driving Models Sparse feature circuits: Discovering and editing interpretable causal graphs in language models

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T09:54:51.080478Z

Source-reported events for the cited work

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Observation bf9db557-4e34-4fbc-9218-a13450df33a3 · outbound

This paper cites Feature visualization,.

Driving the Wrong Way: Leveraging Interpretability in End2End Autonomous Driving Models Feature visualization,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T09:54:51.083134Z

Source-reported events for the cited work

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Observation ed0efce0-f256-4825-ae16-ad933581c861 · outbound

This paper cites From attribution maps to human-understandable explanations through concept relevance propagation,.

Driving the Wrong Way: Leveraging Interpretability in End2End Autonomous Driving Models From attribution maps to human-understandable explanations through concept relevance propagation,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T09:54:51.204492Z

Source-reported events for the cited work

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Observation ff0f7d75-e2d9-42f2-b67c-b343cbeaa91b · outbound

This paper cites On pixel-wise explanations for non-linear classifier deci- sions by layer-wise relevance propagation,.

Driving the Wrong Way: Leveraging Interpretability in End2End Autonomous Driving Models On pixel-wise explanations for non-linear classifier deci- sions by layer-wise relevance propagation,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T09:54:51.109359Z

Source-reported events for the cited work

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Observation aa9a6bf7-822f-407e-8984-81391c064269 · outbound

This paper cites Into the rabbit hull: From task-relevant concepts in DINO to minkowski geometry,.

Driving the Wrong Way: Leveraging Interpretability in End2End Autonomous Driving Models Into the rabbit hull: From task-relevant concepts in DINO to minkowski geometry,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T09:54:51.106243Z

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

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