Paper Citation Record · LEDGER

Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies

As of 12 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 1 inbound Pith citation observation for arXiv:2412.03051.

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

pith.paper-citation-record.v1
2412.03051 v1

Coverage vector

measured 42 of 42 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T22:53:16.357358Z

measured 43 of 43 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T10:43:05.730568Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

42 of 42 outbound references displayed

verified exact3
verified fuzzy9
unresolved27
parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b11c8c9c-cb82-4f7a-bbc8-192c26dce07d · outbound

This paper cites What might be the economic implications of autonomous vehicles?.

Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies What might be the economic implications of autonomous vehicles?

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:53:19.191343Z

Source-reported events for the cited work

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Observation 8475f697-ff78-4060-b78c-870f66908606 · outbound

This paper cites Learning naturalistic driving environment with statistical realism,.

Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies Learning naturalistic driving environment with statistical realism,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-11T22:53:16.094171Z

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Observation 90220378-2b7e-4f5b-9683-efd0d818fee5 · outbound

This paper cites Trustworthy safety improvement for autonomous driving using reinforcement learning,.

Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies Trustworthy safety improvement for autonomous driving using reinforcement learning,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-11T22:53:16.112696Z

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Observation e4dbd387-9c19-4e69-99c6-6f964b853ea0 · outbound

This paper cites Towards Robust Decision-Making for Autonomous Driving on Highway,.

Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies Towards Robust Decision-Making for Autonomous Driving on Highway,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-11T22:53:16.118527Z

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Observation 2ece139f-7a54-4b8b-a07d-19a7d8a69d92 · outbound

This paper cites Deep Reinforcement Learning Based Decision -Making Strategy of Autonomous Vehicle in Highway Uncertain Driving Environments,.

Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies Deep Reinforcement Learning Based Decision -Making Strategy of Autonomous Vehicle in Highway Uncertain Driving Environments,

Reference 6

Resolution: verified exact; doi, observed 2026-08-11T22:53:16.491701Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 2fd2b63b-be4b-4618-980e-290fc6710472 · outbound

This paper cites Deep multi -agent reinforcement learning for highway on -ramp merging in mixed traffic,.

Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies Deep multi -agent reinforcement learning for highway on -ramp merging in mixed traffic,

Reference 7

Resolution: metadata mismatch; raw_fallback, observed 2026-08-11T22:53:19.017855Z

Source-reported events for the cited work

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Observation 4e8f9b43-a0c1-4b2d-8411-29915875cee0 · outbound

This paper cites Reinforcement Learning -Based Multi-Lane Cooperative Control for On -Ramp Merging in Mixed - Autonomy Traffic,.

Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies Reinforcement Learning -Based Multi-Lane Cooperative Control for On -Ramp Merging in Mixed - Autonomy Traffic,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-11T22:53:16.136213Z

Source-reported events for the cited work

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Observation ece84379-6a1c-458e-ba49-7c618f6f0696 · outbound

This paper cites On -Ramp Merging for Highway Autonomous Driving: An Application of a New Safety Indicator in Deep Reinforcement Learning,.

Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies On -Ramp Merging for Highway Autonomous Driving: An Application of a New Safety Indicator in Deep Reinforcement Learning,

Reference 9

Resolution: verified exact; doi, observed 2026-08-11T22:53:16.470623Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 3fc8d7dc-b432-4e0f-84ca-4c4d0db32548 · outbound

This paper cites Ensemble Quantile Networks: Uncertainty-Aware Reinforcement Learning with Applications in Autonomous Driving,.

Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies Ensemble Quantile Networks: Uncertainty-Aware Reinforcement Learning with Applications in Autonomous Driving,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-11T22:53:16.148991Z

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Observation 2320ef4b-56d8-4def-95af-96b3691cdba3 · outbound

This paper cites Predictive trajectory planning for autonomous vehicles at intersections using reinforcement learning,.

Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies Predictive trajectory planning for autonomous vehicles at intersections using reinforcement learning,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-11T22:53:16.154952Z

Source-reported events for the cited work

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Observation 5c70ba85-4274-4bd2-83fe-fe2470617e37 · outbound

This paper cites Seeing is not Believing: Robust Reinforcement Learning against Spurious Correlation ,.

Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies Seeing is not Believing: Robust Reinforcement Learning against Spurious Correlation ,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:53:19.174606Z

Source-reported events for the cited work

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Observation c02804fc-3a67-4031-a80d-40b9cc736abc · outbound

This paper cites Targeted Attack on Deep RL-based Autonomous Driving with Learned Visual Patterns,.

Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies Targeted Attack on Deep RL-based Autonomous Driving with Learned Visual Patterns,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-11T22:53:16.168998Z

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Observation 7f7bc2ed-6026-443d-94b7-976cd07d6007 · outbound

This paper cites Adversarial Deep Reinforcement Learning for Improving the Robustness of Multi -agent Autonomous Driving Policies,.

Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies Adversarial Deep Reinforcement Learning for Improving the Robustness of Multi -agent Autonomous Driving Policies,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-11T22:53:16.175311Z

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Observation 697e850e-969d-4b7e-b76c-bba462e933a2 · outbound

This paper cites Deep learning adversarial attacks and defenses in autonomous vehicles: a systematic literature review from a safety perspective,.

Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies Deep learning adversarial attacks and defenses in autonomous vehicles: a systematic literature review from a safety perspective,

Reference 15

Resolution: verified exact; doi, observed 2026-08-11T22:53:16.449293Z

Source-reported events for the cited work

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Observation 76ec5fdf-f239-4e7c-ad14-3307c24aa3f6 · outbound

This paper cites an unresolved cited work.

Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies Unresolved cited work

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-11T22:53:16.187738Z

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Observation f3efb81e-0b83-426f-8f83-8b177a14bb1d · outbound

This paper cites Tactics of Adversarial Attack on Deep Reinforcement Learning Agents,.

Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies Tactics of Adversarial Attack on Deep Reinforcement Learning Agents,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:53:19.156027Z

Source-reported events for the cited work

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Observation 68e4093f-272f-47fe-9c6d-190c7b324994 · outbound

This paper cites ATS -O2A: A state-based adversarial attack strategy on deep reinforcement learning,.

Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies ATS -O2A: A state-based adversarial attack strategy on deep reinforcement learning,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-11T22:53:16.200918Z

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Observation f0786f87-fea2-4387-9212-74e20bb490d5 · outbound

This paper cites Stealthy and Efficient Adversa rial Attacks against Deep Reinforcement Learning,.

Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies Stealthy and Efficient Adversa rial Attacks against Deep Reinforcement Learning,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-11T22:53:16.207960Z

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Observation 90927e0a-0877-4833-a0a1-68f91b9586cc · outbound

This paper cites Attacking Deep Reinforcement Learning with Decoupled Adversarial Policy,.

Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies Attacking Deep Reinforcement Learning with Decoupled Adversarial Policy,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-11T22:53:16.216054Z

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Observation 2f620d3c-7e2c-4126-ae3f-3c97bd542164 · outbound

This paper cites Proximal Policy Optimization Algorithms.

Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies Proximal Policy Optimization Algorithms

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-11T22:53:16.233993Z

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Observation 0013f968-9a90-4688-812b-da5306d64f86 · outbound

This paper cites Microscopic Traffic Simulation using SUMO,.

Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies Microscopic Traffic Simulation using SUMO,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:53:19.137474Z

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Observation 08986940-af2b-4411-9eea-63c699b9cee2 · outbound

This paper cites Recent advances in reinforcement learning-based autonomous driving behavior planning: A survey,.

Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies Recent advances in reinforcement learning-based autonomous driving behavior planning: A survey,

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-11T22:53:16.252995Z

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Observation a23f573c-8bca-43c3-9a49-d82753364ceb · outbound

This paper cites Efficient Deep Reinforcement Learning with Imitative Expert Priors for Autonomous Driving,.

Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies Efficient Deep Reinforcement Learning with Imitative Expert Priors for Autonomous Driving,

Reference 24

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Observation 166b7973-7771-4e74-91eb-e4bb434e362d · outbound

This paper cites Boosting Offline Reinforcement Learning for Autonomous Driving with Hierarchical Latent Skills,.

Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies Boosting Offline Reinforcement Learning for Autonomous Driving with Hierarchical Latent Skills,

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-11T22:53:16.264414Z

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Observation 03a06724-96d3-4aed-ba24-e47564bdca2d · outbound

This paper cites Event-Triggered Model Predictive Control With Deep Reinforcement Learning for Autonomous Driving,.

Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies Event-Triggered Model Predictive Control With Deep Reinforcement Learning for Autonomous Driving,

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-11T22:53:16.269465Z

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Observation d69cc415-15a6-44fd-9cc3-ff8182724ff6 · outbound

This paper cites End-to-end Autonomous Driving: Challenges and Frontiers,.

Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies End-to-end Autonomous Driving: Challenges and Frontiers,

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-11T22:53:16.274775Z

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Observation b94efaa3-83ac-4c9d-84e9-a27aff117633 · outbound

This paper cites An Analysis of Adversarial Attacks and Defenses on Autonomous Driving Models,.

Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies An Analysis of Adversarial Attacks and Defenses on Autonomous Driving Models,

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-11T22:53:16.280039Z

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Observation 51bba38a-5151-4444-b47b-19db329a3b69 · outbound

This paper cites Robust Decision Making for Autonomous Vehicles at Highway On -Ramps: A Constrained Adversarial Reinforcement Learning Approach,.

Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies Robust Decision Making for Autonomous Vehicles at Highway On -Ramps: A Constrained Adversarial Reinforcement Learning Approach,

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-11T22:53:16.285723Z

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Observation fa4a3b44-f42a-42a0-9dc9-3d27282699ca · outbound

This paper cites Explainable Deep Adversaria l Reinforcement Learning Approach for Robust Autonomous Driving,.

Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies Explainable Deep Adversaria l Reinforcement Learning Approach for Robust Autonomous Driving,

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-11T22:53:16.291254Z

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Observation 7d91819c-14b7-4938-bcc6-cb295ab2142b · outbound

This paper cites Adversarial Stress Test for Autonomous Vehicle Via Series Reinforcement Learning Tasks With Reward Shaping,.

Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies Adversarial Stress Test for Autonomous Vehicle Via Series Reinforcement Learning Tasks With Reward Shaping,

Reference 31

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Source-reported events for the cited work

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Observation b01b521c-83a0-4337-ae62-6b7272b1c512 · outbound

This paper cites CRASH: Challenging Reinforcement-Learning Based Adversarial Scenarios For Safety Hardening.

Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies CRASH: Challenging Reinforcement-Learning Based Adversarial Scenarios For Safety Hardening

Reference 32

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Observation bba7b09d-34f6-459e-ae71-806b0d88bf02 · outbound

This paper cites Robust Lane Change Decision Making for Autonomous Veh icles: An Observation Adversarial Reinforcement Learning Approach,.

Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies Robust Lane Change Decision Making for Autonomous Veh icles: An Observation Adversarial Reinforcement Learning Approach,

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-11T22:53:16.307313Z

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Observation af53e424-1c21-4d1c-a6d7-1631a87162f7 · outbound

This paper cites Improved Robustness and Safety for Auton omous Vehicle Control with Adversarial Reinforcement Learning,.

Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies Improved Robustness and Safety for Auton omous Vehicle Control with Adversarial Reinforcement Learning,

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-11T22:53:16.312293Z

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Observation 396b8c9a-323d-4ff7-a7b2-943fe0ce3e19 · outbound

This paper cites Stealthy Black- Box Attack With Dynamic Threshold Against MARL -Based Traffic Signal Control System,.

Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies Stealthy Black- Box Attack With Dynamic Threshold Against MARL -Based Traffic Signal Control System,

Reference 35

Resolution: metadata mismatch; raw_fallback, observed 2026-08-11T22:53:16.823907Z

Source-reported events for the cited work

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Observation 8f47f27e-5c6e-440e-998f-cce39ca3c06a · outbound

This paper cites Energy- Constrained Safe Path Planning for UAV -Assisted Data C ollection of Mobile IoT Devices,.

Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies Energy- Constrained Safe Path Planning for UAV -Assisted Data C ollection of Mobile IoT Devices,

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-11T22:53:16.321838Z

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Observation b6bb50bb-5285-4f88-8ff8-aea66b02184c · outbound

This paper cites Soft Actor-Critic: Off- Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor,.

Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies Soft Actor-Critic: Off- Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:53:19.119487Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 3913350e-6b21-4e39-b462-316659879542 · outbound

This paper cites Addressing Function Approximation Error in Actor-Critic Methods,.

Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies Addressing Function Approximation Error in Actor-Critic Methods,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:53:19.099487Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation ea5bcd94-1ed1-4fd9-840e-c91062dbd8c9 · outbound

This paper cites Fear -Neuro-Inspired Reinforcement Learning for Safe Autonomous Driving,.

Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies Fear -Neuro-Inspired Reinforcement Learning for Safe Autonomous Driving,

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-11T22:53:16.337257Z

Source-reported events for the cited work

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Observation 47118770-75cf-4d46-8811-14d3ae18e1df · outbound

This paper cites Stable -baselines3: Reliable reinforcement learning implementations,.

Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies Stable -baselines3: Reliable reinforcement learning implementations,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:53:19.080056Z

Source-reported events for the cited work

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Observation d354080e-6296-458b-8dc8-cded3cb2e7dc · outbound

This paper cites Explaining and Harnessing Adversarial Examples,.

Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies Explaining and Harnessing Adversarial Examples,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:53:19.059758Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 6c563cd6-9b94-438a-9bbd-cd08a2072939 · outbound

This paper cites Towards Deep Learning Models Resistant to Adversarial Attacks,.

Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies Towards Deep Learning Models Resistant to Adversarial Attacks,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:53:19.037640Z

Source-reported events for the cited work

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Observation 254a6498-93ae-4a85-a27e-f57c2cb4b430 · outbound

This paper cites an unresolved cited work.

Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies Unresolved cited work

Reference 2582

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Pith citing papers

Observation 7e87388b-3d36-4dd8-b891-c3a48ce72d77 · inbound

Robust Driving Control for Autonomous Vehicles: An Intelligent General-sum Constrained Adversarial Reinforcement Learning Approach cites this paper.

Robust Driving Control for Autonomous Vehicles: An Intelligent General-sum Constrained Adversarial Reinforcement Learning Approach Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-04T10:43:05.730568Z

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