Enabling Federated Object Detection for Connected Autonomous Vehicles: A Deployment-Oriented Evaluation | Paper Citation Record

Observation ee346929-0e2b-4ad9-9a54-6977e9be43da · outbound

This paper cites A survey of autonomous driving: Common practices and emerging technologies,.

Enabling Federated Object Detection for Connected Autonomous Vehicles: A Deployment-Oriented Evaluation A survey of autonomous driving: Common practices and emerging technologies,

Reference 1

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Observation ddd5f7ec-7b00-4f44-9015-5e8ff368ac8b · outbound

This paper cites A survey of autonomous driving from a deep learning perspective,.

Enabling Federated Object Detection for Connected Autonomous Vehicles: A Deployment-Oriented Evaluation A survey of autonomous driving from a deep learning perspective,

Reference 2

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This paper cites Toward ensuring safety for autonomous driving perception: Standardization progress, research advances, and perspectives,.

Enabling Federated Object Detection for Connected Autonomous Vehicles: A Deployment-Oriented Evaluation Toward ensuring safety for autonomous driving perception: Standardization progress, research advances, and perspectives,

Reference 3

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This paper cites Autonomous vehicles perception (avp) using deep learning: Modeling, assessment, and challenges,.

Enabling Federated Object Detection for Connected Autonomous Vehicles: A Deployment-Oriented Evaluation Autonomous vehicles perception (avp) using deep learning: Modeling, assessment, and challenges,

Reference 4

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This paper cites Federated learning for internet of things: A comprehensive survey,.

Enabling Federated Object Detection for Connected Autonomous Vehicles: A Deployment-Oriented Evaluation Federated learning for internet of things: A comprehensive survey,

Reference 5

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This paper cites A secure personalized federated learning algorithm for autonomous driving,.

Enabling Federated Object Detection for Connected Autonomous Vehicles: A Deployment-Oriented Evaluation A secure personalized federated learning algorithm for autonomous driving,

Reference 6

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This paper cites Federated learning for connected and automated vehicles: A survey of existing approaches and challenges,.

Enabling Federated Object Detection for Connected Autonomous Vehicles: A Deployment-Oriented Evaluation Federated learning for connected and automated vehicles: A survey of existing approaches and challenges,

Reference 7

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Observation 02d08ad4-fac5-4da2-a7f9-95ef79731099 · outbound

This paper cites A survey on federated learning in intelligent transportation systems,.

Enabling Federated Object Detection for Connected Autonomous Vehicles: A Deployment-Oriented Evaluation A survey on federated learning in intelligent transportation systems,

Reference 8

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This paper cites A survey on federated learning for resource-constrained iot devices,.

Enabling Federated Object Detection for Connected Autonomous Vehicles: A Deployment-Oriented Evaluation A survey on federated learning for resource-constrained iot devices,

Reference 9

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Observation 0bb54dc8-a049-40a9-b145-22a0c0fbc921 · outbound

This paper cites Federated learning in vehicular networks,.

Enabling Federated Object Detection for Connected Autonomous Vehicles: A Deployment-Oriented Evaluation Federated learning in vehicular networks,

Reference 10

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This paper cites Client selection for federated learning in vehicular edge computing: A deep reinforcement learning approach,.

Enabling Federated Object Detection for Connected Autonomous Vehicles: A Deployment-Oriented Evaluation Client selection for federated learning in vehicular edge computing: A deep reinforcement learning approach,

Reference 11

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This paper cites Federated learning with non-iid data: A survey,.

Enabling Federated Object Detection for Connected Autonomous Vehicles: A Deployment-Oriented Evaluation Federated learning with non-iid data: A survey,

Reference 12

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This paper cites Real-time Traffic Object Detection for Autonomous Driving.

Enabling Federated Object Detection for Connected Autonomous Vehicles: A Deployment-Oriented Evaluation Real-time Traffic Object Detection for Autonomous Driving

Reference 13

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This paper cites Squeezedet: Unified, small, low power fully convolutional neural networks for real-time object detection for autonomous driving,.

Enabling Federated Object Detection for Connected Autonomous Vehicles: A Deployment-Oriented Evaluation Squeezedet: Unified, small, low power fully convolutional neural networks for real-time object detection for autonomous driving,

Reference 14

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This paper cites A review and comparative study on probabilistic object detection in autonomous driv- ing,.

Enabling Federated Object Detection for Connected Autonomous Vehicles: A Deployment-Oriented Evaluation A review and comparative study on probabilistic object detection in autonomous driv- ing,

Reference 15

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Observation 560011f8-f8c3-4b10-ab29-8b9e3f04b65a · outbound

This paper cites A deep learning-based hybrid framework for object detection and recognition in autonomous driving,.

Enabling Federated Object Detection for Connected Autonomous Vehicles: A Deployment-Oriented Evaluation A deep learning-based hybrid framework for object detection and recognition in autonomous driving,

Reference 16

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Observation c695a08c-ec66-4077-805a-a4acb8aa9a03 · outbound

This paper cites Toward performing image classification and object detection with convolutional neural networks in autonomous driving systems: A survey,.

Enabling Federated Object Detection for Connected Autonomous Vehicles: A Deployment-Oriented Evaluation Toward performing image classification and object detection with convolutional neural networks in autonomous driving systems: A survey,

Reference 17

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This paper cites Navigating the yolo landscape: A comparative study of object detection models for emotion recognition,.

Enabling Federated Object Detection for Connected Autonomous Vehicles: A Deployment-Oriented Evaluation Navigating the yolo landscape: A comparative study of object detection models for emotion recognition,

Reference 18

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Observation 1872a74b-8771-4553-97db-61fe9404c818 · outbound

This paper cites Deformable DETR: Deformable Transformers for End-to-End Object Detection.

Enabling Federated Object Detection for Connected Autonomous Vehicles: A Deployment-Oriented Evaluation Deformable DETR: Deformable Transformers for End-to-End Object Detection

Reference 19

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Observation 88941e77-81e7-4a2e-8510-f174e6fa25a9 · outbound

This paper cites Hydraspace: computational data storage for autonomous vehicles,.

Enabling Federated Object Detection for Connected Autonomous Vehicles: A Deployment-Oriented Evaluation Hydraspace: computational data storage for autonomous vehicles,

Reference 20

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Observation e0ef81a9-f220-4ca9-bde8-700042c94888 · outbound

This paper cites Feasibility of 5G mm-wave communication for connected autonomous vehicles.

Enabling Federated Object Detection for Connected Autonomous Vehicles: A Deployment-Oriented Evaluation Feasibility of 5G mm-wave communication for connected autonomous vehicles

Reference 21

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Observation 4af508c0-47a6-4d40-8cdb-b009fa8968ec · outbound

This paper cites Data storage system requirement for autonomous vehicle,.

Enabling Federated Object Detection for Connected Autonomous Vehicles: A Deployment-Oriented Evaluation Data storage system requirement for autonomous vehicle,

Reference 22

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Observation 09bb3f87-0a1b-4788-9a88-344db4a4df1a · outbound

This paper cites Personalized Federated Learning of Driver Prediction Models for Autonomous Driving.

Enabling Federated Object Detection for Connected Autonomous Vehicles: A Deployment-Oriented Evaluation Personalized Federated Learning of Driver Prediction Models for Autonomous Driving

Reference 23

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Observation f477ac16-dc9e-4ad1-bc0c-9b8a68c84549 · outbound

This paper cites Privacy-preserved federated learning for autonomous driving,.

Enabling Federated Object Detection for Connected Autonomous Vehicles: A Deployment-Oriented Evaluation Privacy-preserved federated learning for autonomous driving,

Reference 24

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Observation 53e1ee0f-6763-47da-bbc7-dc8377b8a0d6 · outbound

This paper cites Privacy and bias in edge computing,.

Enabling Federated Object Detection for Connected Autonomous Vehicles: A Deployment-Oriented Evaluation Privacy and bias in edge computing,

Reference 25

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Observation 57cf37cf-222c-4ae2-812e-fef3a7e0b7c6 · outbound

This paper cites An incentive mechanism of incorporating supervision game for federated learning in autonomous driving,.

Enabling Federated Object Detection for Connected Autonomous Vehicles: A Deployment-Oriented Evaluation An incentive mechanism of incorporating supervision game for federated learning in autonomous driving,

Reference 26

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Observation fc69624c-c68c-42ec-a9fc-eacee9cf030b · outbound

This paper cites Adaptive segmentation enhanced asynchronous federated learning for sustainable intelligent transportation systems,.

Enabling Federated Object Detection for Connected Autonomous Vehicles: A Deployment-Oriented Evaluation Adaptive segmentation enhanced asynchronous federated learning for sustainable intelligent transportation systems,

Reference 27

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This paper cites Federated and asynchronized learning for autonomous and intelligent things,.

Enabling Federated Object Detection for Connected Autonomous Vehicles: A Deployment-Oriented Evaluation Federated and asynchronized learning for autonomous and intelligent things,

Reference 28

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Observation cbd3b0cb-e56a-4bda-933d-05c1be2b01b7 · outbound

This paper cites Large model- assisted federated learning for object detection of autonomous vehicles in edge,.

Enabling Federated Object Detection for Connected Autonomous Vehicles: A Deployment-Oriented Evaluation Large model- assisted federated learning for object detection of autonomous vehicles in edge,

Reference 29

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Observation d2e585fc-ff3d-4f9c-a397-6a59121841d7 · outbound

This paper cites Parameter-efficient federated cooperative learning for 3d object detection in autonomous driving,.

Enabling Federated Object Detection for Connected Autonomous Vehicles: A Deployment-Oriented Evaluation Parameter-efficient federated cooperative learning for 3d object detection in autonomous driving,

Reference 30

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Observation 7be9dd25-5438-4c99-be9d-563608ce19bb · outbound

This paper cites Toward efficient and secure object detection with sparse federated training over internet of vehicles,.

Enabling Federated Object Detection for Connected Autonomous Vehicles: A Deployment-Oriented Evaluation Toward efficient and secure object detection with sparse federated training over internet of vehicles,

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Observation e079dbf3-1b3d-4a78-a975-5e0a38efd952 · outbound

This paper cites Vehicular federated learning for pedestrian detection under adverse image conditions,.

Enabling Federated Object Detection for Connected Autonomous Vehicles: A Deployment-Oriented Evaluation Vehicular federated learning for pedestrian detection under adverse image conditions,

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This paper cites Federated semi-supervised learning for object detection in autonomous driving,.

Enabling Federated Object Detection for Connected Autonomous Vehicles: A Deployment-Oriented Evaluation Federated semi-supervised learning for object detection in autonomous driving,

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Observation 2f18399d-365e-44e3-9f26-344ca4b7eec1 · outbound

This paper cites Federated Learning with Heterogeneous Data Handling for Robust Vehicular Object Detection.

Enabling Federated Object Detection for Connected Autonomous Vehicles: A Deployment-Oriented Evaluation Federated Learning with Heterogeneous Data Handling for Robust Vehicular Object Detection

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Observation 2d6f11be-6abc-46a1-b3d8-5cc38c2d03a8 · outbound

This paper cites FedPylot: Navigating Federated Learning for Real-Time Object Detection in Internet of Vehicles.

Enabling Federated Object Detection for Connected Autonomous Vehicles: A Deployment-Oriented Evaluation FedPylot: Navigating Federated Learning for Real-Time Object Detection in Internet of Vehicles

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Observation dede3c01-f7db-45d6-b34f-fbd658907d28 · outbound

This paper cites Towards continual federated learning of monocular depth for autonomous vehicles,.

Enabling Federated Object Detection for Connected Autonomous Vehicles: A Deployment-Oriented Evaluation Towards continual federated learning of monocular depth for autonomous vehicles,

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This paper cites Federated learning for object detection in autonomous vehi- cles,.

Enabling Federated Object Detection for Connected Autonomous Vehicles: A Deployment-Oriented Evaluation Federated learning for object detection in autonomous vehi- cles,

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Observation fe777ee3-dbc2-4b2e-a14d-e4420cab54ee · outbound

This paper cites Explainable federated learning based secure and transparent object detection model for autonomous vehicles,.

Enabling Federated Object Detection for Connected Autonomous Vehicles: A Deployment-Oriented Evaluation Explainable federated learning based secure and transparent object detection model for autonomous vehicles,

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:09:16.719109Z

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Observation b4852f6a-e4d1-4143-ab07-72002171d9e6 · outbound

This paper cites Autofed: Heterogeneity- aware federated multimodal learning for robust autonomous driving,.

Enabling Federated Object Detection for Connected Autonomous Vehicles: A Deployment-Oriented Evaluation Autofed: Heterogeneity- aware federated multimodal learning for robust autonomous driving,

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Observation 5f431acc-883f-4307-bc8f-5469ff8fc68b · outbound

This paper cites Federated coopera- tive 3d object detection for autonomous driving,.

Enabling Federated Object Detection for Connected Autonomous Vehicles: A Deployment-Oriented Evaluation Federated coopera- tive 3d object detection for autonomous driving,

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:09:16.302654Z

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Observation d19b4564-56d5-4880-ab53-8d1dae0a9c4e · outbound

This paper cites Fedbevt: Federated learning bird’s eye view perception transformer in road traffic systems,.

Enabling Federated Object Detection for Connected Autonomous Vehicles: A Deployment-Oriented Evaluation Fedbevt: Federated learning bird’s eye view perception transformer in road traffic systems,

Reference 41

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Observation b96f1d1b-ffe5-481b-9be1-9e5303f25783 · outbound

This paper cites On the federated learning framework for cooperative perception,.

Enabling Federated Object Detection for Connected Autonomous Vehicles: A Deployment-Oriented Evaluation On the federated learning framework for cooperative perception,

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:09:15.683363Z

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Observation ff2e5e25-8468-4ff2-9449-dee7bd3457b0 · outbound

This paper cites Federated deep learning meets autonomous vehicle perception: Design and verification,.

Enabling Federated Object Detection for Connected Autonomous Vehicles: A Deployment-Oriented Evaluation Federated deep learning meets autonomous vehicle perception: Design and verification,

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:09:15.457073Z

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Observation f2ae4392-f7f2-4867-b42a-bd6e8f63d853 · outbound

This paper cites A secure object detection technique for intelligent transportation systems,.

Enabling Federated Object Detection for Connected Autonomous Vehicles: A Deployment-Oriented Evaluation A secure object detection technique for intelligent transportation systems,

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:09:15.131402Z

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Observation 15fbcd3d-5232-4c62-be0e-f7e43cca7f67 · outbound

This paper cites Cooperative driving of connected autonomous vehicles in heterogeneous mixed traffic: A game theoretic approach,.

Enabling Federated Object Detection for Connected Autonomous Vehicles: A Deployment-Oriented Evaluation Cooperative driving of connected autonomous vehicles in heterogeneous mixed traffic: A game theoretic approach,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:09:14.795322Z

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Observation 8e9ffc49-6183-4e1a-9dd1-096815ed1fe4 · outbound

This paper cites NIPD: A Federated Learning Person Detection Benchmark Based on Real-World Non-IID Data.

Enabling Federated Object Detection for Connected Autonomous Vehicles: A Deployment-Oriented Evaluation NIPD: A Federated Learning Person Detection Benchmark Based on Real-World Non-IID Data

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Resolution: verified exact; local_arxiv, observed 2026-08-05T12:09:06.625157Z

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Observation 98e1eb1a-ecd7-4ead-80e4-264f9505f656 · outbound

This paper cites Enhancing federated learning in heterogeneous internet of vehicles: A collaborative training approach,.

Enabling Federated Object Detection for Connected Autonomous Vehicles: A Deployment-Oriented Evaluation Enhancing federated learning in heterogeneous internet of vehicles: A collaborative training approach,

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:09:14.551534Z

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Observation b6f09444-5e47-4c37-b49b-65bcf19faf8f · outbound

This paper cites On addressing heterogeneity in federated learning for autonomous vehicles connected to a drone orchestrator,.

Enabling Federated Object Detection for Connected Autonomous Vehicles: A Deployment-Oriented Evaluation On addressing heterogeneity in federated learning for autonomous vehicles connected to a drone orchestrator,

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:09:14.245160Z

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Observation f216e8a3-a2c2-4f17-8e59-f6601dd6fcd0 · outbound

This paper cites Hierarchical cooperative control of connected vehicles: From hetero- geneous parameters to heterogeneous structures,.

Enabling Federated Object Detection for Connected Autonomous Vehicles: A Deployment-Oriented Evaluation Hierarchical cooperative control of connected vehicles: From hetero- geneous parameters to heterogeneous structures,

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:09:13.965920Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 51f3f33f-8b41-4996-8a77-36e91c1cfa96 · outbound

This paper cites Reliable and efficient autonomous driving: the need for heterogeneous vehicular networks,.

Enabling Federated Object Detection for Connected Autonomous Vehicles: A Deployment-Oriented Evaluation Reliable and efficient autonomous driving: the need for heterogeneous vehicular networks,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:09:13.657495Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation ee65289c-37a3-4928-948b-30c5c0118d43 · outbound

This paper cites HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients.

Enabling Federated Object Detection for Connected Autonomous Vehicles: A Deployment-Oriented Evaluation HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-05T12:09:02.916367Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a41878e5-7c48-4ab9-a513-ee9c2de805a0 · outbound

This paper cites Fedadapt: Adaptive offloading for iot devices in federated learning,.

Enabling Federated Object Detection for Connected Autonomous Vehicles: A Deployment-Oriented Evaluation Fedadapt: Adaptive offloading for iot devices in federated learning,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:09:13.408384Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation bbe34de6-53c7-41df-8600-7eab0829f136 · outbound

This paper cites Deep multi-modal object detection and semantic segmentation for autonomous driving: Datasets, methods, and challenges,.

Enabling Federated Object Detection for Connected Autonomous Vehicles: A Deployment-Oriented Evaluation Deep multi-modal object detection and semantic segmentation for autonomous driving: Datasets, methods, and challenges,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:09:13.121485Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation ae9702e8-6d64-48db-9485-477437afab83 · outbound

This paper cites Communication and computing resource optimization for connected au- tonomous driving,.

Enabling Federated Object Detection for Connected Autonomous Vehicles: A Deployment-Oriented Evaluation Communication and computing resource optimization for connected au- tonomous driving,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:09:12.874465Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 3a2efc93-2e2d-406c-91c7-28da65cb7f51 · outbound

This paper cites Performance evaluation of image processing algorithms on the gpu,.

Enabling Federated Object Detection for Connected Autonomous Vehicles: A Deployment-Oriented Evaluation Performance evaluation of image processing algorithms on the gpu,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:09:12.620633Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation afa9ac9a-acfa-4f6c-a60c-f5d2d27fb8f0 · outbound

This paper cites Advanced op- timization techniques for federated learning on non-iid data,.

Enabling Federated Object Detection for Connected Autonomous Vehicles: A Deployment-Oriented Evaluation Advanced op- timization techniques for federated learning on non-iid data,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:09:12.332314Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation a6bef9e9-a85c-4b6d-b47d-b7abbfd1000a · outbound

This paper cites A deep learning framework performance evaluation to use yolo in nvidia jetson platform,.

Enabling Federated Object Detection for Connected Autonomous Vehicles: A Deployment-Oriented Evaluation A deep learning framework performance evaluation to use yolo in nvidia jetson platform,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:09:12.062324Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation b3fe9b15-392d-4cb7-8534-9d0f1bfd90b6 · outbound

This paper cites Deep learning with gpus,.

Enabling Federated Object Detection for Connected Autonomous Vehicles: A Deployment-Oriented Evaluation Deep learning with gpus,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:09:11.817952Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation faf3ca3f-989a-417f-97dd-4956367d5464 · outbound

This paper cites Lopecs: A low-power edge computing system for real-time autonomous driving services,.

Enabling Federated Object Detection for Connected Autonomous Vehicles: A Deployment-Oriented Evaluation Lopecs: A low-power edge computing system for real-time autonomous driving services,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:09:11.617323Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 8ef98b9f-2b8c-4c9c-bde8-64dd609eb2d7 · outbound

This paper cites The architectural implications of autonomous driving: Constraints and acceleration,.

Enabling Federated Object Detection for Connected Autonomous Vehicles: A Deployment-Oriented Evaluation The architectural implications of autonomous driving: Constraints and acceleration,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:09:11.417891Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 0970693b-edcc-4be5-a5f3-684a12a45b65 · outbound

This paper cites Deep learning based approaches to enhance energy efficiency in autonomous driving systems,.

Enabling Federated Object Detection for Connected Autonomous Vehicles: A Deployment-Oriented Evaluation Deep learning based approaches to enhance energy efficiency in autonomous driving systems,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:09:11.173872Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 26dd187a-6f3d-4e81-b019-9ba15d8f4ce6 · outbound

This paper cites Reducing power consumption and latency of autonomous vehicles with efficient task and path assignment in the v2x-mec based on nash equilibrium,.

Enabling Federated Object Detection for Connected Autonomous Vehicles: A Deployment-Oriented Evaluation Reducing power consumption and latency of autonomous vehicles with efficient task and path assignment in the v2x-mec based on nash equilibrium,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:09:10.931831Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 86177c3e-04ab-43b9-9539-e0a2e6e4d2a7 · outbound

This paper cites Object detection in autonomous vehicles under adverse weather: A review of traditional and deep learning approaches,.

Enabling Federated Object Detection for Connected Autonomous Vehicles: A Deployment-Oriented Evaluation Object detection in autonomous vehicles under adverse weather: A review of traditional and deep learning approaches,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:09:10.646883Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 86eda7ef-7427-4d62-a622-7f09dd7099ca · outbound

This paper cites Watch tesla autopilot go through a snowstorm,.

Enabling Federated Object Detection for Connected Autonomous Vehicles: A Deployment-Oriented Evaluation Watch tesla autopilot go through a snowstorm,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:09:10.408475Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 5140dd2c-b83d-4776-a524-84da5b6b5321 · outbound

This paper cites Super Cruise: Hands-Free Driving, Cutting Edge Technology,.

Enabling Federated Object Detection for Connected Autonomous Vehicles: A Deployment-Oriented Evaluation Super Cruise: Hands-Free Driving, Cutting Edge Technology,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:09:10.146437Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 3a261754-349c-4b24-8ba2-4cc66b05d8ce · outbound

This paper cites On the roles of circulation and aerosols in the decline of mist and dense fog in europe over the last 30 years,.

Enabling Federated Object Detection for Connected Autonomous Vehicles: A Deployment-Oriented Evaluation On the roles of circulation and aerosols in the decline of mist and dense fog in europe over the last 30 years,

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:09:09.895922Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 13331b3e-6cf9-4610-b444-f7d50c861a6f · outbound

This paper cites Seeing through fog without seeing fog: Deep multi- modal sensor fusion in unseen adverse weather,.

Enabling Federated Object Detection for Connected Autonomous Vehicles: A Deployment-Oriented Evaluation Seeing through fog without seeing fog: Deep multi- modal sensor fusion in unseen adverse weather,

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:09:09.648999Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 90d0b9dc-7e9e-49c6-96f8-447280d382d5 · outbound

This paper cites Perception and sensing for autonomous vehicles under adverse weather conditions: A survey,.

Enabling Federated Object Detection for Connected Autonomous Vehicles: A Deployment-Oriented Evaluation Perception and sensing for autonomous vehicles under adverse weather conditions: A survey,

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-05T12:09:05.204799Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d3fe11ed-7a63-4e13-938a-9e0ac2d1cf4a · outbound

This paper cites Vision meets robotics: The kitti dataset,.

Enabling Federated Object Detection for Connected Autonomous Vehicles: A Deployment-Oriented Evaluation Vision meets robotics: The kitti dataset,

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:09:09.321631Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 73fe5ed4-6cd6-4521-a479-6aee11702142 · outbound

This paper cites Bdd100k: A diverse driving dataset for heterogeneous multitask learning,.

Enabling Federated Object Detection for Connected Autonomous Vehicles: A Deployment-Oriented Evaluation Bdd100k: A diverse driving dataset for heterogeneous multitask learning,

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:09:09.116050Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 4a25cd09-d75c-45df-ab1e-add280c5cf0a · outbound

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

Enabling Federated Object Detection for Connected Autonomous Vehicles: A Deployment-Oriented Evaluation nuscenes: A multimodal dataset for autonomous driving,

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-05T12:09:05.645243Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 114129d1-db1e-4eb7-afcf-81af46071f79 · outbound

This paper cites Flower: A Friendly Federated Learning Research Framework.

Enabling Federated Object Detection for Connected Autonomous Vehicles: A Deployment-Oriented Evaluation Flower: A Friendly Federated Learning Research Framework

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-05T12:09:05.777452Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1e82de72-0693-432d-975f-b1ede5bf10e4 · outbound

This paper cites Ars-detr: Aspect ratio- sensitive detection transformer for aerial oriented object detection,.

Enabling Federated Object Detection for Connected Autonomous Vehicles: A Deployment-Oriented Evaluation Ars-detr: Aspect ratio- sensitive detection transformer for aerial oriented object detection,

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:09:08.826961Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation d312b485-b11e-4597-8465-11045d828e63 · outbound

This paper cites Enabling federated learning for object detection in connected autonomous driving using yolo with the flower framework,.

Enabling Federated Object Detection for Connected Autonomous Vehicles: A Deployment-Oriented Evaluation Enabling federated learning for object detection in connected autonomous driving using yolo with the flower framework,

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:09:08.545724Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 894a23b2-0e27-4af0-8441-980ec824d4db · outbound

This paper cites Vehicle computing: Vision and challenges,.

Enabling Federated Object Detection for Connected Autonomous Vehicles: A Deployment-Oriented Evaluation Vehicle computing: Vision and challenges,

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:09:08.329489Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 590f386d-c256-43fc-8cd5-a67c495ffb42 · outbound

This paper cites A survey on federated unlearning: Challenges, methods, and future directions,.

Enabling Federated Object Detection for Connected Autonomous Vehicles: A Deployment-Oriented Evaluation A survey on federated unlearning: Challenges, methods, and future directions,

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:09:08.090914Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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