Paper Citation Record · LEDGER

UPAQ: A Framework for Real-Time and Energy-Efficient 3D Object Detection in Autonomous Vehicles

As of 19 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 0 inbound Pith citation observations for arXiv:2501.04213.

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

pith.paper-citation-record.v1
2501.04213 v1

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T21:45:21.068319Z

measured 41 of 41 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+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

41 of 41 outbound references displayed

verified exact0
verified fuzzy34
unresolved7
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6dc2df78-d059-4ed5-bad0-723b24460eed · outbound

This paper cites an unresolved cited work.

UPAQ: A Framework for Real-Time and Energy-Efficient 3D Object Detection in Autonomous Vehicles Unresolved cited work

Reference 1

Resolution: unresolved; raw_fallback, observed 2026-08-10T21:45:21.752862Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b0c287d2-b5cc-42bd-98cd-bcb776dcdaed · outbound

This paper cites Object Detection in Autonomous Vehicles: Status and Open Challenges.

UPAQ: A Framework for Real-Time and Energy-Efficient 3D Object Detection in Autonomous Vehicles Object Detection in Autonomous Vehicles: Status and Open Challenges

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-10T21:45:20.870973Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 748580f3-8b11-4573-b56d-e474d380564d · outbound

This paper cites Advanced Driver Assistance Systems: A Path Toward Autonomous Vehicles.

UPAQ: A Framework for Real-Time and Energy-Efficient 3D Object Detection in Autonomous Vehicles Advanced Driver Assistance Systems: A Path Toward Autonomous Vehicles

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:45:21.736819Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ff2687c3-884d-4054-a2a0-53d625494c8a · outbound

This paper cites Machine Learning and Optimization Techniques for Automotive Cyber -Physical Systems.

UPAQ: A Framework for Real-Time and Energy-Efficient 3D Object Detection in Autonomous Vehicles Machine Learning and Optimization Techniques for Automotive Cyber -Physical Systems

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:45:21.719382Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation aa113e97-b8d6-43df-8bde-a9e4897ed650 · outbound

This paper cites Object detection based on lightweight YOLOX for autonomous driving.

UPAQ: A Framework for Real-Time and Energy-Efficient 3D Object Detection in Autonomous Vehicles Object detection based on lightweight YOLOX for autonomous driving

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:45:21.703424Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation af8c227b-8a39-41ee-baaa-2cd7f1c118a0 · outbound

This paper cites Sparsity in deep learning: Pruning and growth for efficient inference and training in neural networks.

UPAQ: A Framework for Real-Time and Energy-Efficient 3D Object Detection in Autonomous Vehicles Sparsity in deep learning: Pruning and growth for efficient inference and training in neural networks

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:45:21.688252Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 356d8c24-6cbb-443d-9edf-c376709b10f4 · outbound

This paper cites Pruning vs Quantization: Which is Better?.

UPAQ: A Framework for Real-Time and Energy-Efficient 3D Object Detection in Autonomous Vehicles Pruning vs Quantization: Which is Better?

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-10T21:45:20.895622Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6e3a0590-7512-4d9b-85ea-0dd35c7520c8 · outbound

This paper cites Fast R-CNN,.

UPAQ: A Framework for Real-Time and Energy-Efficient 3D Object Detection in Autonomous Vehicles Fast R-CNN,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:45:21.673672Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d5b15eb2-ea2e-465b-9158-a423e867e024 · outbound

This paper cites Faster R-CNN: an Approach to Real -Time Object Detection,.

UPAQ: A Framework for Real-Time and Energy-Efficient 3D Object Detection in Autonomous Vehicles Faster R-CNN: an Approach to Real -Time Object Detection,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:45:21.658124Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5278e70d-a925-441d-8bc1-a9077fdf2f00 · outbound

This paper cites Focal Loss for Dense Object Detection.

UPAQ: A Framework for Real-Time and Energy-Efficient 3D Object Detection in Autonomous Vehicles Focal Loss for Dense Object Detection

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-10T21:45:20.914907Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fa5cf761-b247-4c83-9adc-3ad78d4ca6c6 · outbound

This paper cites On the performance of one -stage and two -stage object detectors in autonomous vehicles using camera data.

UPAQ: A Framework for Real-Time and Energy-Efficient 3D Object Detection in Autonomous Vehicles On the performance of one -stage and two -stage object detectors in autonomous vehicles using camera data

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:45:21.641340Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f817eb0c-26b1-43e9-a4e4-1c79bb5e556b · outbound

This paper cites YOLOX: Exceeding YOLO Series in 2021.

UPAQ: A Framework for Real-Time and Energy-Efficient 3D Object Detection in Autonomous Vehicles YOLOX: Exceeding YOLO Series in 2021

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-10T21:45:20.925400Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d6507f13-dde1-4b07-aab1-54a13c0c51b1 · outbound

This paper cites Pointnet: Deep learning on point sets for 3d classification and segmentation.

UPAQ: A Framework for Real-Time and Energy-Efficient 3D Object Detection in Autonomous Vehicles Pointnet: Deep learning on point sets for 3d classification and segmentation

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:45:21.625485Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1c9e740e-5350-42e2-aaba-71e36bba3224 · outbound

This paper cites Pointpillars: Fast encoders for object detection from point clouds.

UPAQ: A Framework for Real-Time and Energy-Efficient 3D Object Detection in Autonomous Vehicles Pointpillars: Fast encoders for object detection from point clouds

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:45:21.609635Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation cd061eb1-ff05-4012-a49a-92fe1765be71 · outbound

This paper cites Objects are different: Flexible monocular 3d object detection.

UPAQ: A Framework for Real-Time and Energy-Efficient 3D Object Detection in Autonomous Vehicles Objects are different: Flexible monocular 3d object detection

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:45:21.593856Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e57224d1-5b76-4e7c-aae6-d9b4b345e31e · outbound

This paper cites Smoke: Single-stage monocular 3d object detection via keypoint estimation.

UPAQ: A Framework for Real-Time and Energy-Efficient 3D Object Detection in Autonomous Vehicles Smoke: Single-stage monocular 3d object detection via keypoint estimation

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:45:21.578851Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 924a2264-c914-4359-83ab-a3a7253f4713 · outbound

This paper cites Second: Sparsely embedded convolutional detection.

UPAQ: A Framework for Real-Time and Energy-Efficient 3D Object Detection in Autonomous Vehicles Second: Sparsely embedded convolutional detection

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:45:21.562533Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 77874087-f7c0-4bf3-8aab-2f7721181213 · outbound

This paper cites Focal sparse convolutional networks for 3d object detection.

UPAQ: A Framework for Real-Time and Energy-Efficient 3D Object Detection in Autonomous Vehicles Focal sparse convolutional networks for 3d object detection

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:45:21.546178Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f75e61c7-a37d-4ea1-964f-63078916ab3d · outbound

This paper cites Virtual sparse convolution for multimodal 3d object detection.

UPAQ: A Framework for Real-Time and Energy-Efficient 3D Object Detection in Autonomous Vehicles Virtual sparse convolution for multimodal 3d object detection

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:45:21.530057Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 58772245-9bb0-49bb-8fc2-9b28a40fe003 · outbound

This paper cites Ps and qs : Quantization-aware pruning for efficient low latency neural network inference.

UPAQ: A Framework for Real-Time and Energy-Efficient 3D Object Detection in Autonomous Vehicles Ps and qs : Quantization-aware pruning for efficient low latency neural network inference

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:45:21.513348Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 02ae7f9c-9ef3-4765-8bc9-796fabf3ce34 · outbound

This paper cites Clip-q: Deep network compression learning by in - parallel pruning -quantization.

UPAQ: A Framework for Real-Time and Energy-Efficient 3D Object Detection in Autonomous Vehicles Clip-q: Deep network compression learning by in - parallel pruning -quantization

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:45:21.498505Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation bb326d49-f1d8-44d2-8819-4374eaa488d5 · outbound

This paper cites LiDAR-PTQ: Post-Training Quantization for Point Cloud 3D Object Detection.

UPAQ: A Framework for Real-Time and Energy-Efficient 3D Object Detection in Autonomous Vehicles LiDAR-PTQ: Post-Training Quantization for Point Cloud 3D Object Detection

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-10T21:45:20.972016Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1b10b9db-7381-4c0f-aed4-3cd550766a65 · outbound

This paper cites R-TOSS: A framework for real-time object detection using semi -structured pruning.

UPAQ: A Framework for Real-Time and Energy-Efficient 3D Object Detection in Autonomous Vehicles R-TOSS: A framework for real-time object detection using semi -structured pruning

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:45:21.482538Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 6f97cf73-7560-477d-9520-bddc69977a61 · outbound

This paper cites Inducing and Exploiting Activation Sparsity for Fast Inference on Deep Neural Networks,.

UPAQ: A Framework for Real-Time and Energy-Efficient 3D Object Detection in Autonomous Vehicles Inducing and Exploiting Activation Sparsity for Fast Inference on Deep Neural Networks,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:45:21.467114Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 71d4b237-ef76-4aa1-8779-c1542066b409 · outbound

This paper cites Importance estimation for neural network pruning,.

UPAQ: A Framework for Real-Time and Energy-Efficient 3D Object Detection in Autonomous Vehicles Importance estimation for neural network pruning,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:45:21.451759Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2460d64a-abf3-448a-bbe9-287b4784e157 · outbound

This paper cites Pruning neural networks without any data by iteratively conserving synaptic flow,.

UPAQ: A Framework for Real-Time and Energy-Efficient 3D Object Detection in Autonomous Vehicles Pruning neural networks without any data by iteratively conserving synaptic flow,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:45:21.436245Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f4e6c79c-5c00-456c-9699-30d3136786ae · outbound

This paper cites Edge devices object detection by filter pruning,.

UPAQ: A Framework for Real-Time and Energy-Efficient 3D Object Detection in Autonomous Vehicles Edge devices object detection by filter pruning,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:45:21.422488Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 52eeca14-51a0-4660-bee8-706faa5fc8be · outbound

This paper cites Localization -aware channel pruning for object detection,.

UPAQ: A Framework for Real-Time and Energy-Efficient 3D Object Detection in Autonomous Vehicles Localization -aware channel pruning for object detection,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:45:21.407847Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 51ef159f-a2f9-4e8b-9d22-0c03f51c6be8 · outbound

This paper cites TensorRT-Based Framework and Optimization Methodology for Deep Learning Inference on Jetson Boards.

UPAQ: A Framework for Real-Time and Energy-Efficient 3D Object Detection in Autonomous Vehicles TensorRT-Based Framework and Optimization Methodology for Deep Learning Inference on Jetson Boards

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:45:21.391889Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ba253be9-793b-4172-b46f-8adfac3e97f1 · outbound

This paper cites Patdnn: Achieving real -time dnn execution on mobile devices with pattern -based weight pruning,.

UPAQ: A Framework for Real-Time and Energy-Efficient 3D Object Detection in Autonomous Vehicles Patdnn: Achieving real -time dnn execution on mobile devices with pattern -based weight pruning,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:45:21.376635Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2356e1ab-1d8c-4e83-8644-466d0cf181bb · outbound

This paper cites Loss aware post -training quantization.

UPAQ: A Framework for Real-Time and Energy-Efficient 3D Object Detection in Autonomous Vehicles Loss aware post -training quantization

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:45:21.360904Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f639a845-2a83-440e-a81c-87a5785e2d9b · outbound

This paper cites A survey of quantization methods for efficient neural network inference.

UPAQ: A Framework for Real-Time and Energy-Efficient 3D Object Detection in Autonomous Vehicles A survey of quantization methods for efficient neural network inference

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:45:21.344929Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 49edd749-0dd9-49f3-b89f-2951c53424f5 · outbound

This paper cites an unresolved cited work.

UPAQ: A Framework for Real-Time and Energy-Efficient 3D Object Detection in Autonomous Vehicles Unresolved cited work

Reference 33

Resolution: unresolved; raw_fallback, observed 2026-08-10T21:45:21.328757Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b604354e-4b2c-4fd6-9d1f-1e007f158613 · outbound

This paper cites https://docs.python.org/3/library/copy.html [last accessed on: 11/13/2024].

UPAQ: A Framework for Real-Time and Energy-Efficient 3D Object Detection in Autonomous Vehicles https://docs.python.org/3/library/copy.html [last accessed on: 11/13/2024]

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:45:21.312745Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation de42b207-09d6-4a05-b5b1-cb6ba06e19dd · outbound

This paper cites Quantization and training of neural networks for efficient integer-arithmetic-only inference.

UPAQ: A Framework for Real-Time and Energy-Efficient 3D Object Detection in Autonomous Vehicles Quantization and training of neural networks for efficient integer-arithmetic-only inference

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:45:21.296815Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d512a150-507c-4fb5-bcca-c8288e6e70fc · outbound

This paper cites NVpower at master · wildkid1024/NVpower,.

UPAQ: A Framework for Real-Time and Energy-Efficient 3D Object Detection in Autonomous Vehicles NVpower at master · wildkid1024/NVpower,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:45:21.280244Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0f731b1b-56a9-4d09-a694-3339db62660e · outbound

This paper cites Roadmap for Cybersecurity in Autonomous Vehicles.

UPAQ: A Framework for Real-Time and Energy-Efficient 3D Object Detection in Autonomous Vehicles Roadmap for Cybersecurity in Autonomous Vehicles

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:45:21.244794Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3dc7c59c-794e-4b94-a8d9-bd5bf5b89886 · outbound

This paper cites LATTE: LSTM Self - Attention based Anomaly Detection in Embedded Automotive Platforms.

UPAQ: A Framework for Real-Time and Energy-Efficient 3D Object Detection in Autonomous Vehicles LATTE: LSTM Self - Attention based Anomaly Detection in Embedded Automotive Platforms

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:45:21.227982Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d6810105-ef31-4d93-8298-770224952a29 · outbound

This paper cites VESPA: Optimizing Heterogeneous Sensor Placement and Orientation for Autonomous Vehicles.

UPAQ: A Framework for Real-Time and Energy-Efficient 3D Object Detection in Autonomous Vehicles VESPA: Optimizing Heterogeneous Sensor Placement and Orientation for Autonomous Vehicles

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:45:21.210490Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation abb68c7f-0f71-4cf5-ac49-14c94a740f1f · outbound

This paper cites Co-Optimizing Sensing and Deep Machine Learning in Automotive Cyber-Physical Systems.

UPAQ: A Framework for Real-Time and Energy-Efficient 3D Object Detection in Autonomous Vehicles Co-Optimizing Sensing and Deep Machine Learning in Automotive Cyber-Physical Systems

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:45:21.193644Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f10a91ca-d0b6-408a-8518-4459f0ec81c8 · outbound

This paper cites 28, 2024].

UPAQ: A Framework for Real-Time and Energy-Efficient 3D Object Detection in Autonomous Vehicles 28, 2024]

Reference 2020

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:45:21.262043Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Pith citing papers

No inbound Pith citation observations are available.