Towards Trustworthy and Explainable AI for Perception Models: From Concept to Prototype Vehicle Deployment | Paper Citation Record

Observation 77fc20b1-f21f-4520-abf3-8cfab472c987 · outbound

This paper cites Cross modal transformer via coordinates encoding for 3d object dectection.

Towards Trustworthy and Explainable AI for Perception Models: From Concept to Prototype Vehicle Deployment Cross modal transformer via coordinates encoding for 3d object dectection

Reference 1

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Observation b73161d4-b5a8-4f51-8e65-4f94edd10a43 · outbound

This paper cites Sparsefusion: Fusing multi-modal sparse representations for multi-sensor 3d object detection.

Towards Trustworthy and Explainable AI for Perception Models: From Concept to Prototype Vehicle Deployment Sparsefusion: Fusing multi-modal sparse representations for multi-sensor 3d object detection

Reference 2

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Observation 7650aa7e-0fd9-45c7-8337-1ed279df36e1 · outbound

This paper cites Ethics guidelines for trustworthy AI.

Towards Trustworthy and Explainable AI for Perception Models: From Concept to Prototype Vehicle Deployment Ethics guidelines for trustworthy AI

Reference 3

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Observation a8ea83c8-55dc-4103-b12e-095d7ef5a422 · outbound

This paper cites Artificial intelligence risk management framework.

Towards Trustworthy and Explainable AI for Perception Models: From Concept to Prototype Vehicle Deployment Artificial intelligence risk management framework

Reference 4

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Observation cafbbc4e-a596-4b68-b42f-19295f31a381 · outbound

This paper cites ISO 26262- 1:2018(en): Road vehicles — functional safety.

Towards Trustworthy and Explainable AI for Perception Models: From Concept to Prototype Vehicle Deployment ISO 26262- 1:2018(en): Road vehicles — functional safety

Reference 5

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Observation 244c2924-5152-47fa-874e-2da2624e7048 · outbound

This paper cites ISO 21448:2022: Road vehicles — safety of the intended functionality.

Towards Trustworthy and Explainable AI for Perception Models: From Concept to Prototype Vehicle Deployment ISO 21448:2022: Road vehicles — safety of the intended functionality

Reference 6

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Observation ef1b10a8-6f96-4a8e-a57d-415c3aa1248d · outbound

This paper cites Explainable ai for safe and trustworthy autonomous driving: A systematic review.

Towards Trustworthy and Explainable AI for Perception Models: From Concept to Prototype Vehicle Deployment Explainable ai for safe and trustworthy autonomous driving: A systematic review

Reference 7

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Observation 6d62bbfc-8793-41c8-a121-af334b5bf68f · outbound

This paper cites On calibration of modern neural networks.

Towards Trustworthy and Explainable AI for Perception Models: From Concept to Prototype Vehicle Deployment On calibration of modern neural networks

Reference 8

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Observation acc89e35-0444-4977-bcc6-1c8c6a4c620a · outbound

This paper cites Can we trust you? on calibration of a probabilistic object detector for autonomous driving.

Towards Trustworthy and Explainable AI for Perception Models: From Concept to Prototype Vehicle Deployment Can we trust you? on calibration of a probabilistic object detector for autonomous driving

Reference 9

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Observation 7e058cd5-1c0b-471c-a3e0-6f93b1f5621e · outbound

This paper cites Multi- variate confidence calibration for object detection.

Towards Trustworthy and Explainable AI for Perception Models: From Concept to Prototype Vehicle Deployment Multi- variate confidence calibration for object detection

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T10:06:52.701379Z

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Observation dbbb8450-1b2d-4c91-bc2c-98ba42ae6f69 · outbound

This paper cites “why should i trust you?.

Towards Trustworthy and Explainable AI for Perception Models: From Concept to Prototype Vehicle Deployment “why should i trust you?

Reference 11

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Observation 9dcdb24e-eb0b-4747-a42c-ab01dc8e411e · outbound

This paper cites Grad-cam: Visual explanations from deep networks via gradient-based localization.

Towards Trustworthy and Explainable AI for Perception Models: From Concept to Prototype Vehicle Deployment Grad-cam: Visual explanations from deep networks via gradient-based localization

Reference 12

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Observation e3ad7b09-d547-4a3a-b57d-a615d32fff3b · outbound

This paper cites A unified approach to interpreting model predictions.

Towards Trustworthy and Explainable AI for Perception Models: From Concept to Prototype Vehicle Deployment A unified approach to interpreting model predictions

Reference 13

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Observation c65d3a5c-1799-491f-b539-f2cc7f4dc320 · outbound

This paper cites Interpretable explanations of black boxes by meaningful perturbation.

Towards Trustworthy and Explainable AI for Perception Models: From Concept to Prototype Vehicle Deployment Interpretable explanations of black boxes by meaningful perturbation

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T10:06:52.631100Z

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Observation c58224ac-9e8c-4da7-8632-ff9407a39050 · outbound

This paper cites A methodology to enhance transparency for trustworthy artificial intelligence for cooperative, connected, and automated mobility.

Towards Trustworthy and Explainable AI for Perception Models: From Concept to Prototype Vehicle Deployment A methodology to enhance transparency for trustworthy artificial intelligence for cooperative, connected, and automated mobility

Reference 15

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Observation 8ab3749e-512a-4bd6-9714-2bde50eaf644 · outbound

This paper cites Molnar,Interpretable Machine Learning, 3rd ed.

Towards Trustworthy and Explainable AI for Perception Models: From Concept to Prototype Vehicle Deployment Molnar,Interpretable Machine Learning, 3rd ed

Reference 16

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Observation 1694bee5-5edc-4aac-98e9-224a672b73c9 · outbound

This paper cites Guidelines for human-ai interaction.

Towards Trustworthy and Explainable AI for Perception Models: From Concept to Prototype Vehicle Deployment Guidelines for human-ai interaction

Reference 17

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Observation d31f7d39-5d2b-4964-b167-fc30cfecc8bb · outbound

This paper cites Deep inside convolutional networks: Visualising image classification models and saliency maps.

Towards Trustworthy and Explainable AI for Perception Models: From Concept to Prototype Vehicle Deployment Deep inside convolutional networks: Visualising image classification models and saliency maps

Reference 18

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Observation d0955bbc-2c53-457e-8a00-ecc00c8f8d62 · outbound

This paper cites Rise: Randomized input sampling for explanation of black-box models.

Towards Trustworthy and Explainable AI for Perception Models: From Concept to Prototype Vehicle Deployment Rise: Randomized input sampling for explanation of black-box models

Reference 19

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Observation 89d525ee-a162-4eed-a4ec-d7ad3aeb259b · outbound

This paper cites Black-box explanation of object detectors via saliency maps.

Towards Trustworthy and Explainable AI for Perception Models: From Concept to Prototype Vehicle Deployment Black-box explanation of object detectors via saliency maps

Reference 20

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Observation 6dd5bf43-40f0-4282-9c4c-50ecdf8594a2 · outbound

This paper cites Occam’s laser: Occlusion-based attribution maps for 3d object de- tectors on lidar data.

Towards Trustworthy and Explainable AI for Perception Models: From Concept to Prototype Vehicle Deployment Occam’s laser: Occlusion-based attribution maps for 3d object de- tectors on lidar data

Reference 21

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Observation 7b1b3483-a98b-47c3-b9a1-310c26ae4f57 · outbound

This paper cites Attention is All you Need.

Towards Trustworthy and Explainable AI for Perception Models: From Concept to Prototype Vehicle Deployment Attention is All you Need

Reference 22

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Observation 27c98c1f-c7eb-4854-b54c-d40a54d87e4f · outbound

This paper cites Explainable multi-camera 3d object detection with transformer-based saliency maps.

Towards Trustworthy and Explainable AI for Perception Models: From Concept to Prototype Vehicle Deployment Explainable multi-camera 3d object detection with transformer-based saliency maps

Reference 23

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Observation c773599b-49c2-4d0c-9bb3-460d2a781054 · outbound

This paper cites Attention is not explanation.

Towards Trustworthy and Explainable AI for Perception Models: From Concept to Prototype Vehicle Deployment Attention is not explanation

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T10:06:52.655586Z

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Observation 10ba280a-f695-457c-8d4c-8470153a1e57 · outbound

This paper cites Multicorrupt: A multi-modal robustness dataset and benchmark of lidar-camera fusion for 3d object detection.

Towards Trustworthy and Explainable AI for Perception Models: From Concept to Prototype Vehicle Deployment Multicorrupt: A multi-modal robustness dataset and benchmark of lidar-camera fusion for 3d object detection

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T10:06:52.687409Z

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Observation 0773e157-2f1a-4597-8327-2e4f063f3e1d · outbound

This paper cites Robo3d: Towards robust and reliable 3d perception against corruptions.

Towards Trustworthy and Explainable AI for Perception Models: From Concept to Prototype Vehicle Deployment Robo3d: Towards robust and reliable 3d perception against corruptions

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T10:06:52.657279Z

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Observation 9d8af1e8-24fd-4928-a4f4-95e41af9b301 · outbound

This paper cites Benchmarking and improving bird’s eye view perception robustness in autonomous driving.

Towards Trustworthy and Explainable AI for Perception Models: From Concept to Prototype Vehicle Deployment Benchmarking and improving bird’s eye view perception robustness in autonomous driving

Reference 27

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Observation eb3792aa-1010-4c85-8f31-9daf0f2b2a85 · outbound

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

Towards Trustworthy and Explainable AI for Perception Models: From Concept to Prototype Vehicle Deployment Seeing through fog without seeing fog: Deep multi- modal sensor fusion in unseen adverse weather

Reference 28

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Observation 911018cb-395d-441a-87d1-26ef5455927d · outbound

This paper cites Canadian adverse driving conditions dataset.

Towards Trustworthy and Explainable AI for Perception Models: From Concept to Prototype Vehicle Deployment Canadian adverse driving conditions dataset

Reference 29

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Observation 15de8826-1b77-491d-b747-4174ae645c60 · outbound

This paper cites nuScenes: A Multimodal Dataset for Autonomous Driving.

Towards Trustworthy and Explainable AI for Perception Models: From Concept to Prototype Vehicle Deployment nuScenes: A Multimodal Dataset for Autonomous Driving

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T10:06:52.669133Z

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Observation d6677531-bfba-42db-9468-8d44a59fe4a0 · outbound

This paper cites Scalability in Perception for Autonomous Driving: Waymo Open Dataset.

Towards Trustworthy and Explainable AI for Perception Models: From Concept to Prototype Vehicle Deployment Scalability in Perception for Autonomous Driving: Waymo Open Dataset

Reference 31

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Observation cb6f49bb-2db8-4e36-b417-bd3b2920add0 · outbound

This paper cites Benchmarking the robustness of lidar-camera fusion for 3d object detection.

Towards Trustworthy and Explainable AI for Perception Models: From Concept to Prototype Vehicle Deployment Benchmarking the robustness of lidar-camera fusion for 3d object detection

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T10:06:52.673881Z

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Observation 6c1bbf4b-1cd5-4735-9a40-c3b9304654b1 · outbound

This paper cites Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning.

Towards Trustworthy and Explainable AI for Perception Models: From Concept to Prototype Vehicle Deployment Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

Reference 33

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Observation 3276a509-cea4-4ec8-b72f-98808b95b526 · outbound

This paper cites Sampling-free epistemic uncertainty estimation using approximated variance propagation.

Towards Trustworthy and Explainable AI for Perception Models: From Concept to Prototype Vehicle Deployment Sampling-free epistemic uncertainty estimation using approximated variance propagation

Reference 34

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Observation 03109a84-ccce-4513-a4ec-a40fcf438d61 · outbound

This paper cites Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles.

Towards Trustworthy and Explainable AI for Perception Models: From Concept to Prototype Vehicle Deployment Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T10:06:52.705638Z

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Observation cce130a9-6df7-48e7-92db-e7fbc64f9844 · outbound

This paper cites Estimating the mean and variance of the target probability distribution.

Towards Trustworthy and Explainable AI for Perception Models: From Concept to Prototype Vehicle Deployment Estimating the mean and variance of the target probability distribution

Reference 36

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Observation e1cbdd96-a347-41ec-8555-6b80cd046b08 · outbound

This paper cites Practical confidence and prediction intervals.

Towards Trustworthy and Explainable AI for Perception Models: From Concept to Prototype Vehicle Deployment Practical confidence and prediction intervals

Reference 37

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Observation a6175e94-99fe-4679-b1b2-885a377afc78 · outbound

This paper cites Bayesod: A bayesian approach for uncertainty estimation in deep object detectors.

Towards Trustworthy and Explainable AI for Perception Models: From Concept to Prototype Vehicle Deployment Bayesod: A bayesian approach for uncertainty estimation in deep object detectors

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T10:06:52.699294Z

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Observation 1568c891-0f3e-4f45-addb-352330c98a2d · outbound

This paper cites Uncertainty estimation for deep neural object detectors in safety-critical applications.

Towards Trustworthy and Explainable AI for Perception Models: From Concept to Prototype Vehicle Deployment Uncertainty estimation for deep neural object detectors in safety-critical applications

Reference 39

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Observation 175f0fa2-a9bb-4d8a-aca4-53fdc83755f1 · outbound

This paper cites Gaussian yolov3: An accurate and fast object detector using localization uncertainty for autonomous driving.

Towards Trustworthy and Explainable AI for Perception Models: From Concept to Prototype Vehicle Deployment Gaussian yolov3: An accurate and fast object detector using localization uncertainty for autonomous driving

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T10:06:52.705798Z

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Observation 17cbc60c-fd01-455b-a733-201bc96e9aa0 · outbound

This paper cites Bounding box regression with uncertainty for accurate object detection.

Towards Trustworthy and Explainable AI for Perception Models: From Concept to Prototype Vehicle Deployment Bounding box regression with uncertainty for accurate object detection

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T10:06:52.659635Z

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Observation 8f839d4a-3138-4928-805a-6cf6ab68ba2c · outbound

This paper cites Towards safe autonomous driving: Capture uncertainty in the deep neural network for lidar 3d vehicle detection.

Towards Trustworthy and Explainable AI for Perception Models: From Concept to Prototype Vehicle Deployment Towards safe autonomous driving: Capture uncertainty in the deep neural network for lidar 3d vehicle detection

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T10:06:52.744359Z

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Observation 5f8b5a42-a494-40e0-8712-8ecc444d97d7 · outbound

This paper cites Training independent subnet- works for robust prediction.

Towards Trustworthy and Explainable AI for Perception Models: From Concept to Prototype Vehicle Deployment Training independent subnet- works for robust prediction

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T10:06:52.671389Z

Source-reported events for the cited work

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Observation 59e91b25-ee74-4b71-87a4-2d7da2f86b8f · outbound

This paper cites Lidar-mimo: Efficient uncertainty estimation for lidar-based 3d object detection.

Towards Trustworthy and Explainable AI for Perception Models: From Concept to Prototype Vehicle Deployment Lidar-mimo: Efficient uncertainty estimation for lidar-based 3d object detection

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T10:06:52.717506Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation b364098b-ebdd-4ea1-8361-e74e5a71363f · outbound

This paper cites OCCUQ: Exploring Efficient Uncertainty Quantification for 3D Occupancy Prediction.

Towards Trustworthy and Explainable AI for Perception Models: From Concept to Prototype Vehicle Deployment OCCUQ: Exploring Efficient Uncertainty Quantification for 3D Occupancy Prediction

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T10:06:52.653265Z

Source-reported events for the cited work

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Observation be521536-64f4-4840-86ab-002d17f8234d · outbound

This paper cites Query2uncertainty: Robust uncertainty quantification and calibration for 3d object detection under distribution shift.

Towards Trustworthy and Explainable AI for Perception Models: From Concept to Prototype Vehicle Deployment Query2uncertainty: Robust uncertainty quantification and calibration for 3d object detection under distribution shift

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T10:06:52.733222Z

Source-reported events for the cited work

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Observation ec4490e9-1602-414a-970c-5f5dcec401c0 · outbound

This paper cites Lasernet: An efficient probabilistic 3d object detector for autonomous driving.

Towards Trustworthy and Explainable AI for Perception Models: From Concept to Prototype Vehicle Deployment Lasernet: An efficient probabilistic 3d object detector for autonomous driving

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T10:06:52.707986Z

Source-reported events for the cited work

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Observation b342a3c4-8a5e-4cea-bb0e-4af6d443c0d0 · outbound

This paper cites Leveraging heteroscedastic aleatoric uncertainties for robust real-time lidar 3d object detection.

Towards Trustworthy and Explainable AI for Perception Models: From Concept to Prototype Vehicle Deployment Leveraging heteroscedastic aleatoric uncertainties for robust real-time lidar 3d object detection

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T10:06:52.661776Z

Source-reported events for the cited work

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Observation 40246654-9155-4639-8544-dc9cade9c413 · outbound

This paper cites Uncertainty-Aware Voxel based 3D Object Detection and Tracking with von-Mises Loss.

Towards Trustworthy and Explainable AI for Perception Models: From Concept to Prototype Vehicle Deployment Uncertainty-Aware Voxel based 3D Object Detection and Tracking with von-Mises Loss

Reference 49

Resolution: verified exact; arxiv_id, observed 2026-05-25T06:30:24.531727Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 5440289f-4912-4dfe-9d05-115924780f77 · outbound

This paper cites Robust collaborative 3d object detection in presence of pose errors.

Towards Trustworthy and Explainable AI for Perception Models: From Concept to Prototype Vehicle Deployment Robust collaborative 3d object detection in presence of pose errors

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T10:06:52.725041Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation e9864082-964a-427e-95b5-b749fcf1b82d · outbound

This paper cites Bevfusion: Multi-task multi-sensor fusion with unified bird’s-eye view representation.

Towards Trustworthy and Explainable AI for Perception Models: From Concept to Prototype Vehicle Deployment Bevfusion: Multi-task multi-sensor fusion with unified bird’s-eye view representation

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T10:06:52.716168Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 6bc18f6c-b6d9-4472-a289-055f4abcf175 · outbound

This paper cites Probabilistic Outputs for Support Vector Machines and Comparisons to Regularized Likelihood Methods.

Towards Trustworthy and Explainable AI for Perception Models: From Concept to Prototype Vehicle Deployment Probabilistic Outputs for Support Vector Machines and Comparisons to Regularized Likelihood Methods

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T10:06:52.695287Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 0ec90e59-36af-410f-aa57-a5dd7a9147d3 · outbound

This paper cites Accurate uncertainties for deep learning using calibrated regression.

Towards Trustworthy and Explainable AI for Perception Models: From Concept to Prototype Vehicle Deployment Accurate uncertainties for deep learning using calibrated regression

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T10:06:52.697342Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation d6300e0e-5d83-4426-b376-7a9a698750d1 · outbound

This paper cites Deep- interaction: 3d object detection via modality interaction.

Towards Trustworthy and Explainable AI for Perception Models: From Concept to Prototype Vehicle Deployment Deep- interaction: 3d object detection via modality interaction

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T10:06:52.699475Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 59103769-5747-461b-9f1d-448a80687038 · outbound

This paper cites TransFusion: Robust Lidar-Camera Fusion for 3d Object Detection with Transformers.

Towards Trustworthy and Explainable AI for Perception Models: From Concept to Prototype Vehicle Deployment TransFusion: Robust Lidar-Camera Fusion for 3d Object Detection with Transformers

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T10:06:52.650894Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 1b2b172d-1e0f-401f-b300-487d5f7ca299 · outbound

This paper cites Is- fusion: Instance-scene collaborative fusion for multimodal 3d object detection.

Towards Trustworthy and Explainable AI for Perception Models: From Concept to Prototype Vehicle Deployment Is- fusion: Instance-scene collaborative fusion for multimodal 3d object detection

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T10:06:52.709712Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation f0711d1b-9f31-4cfc-ab0c-6379d6591da2 · outbound

This paper cites karl. - A Research Vehicle for Automated and Connected Driving.

Towards Trustworthy and Explainable AI for Perception Models: From Concept to Prototype Vehicle Deployment karl. - A Research Vehicle for Automated and Connected Driving

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T10:06:52.724872Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Reference resolution

Outbound references

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