AutoGrAN: Autonomous Vehicle LiDAR Contaminant Detection using Graph Attention Networks-Reference-Cited by-同舟云学术

AutoGrAN: Autonomous Vehicle LiDAR Contaminant Detection using Graph Attention Networks

Published:2024-05-07 Issue: Volume: Page:112-119
ISSN:
Container-title:Companion of the 15th ACM/SPEC International Conference on Performance Engineering
language:
Short-container-title:

Author:

Jati Grafika¹^ORCID,Molan Martin¹^ORCID,Khan Junaid Ahmed¹^ORCID,Barchi Francesco¹^ORCID,Bartolini Andrea¹^ORCID,Mercurio Giuseppe²^ORCID,Acquaviva Andrea¹^ORCID

Affiliation:

1. DEI Department, University of Bologna, Bologna, Italy

2. FEV Italia s.r.l., Bologna, Italy

Funder

EU National Recovery and Resilience Plan (NRRP) Mission 4, Component 2, Investment 3.3

FEV Italia s.r.l.

The UAE Technology Innovation Institute (TII) through the Zero-Trust project

The Graph-Massivizer project

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3629527.3652896

Reference25 articles.

1. Positioning and perception in LIDAR point clouds

2. Daniel Bogdoll et al. 2022. Anomaly detection in autonomous driving: A survey. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 4488--4499.

3. Krzysztof Czarnecki and Rick Salay. 2018. Towards a Framework to Manage Perceptual Uncertainty for Safe Automated Driving. In Computer Safety, Reliability, and Security, Barbara Gallina, Amund Skavhaug, Erwin Schoitsch, and Friedemann Bitsch (Eds.). Springer International Publishing, Cham, 439--445.

4. Voxel R-CNN: Towards High Performance Voxel-based 3D Object Detection

5. Yinpeng Dong, Caixin Kang, Jinlai Zhang, Zijian Zhu, Yikai Wang, Xiao Yang, Hang Su, Xingxing Wei, and Jun Zhu. 2023. Benchmarking Robustness of 3D Object Detection to Common Corruptions. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 1022--1032.