RIANet++: Road Graph and Image Attention Networks for Robust Urban Autonomous Driving Under Road Changes

Author:

Ha Taeoh1ORCID,Oh Jeongwoo1,Lee Gunmin1,Heo Jaeseok1,Kim Do Hyung2,Park Byungkyu2,Lee Chang-Gun2,Oh Songhwai1ORCID

Affiliation:

1. Department of Electrical and Computer Engineering and ASRI, Seoul National University, Seoul, South Korea

2. Department of Computer Science and Engineering, Seoul National University, Seoul, South Korea

Funder

Institute of Information & Communications Technology Planning & Evaluation

Korea government

Robot Learning: Efficient, Safe, and Socially-Acceptable Machine Learning

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Artificial Intelligence,Control and Optimization,Computer Science Applications,Computer Vision and Pattern Recognition,Mechanical Engineering,Human-Computer Interaction,Biomedical Engineering,Control and Systems Engineering

Reference26 articles.

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3. Semi-supervised classification with graph convolutional networks;kipf,0

4. Trust, but verify: Cross-modality fusion for HD map change detection;lambert;Adv Neural Inf Process Syst Track Datasets Benchmarks,0

5. HD Map Change Detection with a Boosted Particle Filter

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