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

The structure of roads plays an important role in designing autonomous driving algorithms. We propose a novel road graph based driving framework, named RIANet++. The proposed framework considers the road structural scene context by incorporating both graphical features of the road and visual informa...

Full description

Saved in:
Bibliographic Details
Published inIEEE robotics and automation letters Vol. 8; no. 11; pp. 7815 - 7822
Main Authors Ha, Taeoh, Oh, Jeongwoo, Lee, Gunmin, Heo, Jaeseok, Kim, Do Hyung, Park, Byungkyu, Lee, Chang-Gun, Oh, Songhwai
Format Journal Article
LanguageEnglish
Published Piscataway IEEE 01.11.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Algorithms
Autonomous vehicle navigation
Autonomous vehicles
Cameras
Change detection
Graph matching
imitation learning
Performance degradation
Roads
Roads & highways
Robot sensing systems
Semantics
sensor fusion
Similarity
Vehicles
vision-based navigation
Visualization
Online AccessGet full text

Cover

More Information
Summary:The structure of roads plays an important role in designing autonomous driving algorithms. We propose a novel road graph based driving framework, named RIANet++. The proposed framework considers the road structural scene context by incorporating both graphical features of the road and visual information through the attention mechanism. Also, the proposed framework can deal with the performance degradation problem, caused by road changes and corresponding road graph data unreliability. For this purpose, we suggest a road change detection module which can filter out unreliable road graph data by evaluating the similarity between the camera image and the query road graph. In this letter, we suggest two types of detection methods, semantic matching and graph matching. The semantic matching (resp., graph matching) method computes the similarity score by transforming the road graph data (resp., camera data) into the semantic image domain (resp., road graph domain). In experiments, we test the proposed method in two driving environments: the CARLA simulator and the FMTC real-world environment. The experiment results demonstrate that the proposed driving framework outperforms other baselines and operates robustly under road changes.
ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2023.3320491