Open-World Learning for Traffic Scenarios Categorisation
Author:
Affiliation:
1. Technische Hochschule Ingolstadt, Carissma Institute of Automated Driving, Ingolstadt, Germany
2. Royal Melbourne Institute of Technology, Melbourne, VIC, Australia
Funder
Bayerisches Staatsministerium für Wissenschaft und Kunst
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Artificial Intelligence,Control and Optimization,Automotive Engineering
Link
http://xplorestaging.ieee.org/ielx7/7274857/10136408/10078164.pdf?arnumber=10078164
Reference54 articles.
1. Taxonomy and definitions for terms related to driving automation systems for on-road motor vehicles,2021
2. Pegasus research project;Cologne,2017
3. Clustering Traffic Scenarios Using Mental Models as Little as Possible
4. Large Scale Autonomous Driving Scenarios Clustering with Self-supervised Feature Extraction
5. Clustering of Driving Encounter Scenarios Using Connected Vehicle Trajectories
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2. Augmenting Scenario Description Languages for Intelligence Testing of Automated Driving Systems;2024 IEEE Intelligent Vehicles Symposium (IV);2024-06-02
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