Open-World Learning for Traffic Scenarios Categorisation
Categorisation of traffic scenarios is an important component of scenario-based development and validation of automated vehicles. This problem requires an open-world learning approach but most of the machine learning methods used for traffic scenario categorisation work under the closed-world assumption. A closed-world model will classify all the inputs to one of the classes from the training data. An open-world learning method can identify, collect and cluster unknown traffic scenarios and incrementally add new scenario categories to the already existing ones. In this work, a hierarchical architecture for open-world learning method is proposed. The open-world architecture consists of the following components: an open-set recognition model, storage buffer, outlier detection, class-conditioned generative replay model, and clustering method. The components in the architecture contain novel machine learning approaches to address the challenging open-world learning tasks, e.g., Extreme Value Theory (EVT) for open-set recognition, Random Forest Activation Patterns (RFAPs) for clustering, class-conditioned generative models for replay, and self-supervised pre-training for feature generation. The proposed architecture is tested using real-world and simulation-based datasets. The results show the performance advantages of the proposed method. Also, extensive analysis of each component of the hierarchical open-world architecture underlines their importance in the overall architecture.
- Record URL:
-
Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/23798858
-
Supplemental Notes:
- Copyright © 2023, IEEE.
-
Authors:
- Balasubramanian, Lakshman
- Wurst, Jonas
- Botsch, Michael
- Deng, Ke
- Publication Date: 2023-5
Language
- English
Media Info
- Media Type: Web
- Features: Figures; References;
- Pagination: pp 3506-3521
-
Serial:
- IEEE Transactions on Intelligent Vehicles
- Volume: 8
- Issue Number: 5
- Publisher: Institute of Electrical and Electronics Engineers (IEEE)
- ISSN: 2379-8858
- Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=7274857
Subject/Index Terms
- TRT Terms: Autonomous vehicles; Computer architecture; Machine learning; Radio frequency; Traffic forecasting; Traffic simulation
- Subject Areas: Data and Information Technology; Highways; Planning and Forecasting; Vehicles and Equipment;
Filing Info
- Accession Number: 01909414
- Record Type: Publication
- Files: TRIS
- Created Date: Feb 22 2024 4:14PM