Testing Scenario Identification for Automated Vehicles Based on Deep Unsupervised Learning

Testing Scenario Identification for Automated Vehicles Based on Deep Unsupervised Learning

Naturalistic driving data (NDD) are valuable for testing autonomous driving systems under various driving conditions. Automatically identifying scenes from high-dimensional and unlabeled NDD remains a challenging task. This paper presents a novel approach for automatically identifying test scenarios...

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Bibliographic Details
Main Authors: Shuai Liu, Fan Ren, Ping Li, Zhijie Li, Hao Lv, Yonggang Liu
Format: Article
Language:English
Published: MDPI AG 2023-08-01
Series:World Electric Vehicle Journal
Subjects:
autonomous driving
scenario identification
naturalistic driving data
one-dimensional residual convolutional autoencoder
optimized K-means algorithm
Online Access:https://www.mdpi.com/2032-6653/14/8/208

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https://www.mdpi.com/2032-6653/14/8/208

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