Design of Data Driven Automated Driving Control Algorithm for Enhanced Human Acceptance

This article presents a data-driven automated driving control algorithm designed to enhance human acceptance in autonomous vehicles. To achieve this, we utilize an LSTM autoencoder to extract latent driving features from collected data, which are then clustered into lateral and longitudinal behavior...

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Veröffentlicht in:	IEEE transactions on consumer electronics Jg. 71; H. 3; S. 7848 - 7863
Hauptverfasser:	Shin, Donghoon, Han, YongHa, Park, Kang-Moon
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.08.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Acceptance Algorithms autoencoder Autoencoders Automation Autonomous driving Autonomous vehicles Control algorithms Control theory Data collection Data mining data visualization deep learning Discomfort Driver behavior driving behavior Feature extraction human factor Long short term memory Passenger comfort Quality assessment ride comfort Riding quality risk assessment Roads Safety Trajectory Vehicles
ISSN:	0098-3063, 1558-4127
Online-Zugang:	Volltext
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