A Study on Data Analysis for Improving Driving Safety in Field Operational Test (FOT) of Autonomous Vehicles

Autor: Seok-San Shin, Ho-Joon Kang, Seong-Jin Kwon
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: Machines, Vol 10, Iss 9, p 784 (2022)
Druh dokumentu: article
ISSN: 2075-1702
DOI: 10.3390/machines10090784
Popis: In this study, an autonomous driving test was conducted from the perspective of FOT (field operational test). For data analysis and improvement methods, scenarios for FOT were classified and defined by considering autonomous driving level (SAE J3016) and the viewpoints of the vehicle, driver, road, environment, etc. To obtain data from FOT, performance indicators were selected, a data collection environment was implemented in the test cases, and driving roads were selected to obtain driving data from the vehicle while it was driven on an actual road. In the pilot FOT course, data were collected in various driving situations using a test vehicle, and the effect of autonomous driving-related functions on improving driving safety was studied through data analysis of discovered major events.
Databáze: Directory of Open Access Journals