Citation: Shin, S.-S.; Kang, H.-J.;
Kwon, S.-J. A Study on Data Analysis
for Improving Driving Safety in Field
Operational Test (FOT) of
Autonomous Vehicles. Machines 2022,
10, 784. https://doi.org/10.3390/
machines10090784
Academic Editors: Shuai Li,
Dechao Chen, Mohammed
Aquil Mirza, Vasilios N. Katsikis,
Dunhui Xiao and
Predrag Stanimirovi´c
Received: 26 July 2022
Accepted: 5 September 2022
Published: 7 September 2022
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Article
A Study on Data Analysis for Improving Driving Safety in Field
Operational Test (FOT) of Autonomous Vehicles
Seok-San Shin, Ho-Joon Kang and Seong-Jin Kwon *
Vehicle Safety R&D Center, Korea Automotive Technology Institute (KATECH), 201, Gukgasandanseo-ro,
Guji-myeon, Dalseong-gun, Daegu 43011, Korea
* Correspondence: sjkwon@katech.re.kr; Tel.: +82-53-719-7831
Abstract:
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.
Keywords: autonomous vehicle; field operational test; test scenarios; data analysis
1. Introduction
As ADAS (advanced driving-assisted system) and autonomous driving-related tech-
nologies improve, projects and research on their demonstration are continuously being
developed. In Europe, co-operative autonomous technology demonstrations such as Adap-
tive (2014–2017) [
1
] and DriveMe (2014–2017) [
2
], which provide guidelines and technology
demonstrations, and in which 100 vehicles have participated, are promoted as the most
advanced projects. In addition, the FESTA handbook [
3
] presents a methodology consisting
of a V-cycle divided into FOT preparation, data acquisition, and analysis.
The major European FOT cases for autonomous driving-related demonstration are euro-
FOT (2008–2012) [
4
], FOTsis (2011–2014) [
5
], DriveC2X (2011–2014) [
6
], TeleFOT (
2008–2012
) [
7
],
and the Pegasus Project (2016~2019) [
8
], etc. In the case of the United States, there is the PATH
project (2000~2012) [
9
], Safety Pilot (2011~2013) [
10
], IVBSS (2005~2011) [
11
], M-city (2015) [
12
],
etc. In Japan, there is the Woven City (2021-present) project [
13
]. In South Korea, there are
various demonstration environments and service demonstration cases, such as K-city
(2015~2017), 5G infrastructure construction projects (2020~present), and the establishment
of a future car digital convergence industry demonstration platform (2021~present). In ad-
dition to self-driving-related demonstrations, research on scenarios and ODD (operational
design domain) is constantly being carried out.
Regarding autonomous driving-related research, various studies are being conducted
based on levels 0–5 proposed by SAE [
14
]. As studies on self-driving cars on SAE levels
0~2 continue, the mass production of autonomous vehicles designed to function in envi-
ronments without traffic lights, such as on more remote highways, is gradually emerging.
As self-driving demonstrations and studies increase, it is necessary to study the
systematic process of these research methods and, based on this, to develop an analysis
method for the experimental results. In particular, autonomous driving-related accidents
have continuously occurred since the operation of autonomous vehicles, and according to
statistics released for California, USA, as of June 2022, 483 self-driving vehicle crashes have
Machines 2022, 10, 784. https://doi.org/10.3390/machines10090784 https://www.mdpi.com/journal/machines
