• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.6
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• pp.242-263
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• 2021

This paper presents a method to implement a low-cost driving simulator for developing autonomous driving algorithms. This is implemented by using GTA V, a physical engine-based commercial game software, containing a function to emulate output and data of various sensors for autonomous driving. For this, NF of Script Hook V is incorporated to acquire GT data by accessing internal data of the software engine, and then, various sensor data for autonomous driving are generated. We present an overall function of the developed driving simulator and perform a verification of individual functions. We explain the process of acquiring GT data via direct access to the internal memory of the game engine to build up an autonomous driving algorithm development environment. And, finally, an example applicable to artificial neural network training and performance evaluation by processing the emulated sensor output is included.

• The Journal of Korea Robotics Society
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• v.14 no.2
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• pp.122-130
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• 2019

Collecting a rich but meaningful training data plays a key role in machine learning and deep learning researches for a self-driving vehicle. This paper introduces a detailed overview of existing open-source simulators which could be used for training self-driving vehicles. After reviewing the simulators, we propose a new effective approach to make a synthetic autonomous vehicle simulation platform suitable for learning and training artificial intelligence algorithms. Specially, we develop a synthetic simulator with various realistic situations and weather conditions which make the autonomous shuttle to learn more realistic situations and handle some unexpected events. The virtual environment is the mimics of the activity of a genuine shuttle vehicle on a physical world. Instead of doing the whole experiment of training in the real physical world, scenarios in 3D virtual worlds are made to calculate the parameters and training the model. From the simulator, the user can obtain data for the various situation and utilize it for the training purpose. Flexible options are available to choose sensors, monitor the output and implement any autonomous driving algorithm. Finally, we verify the effectiveness of the developed simulator by implementing an end-to-end CNN algorithm for training a self-driving shuttle.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.23 no.1
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• pp.167-181
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• 2024

Utilization of a driving simulator in the development of autonomous driving technology allows us to perform various tests effectively in criticial environments, thereby reducing the development cost and efforts. However, there exists a serious drawback that the driving simulator has a big difference from the real environment, so a problem occurs when the autonomous driving algorithm developed using the driving simulator is applied directly to the real vehicle system. This is defined as so-called Sim2Real problem and can be classified into scenarios, sensor modeling, and vehicle dynamics. This Paper presensts on a method to solve the Sim2Real problem in autonomous driving simulator focusing on IMU sensor. In order to reduce the difference between emulated virtual IMU sensor real IMU sensor, IMU sensor emulation techniques through precision error modeling of IMU sensor are introduced. The error model of IMU sensors takes into account bias, scale factor, misalignmnet, and random walk by IMU sensor grades.

• Journal of Auto-vehicle Safety Association
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• v.14 no.1
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• pp.20-25
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• 2022

This paper presents a development of simulation environment for validation of autonomous driving (AD) algorithm based on Robot Operating System (ROS). ROS is one of the commonly-used frameworks utilized to control autonomous vehicles. For the evaluation of AD algorithm, a 3D autonomous driving simulator has been developed based on LGSVL. Two additional sensors are implemented in the simulation vehicle. First, Lidar sensor is mounted on the ego vehicle for real-time driving environment perception. Second, GPS sensor is equipped to estimate ego vehicle's position. With the vehicle sensor configuration in the simulation, the AD algorithm can predict the local environment and determine control commands with motion planning. The simulation environment has been evaluated with lane changing and keeping scenarios. The simulation results show that the proposed 3D simulator can successfully imitate the operation of a real-world vehicle.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.3
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• pp.147-159
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• 2023

In order to make autonomous vehicles more trustworthy, it is necessary to focus on the users of autonomous vehicles. By evaluating the preferences for driving behaviors of autonomous vehicles, we aim to identify driving behaviors that increase the acceptance of users in autonomous vehicles. We implemented two driving behaviors, aggressive and cautious, in a driving simulator and allowed users to experience them. Biometric data was collected during the ride, and pre- and post-riding surveys were conducted. Subjects were categorized into two groups based on their driving habits and analyzed against the collected biometric data. Both aggressive and cautious driving subjects preferred the cautious driving behavior of autonomous vehicles.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.44 no.1
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• pp.63-73
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• 2024

Autonomous vehicles at Levels 3 to 5, currently under global research and development, seek to replace the driver's perception, judgment, and control processes with various sensors integrated into the vehicle. This integration enables artificial intelligence to autonomously perform the majority of driving tasks. However, autonomous vehicles currently obtain temporary driving permits, allowing them to operate on roads if they meet minimum criteria for autonomous judgment abilities set by individual countries. When autonomous vehicles become more widespread in the future, it is anticipated that buyers may not have high confidence in the ability of these vehicles to avoid hazardous situations due to the limitations of temporary driving permits. In this study, we propose a method for grading the judgment abilities of autonomous vehicles based on a driving simulator experiment comparing and evaluating drivers' abilities to avoid hazardous situations. The goal is to derive evaluation criteria that allow for grading based on specific scenarios and to propose a framework for grading autonomous vehicles. Thirty adults (25 males and 5 females) participated in the driving simulator experiment. The analysis of the experimental results involved K-means cluster analysis and independent sample t-tests, confirming the possibility of classifying the judgment abilities of autonomous vehicles and the statistical significance of such classifications. Enhancing confidence in the risk-avoidance capabilities of autonomous vehicles in future hazardous situations could be a significant contribution of this research.

• ETRI Journal
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• v.45 no.1
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• pp.75-92
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• 2023

Level 3 autonomous vehicles require conditional autonomous driving in which autonomous and manual driving are alternately performed; whether the driver can resume manual driving within a limited time should be examined. This study investigates whether the demographics and subjective driving tendencies of drivers affect the take-over performance. We measured and analyzed the reengagement and stabilization time after a take-over request from the autonomous driving system to manual driving using a vehicle simulator that supports the driver's take-over mechanism. We discovered that the driver's reengagement and stabilization time correlated with the speeding and wild driving tendency as well as driving workload questionnaires. To verify the efficiency of subjective questionnaire information, we tested whether the driver with slow or fast reengagement and stabilization time can be detected based on machine learning techniques and obtained results. We expect to apply these results to training programs for autonomous vehicles' users and personalized human-vehicle interfaces for future autonomous vehicles.

• Design Convergence Study
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• v.17 no.4
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• pp.17-29
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• 2018

In the semi-autonomous vehicle, before reaching a fully autonomous driving stage, it is imperative for the system to issue a take-over request(TOR) that asks a driver to operate manually in a specific situation. The purpose of this study is to compare whether head-up display(HUD) is a better human-vehicle interaction than head-down display(HUD) in the event of TOR. Upon recognition of TOR in the experiment with a driving simulator, participants were prompted to switch over to manual driving after performing a secondart task, that is, playing a game, while in auto-driving mode. The results show that HUD is superior to HDD in 'ease of use' and 'satisfaction' although there is no significant difference in reaction time and subjective workload. Therefore, designing secondary tasks through HUD during autonomous driving situation improves the user experience of the TOR function. The implication of this study lies in the establishing an empirical case for setting up UX design guidelines for autonomous driving context.

• Journal of Internet Computing and Services
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• v.24 no.1
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• pp.39-47
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• 2023

The development of autonomous driving and Advanced Driver Assistance System (ADAS) technology has grown rapidly in recent years. As most traffic accidents occur due to human error, self-driving vehicles can drastically reduce the number of accidents and crashes that occur on the roads today. Obviously, technical advancements in autonomous driving can lead to improved public driving safety. However, due to the current limitations in technology and lack of public trust in self-driving cars (and drones), the actual use of Autonomous Vehicles (AVs) is still significantly low. According to prior studies, people's acceptance of an AV is mainly determined by trust. It is proven that people still feel much more comfortable in personalized ADAS, designed with the way people drive. Based on such needs, a new attempt for a customized ADAS considering each driver's driving style is proposed in this paper. Each driver's behavior is divided into two categories: assertive and defensive. In this paper, a novel customized ADAS algorithm with high classification accuracy is designed, which divides each driver based on their driving style. Each driver's driving data is collected and simulated using CARLA, which is an open-source autonomous driving simulator. In addition, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) machine learning algorithms are used to optimize the ADAS parameters. The proposed scheme results in a high classification accuracy of time series driving data. Furthermore, among the vast amount of CARLA-based feature data extracted from the drivers, distinguishable driving features are collected selectively using Support Vector Machine (SVM) technology by comparing the amount of influence on the classification of the two categories. Therefore, by extracting distinguishable features and eliminating outliers using SVM, the classification accuracy is significantly improved. Based on this classification, the ADAS sensors can be made more sensitive for the case of assertive drivers, enabling more advanced driving safety support. The proposed technology of this paper is especially important because currently, the state-of-the-art level of autonomous driving is at level 3 (based on the SAE International driving automation standards), which requires advanced functions that can assist drivers using ADAS technology.

• Journal of Korea Society of Digital Industry and Information Management
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• v.17 no.4
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• pp.31-42
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• 2021

In the modern era, radio wave analysis is necessary for various fields of engineering, and interpretation of this is also indispensable. Self-driving cars need multiple different electronic components, and thus accurate and fast electromagnetic simulator for this kind of complex radio environment is required for self-driving simulations. Accordingly, the demand for self-driving simulators as well as existing electromagnetic analysis software has increased. This paper briefly describes the characteristics of numerical analysis techniques for electromagnetic analysis, self-driving simulation software, and conventional electromagnetic simulation software and also summarizes the characteristics of each software. Finally, the verification of the result from in-house code compared to HFSS is demonstrated.