• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.2
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• pp.128-133
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• 2021

Currently, autonomous vehicle markets are commercializing a third-level autonomous vehicle, but there is a possibility that an accident may occur even during fully autonomous driving due to stability issues. In fact, autonomous vehicles have recorded 81 accidents. This is because, unlike level 3, autonomous vehicles after level 4 have to judge and respond to emergency situations by themselves. Therefore, this paper proposes a vehicle crisis detection system(VCDS) that collects and stores information outside the vehicle through CNN, and uses the stored information and vehicle sensor data to output the crisis situation of the vehicle as a number between 0 and 1. The VCDS consists of two modules. The vehicle external situation collection module collects surrounding vehicle and pedestrian data using a CNN-based neural network model. The vehicle crisis situation determination module detects a crisis situation in the vehicle by using the output of the vehicle external situation collection module and the vehicle internal sensor data. As a result of the experiment, the average operation time of VESCM was 55ms, R-CNN was 74ms, and CNN was 101ms. In particular, R-CNN shows similar computation time to VESCM when the number of pedestrians is small, but it takes more computation time than VESCM as the number of pedestrians increases. On average, VESCM had 25.68% faster computation time than R-CNN and 45.54% faster than CNN, and the accuracy of all three models did not decrease below 80% and showed high accuracy.

• Journal of Venture Innovation
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• v.5 no.4
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• pp.91-108
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• 2022

This study is an empirical analysis regarding what kind of factors affect the intention to use autonomous vehicles. For the empirical analysis the research model was derived from value-based adoption model base and integrated some aspects that only autonomous vehicles have. At default variables of VAM are usefulness, enjoyment, technicality, perceived cost, some autonomous vehicle related variables were added, and those are convenience, safety, security, social influence. A survey was done in order to empirically analyze with this research model, and 216 valid survey answers were chosen to analyze. Empirical analysis was done by structural equation using AMOS24. The result of empirical analysis were as follows. Variables usefulness, enjoyment, safety, security had a significant positive effect on perceived value. Technicality and perceived cost had a significant negative effect of perceived value. In addition, security and social influence had no significant effect on perceived value. Furthermore, perceived value had significant positive effect on intention to use. Among the variables that came out to be significantly positive, the most influencing variable was safety, followed by convenience, perceived cost, enjoyment, usefulness and then technicality. In addition, the analysis of mediating effect of perceived value shows that usefulness, enjoyment, convenience, safety, technicality, perceived cost had mediating role towards intention to use. However, security and social influence had no siginificant mediating effect towards intention to use. Considering all these research results this study has provided theoretical and practical implications to researchers on the intention to use autonomous vehicles.

• Journal of the Korea Convergence Society
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• v.9 no.1
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• pp.253-259
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• 2018

The Korea Convergence Society. Recently, as the 4th industrial revolution is rising, there are many changes in various field of industries. Among these industries, autonomous vehicles based on artificial intelligence, big data and internet of things is one of the most promising industry. Autonomous vehicle stray from classical car domain of manufacturers and suppliers, IT/Electronics suppliers and communication companies are widen their business area to autonomous vehicle technology. In this paper, we analysis the state of art of autonomous vehicle technology and development direction of industries/research institute. Finally, we discuss the social/economic effects of autonomous vehicle.

• Journal of Auto-vehicle Safety Association
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• v.12 no.1
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• pp.15-25
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• 2020

This paper presents a longitudinal control algorithm for ensuring takeover time of autonomous vehicle using V2V communication. In the autonomous driving of more than level 3, autonomous systems should control the vehicles by itself partially. However if the driver's intervention is required for functional safety, the driver should take over the control reasonably. Autonomous driving system has to be designed so that drivers can take over the control from autonomous vehicle reasonably for driving safety. In this study, control algorithm considering takeover time has been developed based on computation method of takeover time. Takeover time is analysed by conditions of longitudinal velocity of preceding vehicle in time-velocity plane. In addition, desired clearance is derived based on takeover time. The performance evaluation of the proposed algorithm in this study was conducted using 3D vehicle model with actual driving data in Matlab/Simulink environment. The results of the performance evaluation show that the longitudinal control algorithm can control while securing takeover time reasonably.

• 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.

• Journal of Auto-vehicle Safety Association
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• v.12 no.4
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• pp.13-22
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• 2020

This paper presents an adaptive feedback based actuator fault detection and tolerant control algorithms for longitudinal functional safety of autonomous driving. In order to ensure the functional safety of autonomous vehicles, fault detection and tolerant control algorithms are needed for sensors and actuators used for autonomous driving. In this study, adaptive feedback control algorithm to compute the longitudinal acceleration for autonomous driving has been developed based on relationship function using states. The relationship function has been designed using feedback gains and error states for adaptation rule design. The coefficients in the relationship function have been estimated using recursive least square with multiple forgetting factors. The MIT rule has been adopted to design the adaptation rule for feedback gains online. The stability analysis has been conducted based on Lyapunov direct method. The longitudinal acceleration computed by adaptive control algorithm has been compared to the actual acceleration for fault detection of actuators used for longitudinal autonomous driving.

• ETRI Journal
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• v.45 no.5
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• pp.847-861
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• 2023

Dynamic object detection is essential for ensuring safe and reliable autonomous driving. Recently, light detection and ranging (LiDAR)-based object detection has been introduced and shown excellent performance on various benchmarks. Although LiDAR sensors have excellent accuracy in estimating distance, they lack texture or color information and have a lower resolution than conventional cameras. In addition, performance degradation occurs when a LiDAR-based object detection model is applied to different driving environments or when sensors from different LiDAR manufacturers are utilized owing to the domain gap phenomenon. To address these issues, a sensor-fusion-based object detection and classification method is proposed. The proposed method operates in real time, making it suitable for integration into autonomous vehicles. It performs well on our custom dataset and on publicly available datasets, demonstrating its effectiveness in real-world road environments. In addition, we will make available a novel three-dimensional moving object detection dataset called ETRI 3D MOD.

• International Journal of Aeronautical and Space Sciences
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• v.10 no.1
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• pp.30-36
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• 2009

Many formation guidance laws have been proposed for VAV formation flight. Since most autonomous formation flight methods require various active communication links between the vehicles to know motion information of other vehicles, damage to the receiver or the transmitter and communication delay are critical problem to achieve a given formation flight mission. Therefore, in this point of view, the method that does not need an inter-vehicle communication is preferred in the autonomous formation flight. In this paper, we first summarize the formation guidance law without an inter-vehicle communication using feedback linearization and sliding mode control proposed in previous study. We also propose the modified formation guidance law with robust disturbance observer, which can provide significantly better performance than previously mentioned guidance law in case that other vehicles maneuver with large accelerations. The robust disturbance observer can estimate uncertainties generated by acceleration of leader vehicle. By eliminating the uncertainties using the estimated uncertainties, VAVs are able to achieve the tight formation flight. The performance of the proposed approach is validated by numerical simulations.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.11
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• pp.5299-5320
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• 2019

Interest in self-driving vehicle research has been rapidly increasing, and related research has been continuously conducted. In such a fast-paced self-driving vehicle research area, the development of advanced technology for better convenience safety, and efficiency in road and transportation systems is expected. Here, we investigate research in self-driving vehicles and analyze the main technologies of driverless car software, including: technical aspects of autonomous vehicles, traffic infrastructure and its communications, research techniques with vision recognition, deep leaning algorithms, localization methods, existing problems, and future development directions. First, we introduce intelligent self-driving car and road infrastructure algorithms such as machine learning, image processing methods, and localizations. Second, we examine the intelligent technologies used in self-driving car projects, autonomous vehicles equipped with multiple sensors, and interactions with transport infrastructure. Finally, we highlight the future direction and challenges of self-driving vehicle transportation systems.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.8
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• pp.633-642
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• 2016

It is important for advanced active safety systems and autonomous driving cars to get the accurate estimates of the nearby vehicles in order to increase their safety and performance. This paper proposes a sensor fusion method for radar and vision sensors to accurately estimate the state of the preceding vehicles. In particular, we performed a study on compensating for the lateral state error on automotive radar sensors by using a vision sensor. The proposed method is based on the Interactive Multiple Model(IMM) algorithm, which stochastically integrates the multiple Kalman Filters with the multiple models depending on lateral-compensation mode and radar-single sensor mode. In addition, a Probabilistic Data Association Filter(PDAF) is utilized as a data association method to improve the reliability of the estimates under a cluttered radar environment. A two-step correction method is used in the Kalman filter, which efficiently associates both the radar and vision measurements into single state estimates. Finally, the proposed method is validated through off-line simulations using measurements obtained from a field test in an actual road environment.