• Journal of Positioning, Navigation, and Timing
• /
• v.11 no.4
• /
• pp.351-360
• /
• 2022

Autonomous vehicles require precise knowledge of their position, velocity and orientation in all weather and traffic conditions in any time. And, these information is effectively used for path planning, perception, and control that are key factors for safety of vehicle driving. For this purpose, a high precision GNSS technology is widely adopted in autonomous vehicles as a core localization and navigation method. However, due to the lack of infrastructure as well as cost issue regarding GNSS correction data communication, only a few high precision GNSS technology will be available for future commercial autonomous vehicles. Recently, a high precision GNSS sensor that is based on a Broadcast-RTK system to dramatically reduce network maintenance cost by utilizing the existing broadcasting network is released. In this paper, we present the performance test result of the broadcast-RTK-based commercial high precision GNSS receiver to test the feasibility of the system for autonomous driving in Korea. Massive measurement campaigns covering of Korea region were performed, and the obtained measurements were analyzed in terms of ambiguity fixing rate, integer ambiguity loss recovery, time to retry ambiguity fixing, average correction information update rate as well as accuracy in comparison to other high precision systems.

• Journal of the Korean Society of Systems Engineering
• /
• v.17 no.2
• /
• pp.129-138
• /
• 2021

Today, as the number of vehicles equipped with autonomous driving functions increases, the use of various sensors increases, and the complexity of system configuration increases. The ISO 26262 standard was published to prevent caused by systematic errors. Recently, the issue of external environmental factors rather than mechanical failure has increased. This issue is a problem outside of the scope of ISO 26262, and the ISO/DIS 21448 standard was published to solve this problem. Also, Mobileye proposed the RSS model that defined safe distance for dangerous situations in order to secure the safety of autonomous vehicles and who is responsible in case of an accident. In this paper, integrated process of ISO 21448 and RSS model, and through these results, we expect that possible to contribute to securing the safety and reliability of autonomous vehicles in the future.

• Journal of Auto-vehicle Safety Association
• /
• v.14 no.2
• /
• pp.14-19
• /
• 2022

This paper presents motion planning algorithm that yields to intervening side lane vehicles in a congested traffic flow based on vehicle to vehicle (V2V) communication. Autonomous driving in dense traffic situation requires advanced driving performance in terms of vehicle interaction and risk mitigation. One of the most important functions necessary for congested traffic autonomous driving is to predict the lane change intention of the side lane target vehicle. However, implementing this function by using only environmental sensors has limitations. In this study, V2V communication is used to overcome the limitations and determine the intention of cut-in vehicles. Lane change intention of the intervening side lane vehicle is inferred by its longitudinal speed, steering angle, and turn signal light information received by the on-board-unit (OBU). Once the yield decision is made, the subject vehicle decelerates to generate sufficient clearance for the target vehicle to enter. Validation of the algorithm was conducted with actual autonomous test vehicles.

• ETRI Journal
• /
• v.44 no.4
• /
• pp.641-653
• /
• 2022

In the process of autonomous vehicle motion planning and to create comfort for vehicle occupants, factors that must be considered are the vehicle's safety features and the road's slipperiness and smoothness. In this paper, we build a mathematical model based on the combination of a genetic algorithm and a neural network to offer lane-change solutions of autonomous vehicles, focusing on human vehicle control skills. Traditional moving planning methods often use vehicle kinematic and dynamic constraints when creating lane-change trajectories for autonomous vehicles. When comparing this generated trajectory with a man-generated moving trajectory, however, there is in fact a significant difference. Therefore, to draw the optimal factors from the actual driver's lane-change operations, the solution in this paper builds the training data set for the moving planning process with lane change operation by humans with optimal elements. The simulation results are performed in a MATLAB simulation environment to demonstrate that the proposed solution operates effectively with optimal points such as operator maneuvers and improved comfort for passengers as well as creating a smooth and slippery lane-change trajectory.

• Journal of Sensor Science and Technology
• /
• v.22 no.2
• /
• pp.111-117
• /
• 2013

The autonomous driving method using magnetic sensors recognizes the position by measuring magnetic fields in autonomous robots or vehicles after installing magnetic markers in a moving path. The Position estimate method using magnetic sensors has an advantage of being affected less by variation of driving environment such as oil, water and dust due to the use of magnetic field. It also has the advantages that we can use the magnet as an indicator and there is no consideration for power and communication environment. In this paper, we propose an efficient sensor system for an autonomous driving vehicle supplemented for existing disadvantage. In order to efficiently eliminate geomagnetism, we analyze the components of the horizontal and vertical magnetic field. We propose an algorithm for position estimation and geomagnetic elimination to ease analysis, and also propose an initialization method for sensor applied in the vehicle. We measured and analyzed the developed system in various environments, and we verify the advantages of proposed methods.

• ETRI Journal
• /
• v.37 no.3
• /
• pp.617-625
• /
• 2015

In this paper, a steering control system for the path tracking of autonomous vehicles is described. The steering control system consists of a path tracker and primitive driver. The path tracker generates the desired steering angle by using the look-ahead distance, vehicle heading, and a lateral offset. A method for applying an autonomous vehicle to path tracking is an advanced pure pursuit method that can reduce cutting corners, which is a weakness of the pure pursuit method. The steering controller controls the steering actuator to follow the desired steering angle. A servo motor is installed to control the steering handle, and it can transmit the steering force using a belt and pulley. We designed a steering controller that is applied to a proportional integral differential controller. However, because of a dead band, the path tracking performance and stability of autonomous vehicles are reduced. To overcome the dead band, a dead band compensator was developed. As a result of the compensator, the path tracking performance and stability are improved.

• Electronics and Telecommunications Trends
• /
• v.33 no.6
• /
• pp.107-117
• /
• 2018

In recent years, with the development of autonomous driving technology, high-performance artificial intelligence computing hardware platforms have been developed that can process multi-sensor data, object recognition, and vehicle control for autonomous vehicles. Most of these hardware platforms have been developed overseas, such as NVIDIA's DRIVE PX, Audi's zFAS, Intel GO, Mobile Eye's EyeQ, and BAIDU's Apollo Pilot. In Korea, however, ETRI's artificial intelligence computing platform has been developed. In this paper, we discuss the specifications, structure, performance, and development status centering on hardware platforms that support autonomous driving rather than the overall contents of autonomous driving technology.

• Journal of Aerospace System Engineering
• /
• v.15 no.2
• /
• pp.36-44
• /
• 2021

Unmanned aerial vehicles (UAVs) are increasingly needed as they can replace manned aircrafts in dangerous military missions. However, because of their low autonomy, current UAVs can execute missions only under continuous operator control. To overcome this limitation, higher autonomy levels of UAVs based on autonomous situational awareness is required. In this paper, we propose an autonomous situational awareness software consisting of situation awareness management, threat recognition, threat identification, and threat space analysis to detect dynamic situational change by external threats. We implemented the proposed software in real mission computer hardware and evaluated the performance of situational awareness toward dynamic radar threats in flight simulations.

• International Journal of Internet, Broadcasting and Communication
• /
• v.14 no.2
• /
• pp.162-170
• /
• 2022

Today, automobiles have become an indispensable element in people's lives, and the distribution of vehicles with various autonomous driving functions is expanding. Sensors such as cameras are used to recognize various situations on the road as an essential element for autonomous driving functions, but camera sensors have disadvantages that are vulnerable to bad weather. In this paper, we present a derivation process that defines external weather environment factors that negatively affect the performance of a camera for an autonomous vehicle. Through the proposed process, it is expected that it will contribute to securing the reliability of the camera and further improving the safety of autonomous vehicles.

• Electronics and Telecommunications Trends
• /
• v.36 no.2
• /
• pp.1-11
• /
• 2021

As sensors such as Inertial Measurement Unit, cameras, and Light Detection and Rangings have become cheaper and smaller, research has been actively conducted to implement functions automating micro aerial vehicles such as multirotor type drones. This would fully enable the autonomous flight of drones in the real world without human intervention. In this article, we present a survey of state-of-the-art development on autonomous drones. To build an autonomous drone, the essential components can be classified into pose estimation, environmental perception, and obstacle-free trajectory generation. To describe the trend, we selected three leading research groups-University of Pennsylvania, ETH Zurich, and Carnegie Mellon University-which have demonstrated impressive experiment results on automating drones using their estimation, perception, and trajectory generation techniques. For each group, we summarize the core of their algorithm and describe how they implemented those in such small-sized drones. Finally, we present our up to date research status on developing an autonomous drone.