• Journal of Navigation and Port Research
• /
• v.47 no.2
• /
• pp.66-74
• /
• 2023

With the increase of interest in developing Maritime Autonomous Surface Ships (MASS), an optimal ship route planning is gradually gaining popularity as one of the important subsystems for autonomy of modern marine vessels. In the present paper, an optimal ship route planning model for MASS is proposed using a nonlinear MPC approach together with a nonlinear MMG model. Results drawn from this study demonstrated that the optimization problem for the ship route was successfully solved with satisfaction of the nonlinear dynamics of the ship and all constraints for the state and manipulated variables using the nonlinear MPC approach. Given that a route generation system capable of accounting for nonlinear dynamics of the ship and equality/inequality constraints is essential for achieving fully autonomous navigation at sea, it is expected that this paper will contribute to the field of autonomous vehicles by demonstrating the performance of the proposed optimal ship route planning model.

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

• Proceedings of the Korean Institute of Navigation and Port Research Conference
• /
• 2022.11a
• /
• pp.357-359
• /
• 2022

Autonomous decision-making is key to safe and efficient marine autonomy, as global marine industry comprises over 90 percent of the world's cargo transportation. Challenges of the real-world validation in the aquatic domain limits the wide-spread of ASVs despite their promising societal impacts. We propose and demonstrate the real-world validation platform and comprehensive algorithm steps. Such a framework will serve as a more explainable and reliable decision-making system of ASVs as well as autonomous vehicles in other domains.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.23 no.1
• /
• pp.35-45
• /
• 2013

A autonomous vehicle requires improved and robust perception systems than conventional perception systems of intelligent vehicles. In particular, single sensor based perception systems have been widely studied by using cameras and laser radar sensors which are the most representative sensors for perception by providing object information such as distance information and object features. The distance information of the laser radar sensor is used for road environment perception of road structures, vehicles, and pedestrians. The image information of the camera is used for visual recognition such as lanes, crosswalks, and traffic signs. However, single sensor based perception systems suffer from false positives and true negatives which are caused by sensor limitations and road environments. Accordingly, information fusion systems are essentially required to ensure the robustness and stability of perception systems in harsh environments. This paper describes a perception system for autonomous vehicles, which performs information fusion to recognize road environments. Particularly, vision and laser radar sensors are fused together to detect lanes, crosswalks, and obstacles. The proposed perception system was validated on various roads and environmental conditions with an autonomous vehicle.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.19 no.6
• /
• pp.639-645
• /
• 2018

This paper is intended to develop a decision model that can be applied to autonomous vehicles and autonomous mobile vehicles. The developed module has an independent configuration for application in various driving environments and is based on a platform for organically operating them. Each module is studied for decision making on lane changes and for securing safety through reinforcement learning using a deep learning technique. The autonomous mobile moving body operating to change the driving state has a characteristic where the next operation of the mobile body can be determined only if the definition of the speed determination model (according to its functions) and the lane change decision are correctly preceded. Also, if all the moving bodies traveling on a general road are equipped with an autonomous driving function, it is difficult to consider the factors that may occur between each mobile unit from unexpected environmental changes. Considering these factors, we applied the decision model to the platform and studied the lane change decision system for implementation of the platform. We studied the decision model using a modular learning method to reduce system complexity, to reduce the learning time, and to consider model replacement.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.22 no.5
• /
• pp.148-162
• /
• 2023

The objective of this study is to derive the appropriate perception location for changes in driving behavior of autonomous vehicles in urban road environments based on traffic safety signs. For this purpose, 32 types of signs that induce changes in driving behavior were selected from currently used traffic safety signs and classified as three types according to changes in driving behavior. Based on this, three scenarios were designed: stop, speed change, and lane change scenarios. These were used to confirm the impact on traffic flow. As a result of the analysis, it was found that each scenario needs to receive information on traffic safety signs in advance to ensure changes in traffic flow and safety. Consequently, the appropriate perception location can be used as a basis for establishing standards for delivering message sets to autonomous vehicles or revising traffic safety signs for them. In addition, this study is expected to contribute to the establishment of safe and efficient driving strategies on urban roads as autonomous vehicles are introduced in the future.

• ETRI Journal
• /
• v.43 no.4
• /
• pp.603-616
• /
• 2021

Over the last few years, autonomous vehicles have progressed very rapidly. The odometry technique that estimates displacement from consecutive sensor inputs is an essential technique for autonomous driving. In this article, we propose a fast, robust, and accurate odometry technique. The proposed technique is light detection and ranging (LiDAR)-based direct odometry, which uses a spherical range image (SRI) that projects a three-dimensional point cloud onto a two-dimensional spherical image plane. Direct odometry is developed in a vision-based method, and a fast execution speed can be expected. However, applying LiDAR data is difficult because of the sparsity. To solve this problem, we propose an SRI generation method and mathematical analysis, two key point sampling methods using SRI to increase precision and robustness, and a fast optimization method. The proposed technique was tested with the KITTI dataset and real environments. Evaluation results yielded a translation error of 0.69%, a rotation error of 0.0031°/m in the KITTI training dataset, and an execution time of 17 ms. The results demonstrated high precision comparable with state-of-the-art and remarkably higher speed than conventional techniques.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.17 no.1
• /
• pp.49-58
• /
• 2022

With the recent start of the 4th Industrial Revolution, markets related to autonomous driving are rapidly developing. In order to understand the rapidly developed technology trend of autonomous driving technology, we would like to investigate the characteristics and differences of level 0 to level 5 of autonomous driving. The overall configuration, recognition technology, and auxiliary technologies of autonomous vehicles are analyzed, and through this, the structure and algorithm of autonomous driving technology are identified. In addition, by manufacturing a simulated autonomous RC car using an ultrasonic sensor and a camera, the necessity of recognition technology and auxiliary technology is identified.

• Journal of Drive and Control
• /
• v.19 no.4
• /
• pp.110-116
• /
• 2022

Recently, various studies have been conducted to apply deep learning and AI to various fields of autonomous driving, such as recognition, sensor processing, decision-making, and control. This paper proposes a controller applicable to path following, static obstacle avoidance, and pedestrian avoidance situations by utilizing reinforcement learning in autonomous vehicles. For repetitive driving simulation, a reinforcement learning environment was constructed using virtual environments. After learning path following scenarios, we compared control performance with Pure-Pursuit controllers and Stanley controllers, which are widely used due to their good performance and simplicity. Based on the test case of the KNCAP test and assessment protocol, autonomous emergency steering scenarios and autonomous emergency braking scenarios were created and used for learning. Experimental results from zero collisions demonstrated that the reinforcement learning controller was successful in the stationary obstacle avoidance scenario and pedestrian collision scenario under a given condition.

• Journal of Auto-vehicle Safety Association
• /
• v.10 no.2
• /
• pp.29-35
• /
• 2018

This paper presents a quantitative evaluation method and result of moving vehicle perception using automotive radar. It is also important to analyze the accuracy of the perception algorithm quantitatively as well as to accurately percept nearby moving vehicles for safe and efficient autonomous driving. In this study, accuracy of the automotive radar-based perception algorithm which is developed based on interacting multiple model (IMM) has been verified via vehicle tests on real roads. In order to obtain experimental data for quantitative evaluation, Long Range Radar (LRR) has been mounted on the front of the ego vehicle and Short Range Radar (SRR) has been mounted on the rear side of both sides. RT-range has been installed on the ego vehicle and the target vehicle to simultaneously collect reference data on the states of the two vehicles. The experimental data is acquired in various relative positions and velocity, and the accuracy of the algorithm has been analyzed according to relative position and velocity. Quantitative analysis is conducted on relative position, relative heading angle, absolute velocity, and yaw rate of each vehicle.