145 research outputs found

    Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

    Full text link
    Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs

    Traffic light detection and V2I communications of an autonomous vehicle with the traffic light for an effective intersection navigation using MAVS simulation

    Get PDF
    Intersection Navigation plays a significant role in autonomous vehicle operation. This paper focuses on enhancing autonomous vehicle intersection navigation through advanced computer vision and Vehicle-to-Infrastructure (V2I) communication systems. The research unfolds in two phases. In the first phase, an approach utilizing YOLOv8s is proposed for precise traffic light detection and recognition, trained on the Small-Scale Traffic Light Dataset (S2TLD). The second phase establishes seamless connectivity between autonomous vehicles and traffic lights in a simulated Mississippi State University Autonomous Vehicle Simulation (MAVS) environment resembling a small city with multiple intersections. This V2I system enables the transmission of Signal Phase and Timing (SPaT) messages to vehicles, providing information on current traffic light phases and time until the next phase change which enables the vehicles to adjust their speed and behavior in real-time. The simulation demonstrates accurate traffic light detection, with vehicles receiving SPaT messages, showcasing the systemโ€™s effectiveness in a multi-intersection scenario

    Scene Informer: Anchor-based Occlusion Inference and Trajectory Prediction in Partially Observable Environments

    Full text link
    Navigating complex and dynamic environments requires autonomous vehicles (AVs) to reason about both visible and occluded regions. This involves predicting the future motion of observed agents, inferring occluded ones, and modeling their interactions based on vectorized scene representations of the partially observable environment. However, prior work on occlusion inference and trajectory prediction have developed in isolation, with the former based on simplified rasterized methods and the latter assuming full environment observability. We introduce the Scene Informer, a unified approach for predicting both observed agent trajectories and inferring occlusions in a partially observable setting. It uses a transformer to aggregate various input modalities and facilitate selective queries on occlusions that might intersect with the AV's planned path. The framework estimates occupancy probabilities and likely trajectories for occlusions, as well as forecast motion for observed agents. We explore common observability assumptions in both domains and their performance impact. Our approach outperforms existing methods in both occupancy prediction and trajectory prediction in partially observable setting on the Waymo Open Motion Dataset

    Parameterized Decision-making with Multi-modal Perception for Autonomous Driving

    Full text link
    Autonomous driving is an emerging technology that has advanced rapidly over the last decade. Modern transportation is expected to benefit greatly from a wise decision-making framework of autonomous vehicles, including the improvement of mobility and the minimization of risks and travel time. However, existing methods either ignore the complexity of environments only fitting straight roads, or ignore the impact on surrounding vehicles during optimization phases, leading to weak environmental adaptability and incomplete optimization objectives. To address these limitations, we propose a parameterized decision-making framework with multi-modal perception based on deep reinforcement learning, called AUTO. We conduct a comprehensive perception to capture the state features of various traffic participants around the autonomous vehicle, based on which we design a graph-based model to learn a state representation of the multi-modal semantic features. To distinguish between lane-following and lane-changing, we decompose an action of the autonomous vehicle into a parameterized action structure that first decides whether to change lanes and then computes an exact action to execute. A hybrid reward function takes into account aspects of safety, traffic efficiency, passenger comfort, and impact to guide the framework to generate optimal actions. In addition, we design a regularization term and a multi-worker paradigm to enhance the training. Extensive experiments offer evidence that AUTO can advance state-of-the-art in terms of both macroscopic and microscopic effectiveness.Comment: IEEE International Conference on Data Engineering (ICDE2024

    Towards Learning Feasible Hierarchical Decision-Making Policies in Urban Autonomous Driving

    Get PDF
    Modern learning-based algorithms, powered by advanced deep structured neural nets, have multifacetedly facilitated automated driving platforms, spanning from scene characterization and perception to low-level control and state estimation schemes. Nonetheless, urban autonomous driving is regarded as a challenging application for machine learning (ML) and artificial intelligence (AI) since the learnt driving policies must handle complex multi-agent driving scenarios with indeterministic intentions of road participants. In the case of unsignalized intersections, automating the decision-making process at these safety-critical environments entails comprehending numerous layers of abstractions associated with learning robust driving behaviors to allow the vehicle to drive safely and efficiently. Based on our in-depth investigation, we discern that an efficient, yet safe, decision-making scheme for navigating real-world unsignalized intersections does not exist yet. The state-of-the-art schemes lacked practicality to handle real-life complex scenarios as they utilize Low-fidelity vehicle dynamic models which makes them incapable of simulating the real dynamic motion in real-life driving applications. In addition, the conservative behavior of autonomous vehicles, which often overreact to threats which have low likelihood, degrades the overall driving quality and jeopardizes safety. Hence, enhancing driving behavior is essential to attain agile, yet safe, traversing maneuvers in such multi-agent environments. Therefore, the main goal of conducting this PhD research is to develop high-fidelity learning-based frameworks to enhance the autonomous decision-making process at these safety-critical environments. We focus this PhD dissertation on three correlated and complementary research challenges. In our first research challenge, we conduct an in-depth and comprehensive survey on the state-of-the-art learning-based decision-making schemes with the objective of identifying the main shortcomings and potential research avenues. Based on the research directions concluded, we propose, in Problem II and Problem III, novel learning-based frameworks with the objective of enhancing safety and efficiency at different decision-making levels. In Problem II, we develop a novel sensor-independent state estimation for a safety-critical system in urban driving using deep learning techniques. A neural inference model is developed and trained via deep-learning training techniques to obtain accurate state estimates using indirect measurements of vehicle dynamic states and powertrain states. In Problem III, we propose a novel hierarchical reinforcement learning-based decision-making architecture for learning left-turn policies at four-way unsignalized intersections with feasibility guarantees. The proposed technique involves an integration of two main decision-making layers; a high-level learning-based behavioral planning layer which adopts soft actor-critic principles to learn high-level, non-conservative yet safe, driving behaviors, and a motion planning layer that uses low-level Model Predictive Control (MPC) principles to ensure feasibility of the two-dimensional left-turn maneuver. The high-level layer generates reference signals of velocity and yaw angle for the ego vehicle taking into account safety and collision avoidance with the intersection vehicles, whereas the low-level planning layer solves an optimization problem to track these reference commands considering several vehicle dynamic constraints and ride comfort

    ๋„์‹ฌ ๊ต์ฐจ๋กœ์—์„œ์˜ ์ž์œจ์ฃผํ–‰์„ ์œ„ํ•œ ์ฃผ๋ณ€ ์ฐจ๋Ÿ‰ ๊ฒฝ๋กœ ์˜ˆ์ธก ๋ฐ ๊ฑฐ๋™ ๊ณ„ํš ์•Œ๊ณ ๋ฆฌ์ฆ˜

    Get PDF
    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :๊ณต๊ณผ๋Œ€ํ•™ ๊ธฐ๊ณ„ํ•ญ๊ณต๊ณตํ•™๋ถ€,2020. 2. ์ด๊ฒฝ์ˆ˜.์ฐจ๋ž‘์šฉ ์„ผ์‹ฑ ๋ฐ ์ฒ˜๋ฆฌ๊ธฐ์ˆ ์ด ๋ฐœ๋‹ฌํ•จ์— ๋”ฐ๋ผ ์ž๋™์ฐจ ๊ธฐ์ˆ  ์—ฐ๊ตฌ๊ฐ€ ์ˆ˜๋™ ์•ˆ์ „ ๊ธฐ์ˆ ์—์„œ ๋Šฅ๋™ ์•ˆ์ „ ๊ธฐ์ˆ ๋กœ ์ดˆ์ ์ด ํ™•์žฅ๋˜๊ณ  ์žˆ๋‹ค. ์ตœ๊ทผ, ์ฃผ์š” ์ž๋™์ฐจ ์ œ์ž‘์‚ฌ๋“ค์€ ๋Šฅ๋™ํ˜• ์ฐจ๊ฐ„๊ฑฐ๋ฆฌ ์ œ์–ด, ์ฐจ์„  ์œ ์ง€ ๋ณด์กฐ, ๊ทธ๋ฆฌ๊ณ  ๊ธด๊ธ‰ ์ž๋™ ์ œ๋™๊ณผ ๊ฐ™์€ ๋Šฅ๋™ ์•ˆ์ „ ๊ธฐ์ˆ ์ด ์ด๋ฏธ ์ƒ์—…ํ™”ํ•˜๊ณ  ์žˆ๋‹ค. ์ด๋Ÿฌํ•œ ๊ธฐ์ˆ ์  ์ง„๋ณด๋Š” ์‚ฌ์ƒ๋ฅ  ์ œ๋กœ๋ฅผ ๋‹ฌ์„ฑํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ๊ธฐ์ˆ  ์—ฐ๊ตฌ ๋ถ„์•ผ๋ฅผ ๋Šฅ๋™ ์•ˆ์ „ ๊ธฐ์ˆ ์„ ๋„˜์–ด์„œ ์ž์œจ์ฃผํ–‰ ์‹œ์Šคํ…œ์œผ๋กœ ํ™•์žฅ์‹œํ‚ค๊ณ  ์žˆ๋‹ค. ํŠนํžˆ, ๋„์‹ฌ ๋„๋กœ๋Š” ์ธ๋„, ์‚ฌ๊ฐ์ง€๋Œ€, ์ฃผ์ฐจ์ฐจ๋Ÿ‰, ์ด๋ฅœ์ฐจ, ๋ณดํ–‰์ž ๋“ฑ๊ณผ ๊ฐ™์€ ๊ตํ†ต ์œ„ํ—˜ ์š”์†Œ๋ฅผ ๋งŽ์ด ๊ฐ–๊ณ  ์žˆ๊ธฐ ๋•Œ๋ฌธ์— ๊ณ ์†๋„๋กœ๋ณด๋‹ค ์‚ฌ๊ณ  ๋ฐœ์ƒ๋ฅ ๊ณผ ์‚ฌ์ƒ๋ฅ ์ด ๋†’์œผ๋ฉฐ, ์ด๋Š” ๋„์‹ฌ ๋„๋กœ์—์„œ์˜ ์ž์œจ์ฃผํ–‰์€ ํ•ต์‹ฌ ์ด์Šˆ๊ฐ€ ๋˜๊ณ  ์žˆ๋‹ค. ๋งŽ์€ ํ”„๋กœ์ ํŠธ๋“ค์ด ์ž์œจ์ฃผํ–‰์˜ ํ™˜๊ฒฝ์ , ์ธ๊ตฌํ•™์ , ์‚ฌํšŒ์ , ๊ทธ๋ฆฌ๊ณ  ๊ฒฝ์ œ์  ์ธก๋ฉด์—์„œ์˜ ์ž์œจ์ฃผํ–‰์˜ ํšจ๊ณผ๋ฅผ ํ‰๊ฐ€ํ•˜๊ธฐ ์œ„ํ•ด ์ˆ˜ํ–‰๋˜์—ˆ๊ฑฐ๋‚˜ ์ˆ˜ํ–‰ ์ค‘์— ์žˆ๋‹ค. ์˜ˆ๋ฅผ ๋“ค์–ด, ์œ ๋Ÿฝ์˜ AdaptIVE๋Š” ๋‹ค์–‘ํ•œ ์ž์œจ์ฃผํ–‰ ๊ธฐ๋Šฅ์„ ๊ฐœ๋ฐœํ•˜์˜€์œผ๋ฉฐ, ๊ตฌ์ฒด์ ์ธ ํ‰๊ฐ€ ๋ฐฉ๋ฒ•๋ก ์„ ๊ฐœ๋ฐœํ•˜์˜€๋‹ค. ๋˜ํ•œ, CityMobil2๋Š” ์œ ๋Ÿฝ ์ „์—ญ์˜ 9๊ฐœ์˜ ๋‹ค๋ฅธ ํ™˜๊ฒฝ์—์„œ ๋ฌด์ธ ์ง€๋Šฅํ˜• ์ฐจ๋Ÿ‰์„ ์„ฑ๊ณต์ ์œผ๋กœ ํ†ตํ•ฉํ•˜์˜€๋‹ค. ์ผ๋ณธ์—์„œ๋Š” 2014๋…„ 5์›”์— ์‹œ์ž‘๋œ Automated Driving System Research Project๋Š” ์ž์œจ์ฃผํ–‰ ์‹œ์Šคํ…œ๊ณผ ์ฐจ์„ธ๋Œ€ ๋„์‹ฌ ๊ตํ†ต ์ˆ˜๋‹จ์˜ ๊ฐœ๋ฐœ ๋ฐ ๊ฒ€์ฆ์— ์ดˆ์ ์„ ๋งž์ถ”์—ˆ๋‹ค. ๊ธฐ์กด ์—ฐ๊ตฌ๋“ค์— ๋Œ€ํ•œ ์กฐ์‚ฌ๋ฅผ ํ†ตํ•ด ์ž์œจ์ฃผํ–‰ ์‹œ์Šคํ…œ์€ ๊ตํ†ต ์ฐธ์—ฌ์ž๋“ค์˜ ์•ˆ์ „๋„๋ฅผ ํ–ฅ์ƒ์‹œํ‚ค๊ณ , ๊ตํ†ต ํ˜ผ์žก์„ ๊ฐ์†Œ์‹œํ‚ค๋ฉฐ, ์šด์ „์ž ํŽธ์˜์„ฑ์„ ์ฆ์ง„์‹œํ‚ค๋Š” ๊ฒƒ์ด ์ฆ๋ช…๋˜์—ˆ๋‹ค. ๋‹ค์–‘ํ•œ ๋ฐฉ๋ฒ•๋ก ๋“ค์ด ์ธ์ง€, ๊ฑฐ๋™ ๊ณ„ํš, ๊ทธ๋ฆฌ๊ณ  ์ œ์–ด์™€ ๊ฐ™์€ ๋„์‹ฌ ๋„๋กœ ์ž์œจ์ฃผํ–‰์ฐจ์˜ ํ•ต์‹ฌ ๊ธฐ์ˆ ๋“ค์„ ๊ฐœ๋ฐœํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ์‚ฌ์šฉ๋˜์—ˆ๋‹ค. ํ•˜์ง€๋งŒ ๋งŽ์€ ์ตœ์‹ ์˜ ์ž์œจ์ฃผํ–‰ ์—ฐ๊ตฌ๋“ค์€ ๊ฐ ๊ธฐ์ˆ ์˜ ๊ฐœ๋ฐœ์„ ๋ณ„๊ฐœ๋กœ ๊ณ ๋ คํ•˜์—ฌ ์ง„ํ–‰ํ•ด์™”๋‹ค. ๊ฒฐ๊ณผ์ ์œผ๋กœ ํ†ตํ•ฉ์ ์ธ ๊ด€์ ์—์„œ์˜ ์ž์œจ์ฃผํ–‰ ๊ธฐ์ˆ  ์„ค๊ณ„๋Š” ์•„์ง ์ถฉ๋ถ„ํžˆ ๊ณ ๋ ค๋˜์–ด ์•Š์•˜๋‹ค. ๋”ฐ๋ผ์„œ, ๋ณธ ๋…ผ๋ฌธ์€ ๋ณต์žกํ•œ ๋„์‹ฌ ๋„๋กœ ํ™˜๊ฒฝ์—์„œ ๋ผ์ด๋‹ค, ์นด๋ฉ”๋ผ, GPS, ๊ทธ๋ฆฌ๊ณ  ๊ฐ„๋‹จํ•œ ๊ฒฝ๋กœ ๋งต์— ๊ธฐ๋ฐ˜ํ•œ ์™„์ „ ์ž์œจ์ฃผํ–‰ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ๊ฐœ๋ฐœํ•˜๋Š” ๊ฒƒ์„ ๋ชฉํ‘œ๋กœ ํ•˜์˜€๋‹ค. ์ œ์•ˆ๋œ ์ž์œจ์ฃผํ–‰ ์•Œ๊ณ ๋ฆฌ์ฆ˜์€ ๋น„ํ†ต์ œ ๊ต์ฐจ๋กœ๋ฅผ ํฌํ•จํ•œ ๋„์‹ฌ ๋„๋กœ ์ƒํ™ฉ์„ ์ฐจ๋Ÿ‰ ๊ฑฐ๋™ ์˜ˆ์ธก๊ธฐ์™€ ๋ชจ๋ธ ์˜ˆ์ธก ์ œ์–ด ๊ธฐ๋ฒ•์— ๊ธฐ๋ฐ˜ํ•˜์—ฌ ์„ค๊ณ„๋˜์—ˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์€ ๋™์ , ์ •์  ํ™˜๊ฒฝ ํ‘œํ˜„ ๋ฐ ์ข…ํšก๋ฐฉํ–ฅ ๊ฑฐ๋™ ๊ณ„ํš์„ ์ค‘์ ์ ์œผ๋กœ ๋‹ค๋ฃจ์—ˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์€ ๋„์‹ฌ ๋„๋กœ ์ž์œจ์ฃผํ–‰์„ ์œ„ํ•œ ๊ฑฐ๋™ ๊ณ„ํš ์•Œ๊ณ ๋ฆฌ์ฆ˜์˜ ๊ฐœ์š”๋ฅผ ์ œ์‹œํ•˜์˜€์œผ๋ฉฐ, ์‹ค์ œ ๊ตํ†ต ์ƒํ™ฉ์—์„œ์˜ ์‹คํ—˜ ๊ฒฐ๊ณผ๋Š” ์ œ์•ˆ๋œ ์•Œ๊ณ ๋ฆฌ์ฆ˜์˜ ํšจ๊ณผ์„ฑ๊ณผ ์šด์ „์ž ๊ฑฐ๋™๊ณผ์˜ ์œ ์‚ฌ์„ฑ์„ ๋ณด์—ฌ์ฃผ์—ˆ๋‹ค. ์‹ค์ฐจ ์‹คํ—˜ ๊ฒฐ๊ณผ๋Š” ๋น„ํ†ต์ œ ๊ต์ฐจ๋กœ๋ฅผ ํฌํ•จํ•œ ๋„์‹ฌ ์‹œ๋‚˜๋ฆฌ์˜ค์—์„œ์˜ ๊ฐ•๊ฑดํ•œ ์„ฑ๋Šฅ์„ ๋ณด์—ฌ์ฃผ์—ˆ๋‹ค.The foci of automotive researches have been expanding from passive safety systems to active safety systems with advances in sensing and processing technologies. Recently, the majority of automotive makers have already commercialized active safety systems, such as adaptive cruise control (ACC), lane keeping assistance (LKA), and autonomous emergency braking (AEB). Such advances have extended the research field beyond active safety systems to automated driving systems to achieve zero fatalities. Especially, automated driving on urban roads has become a key issue because urban roads possess numerous risk factors for traffic accidents, such as sidewalks, blind spots, on-street parking, motorcycles, and pedestrians, which cause higher accident rates and fatalities than motorways. Several projects have been conducted, and many others are still underway to evaluate the effects of automated driving in environmental, demographic, social, and economic aspects. For example, the European project AdaptIVe, develops various automated driving functions and defines specific evaluation methodologies. In addition, CityMobil2 successfully integrates driverless intelligent vehicles in nine other environments throughout Europe. In Japan, the Automated Driving System Research Project began on May 2014, which focuses on the development and verification of automated driving systems and next-generation urban transportation. From a careful review of a considerable amount of literature, automated driving systems have been proven to increase the safety of traffic users, reduce traffic congestion, and improve driver convenience. Various methodologies have been employed to develop the core technology of automated vehicles on urban roads, such as perception, motion planning, and control. However, the current state-of-the-art automated driving algorithms focus on the development of each technology separately. Consequently, designing automated driving systems from an integrated perspective is not yet sufficiently considered. Therefore, this dissertation focused on developing a fully autonomous driving algorithm in urban complex scenarios using LiDAR, vision, GPS, and a simple path map. The proposed autonomous driving algorithm covered the urban road scenarios with uncontrolled intersections based on vehicle motion prediction and model predictive control approach. Mainly, four research issues are considered: dynamic/static environment representation, and longitudinal/lateral motion planning. In the remainder of this thesis, we will provide an overview of the proposed motion planning algorithm for urban autonomous driving and the experimental results in real traffic, which showed the effectiveness and human-like behaviors of the proposed algorithm. The proposed algorithm has been tested and evaluated using both simulation and vehicle tests. The test results show the robust performance of urban scenarios, including uncontrolled intersections.Chapter 1 Introduction 1 1.1. Background and Motivation 1 1.2. Previous Researches 4 1.3. Thesis Objectives 9 1.4. Thesis Outline 10 Chapter 2 Overview of Motion Planning for Automated Driving System 11 Chapter 3 Dynamic Environment Representation with Motion Prediction 15 3.1. Moving Object Classification 17 3.2. Vehicle State based Direct Motion Prediction 20 3.2.1. Data Collection Vehicle 22 3.2.2. Target Roads 23 3.2.3. Dataset Selection 24 3.2.4. Network Architecture 25 3.2.5. Input and Output Features 33 3.2.6. Encoder and Decoder 33 3.2.7. Sequence Length 34 3.3. Road Structure based Interactive Motion Prediction 36 3.3.1. Maneuver Definition 38 3.3.2. Network Architecture 39 3.3.3. Path Following Model based State Predictor 47 3.3.4. Estimation of predictor uncertainty 50 3.3.5. Motion Parameter Estimation 53 3.3.6. Interactive Maneuver Prediction 56 3.4. Intersection Approaching Vehicle Motion Prediction 59 3.4.1. Driver Behavior Model at Intersections 59 3.4.2. Intention Inference based State Prediction 63 Chapter 4 Static Environment Representation 67 4.1. Static Obstacle Map Construction 69 4.2. Free Space Boundary Decision 74 4.3. Drivable Corridor Decision 76 Chapter 5 Longitudinal Motion Planning 81 5.1. In-Lane Target Following 82 5.2. Proactive Motion Planning for Narrow Road Driving 85 5.2.1. Motivation for Collision Preventive Velocity Planning 85 5.2.2. Desired Acceleration Decision 86 5.3. Uncontrolled Intersection 90 5.3.1. Driving Phase and Mode Definition 91 5.3.2. State Machine for Driving Mode Decision 92 5.3.3. Motion Planner for Approach Mode 95 5.3.4. Motion Planner for Risk Management Phase 98 Chapter 6 Lateral Motion Planning 105 6.1. Vehicle Model 107 6.2. Cost Function and Constraints 109 Chapter 7 Performance Evaluation 115 7.1. Motion Prediction 115 7.1.1. Prediction Accuracy Analysis of Vehicle State based Direct Motion Predictor 115 7.1.2. Prediction Accuracy and Effect Analysis of Road Structure based Interactive Motion Predictor 122 7.2. Prediction based Distance Control at Urban Roads 132 7.2.1. Driving Data Analysis of Direct Motion Predictor Application at Urban Roads 133 7.2.2. Case Study of Vehicle Test at Urban Roads 138 7.2.3. Analysis of Vehicle Test Results on Urban Roads 147 7.3. Complex Urban Roads 153 7.3.1. Case Study of Vehicle Test at Complex Urban Roads 154 7.3.2. Closed-loop Simulation based Safety Analysis 162 7.4. Uncontrolled Intersections 164 7.4.1. Simulation based Algorithm Comparison of Motion Planner 164 7.4.2. Monte-Carlo Simulation based Safety Analysis 166 7.4.3. Vehicle Tests Results in Real Traffic Conditions 172 7.4.4. Similarity Analysis between Human and Automated Vehicle 194 7.5. Multi-Lane Turn Intersections 197 7.5.1. Case Study of a Multi-Lane Left Turn Scenario 197 7.5.2. Analysis of Motion Planning Application Results 203 Chapter 8 Conclusion & Future Works 207 8.1. Conclusion 207 8.2. Future Works 209 Bibliography 210 Abstract in Korean 219Docto
    • โ€ฆ
    corecore