준정형화된 환경에서 Look-ahead Point를 이용한 모방학습 기반 자율 내비게이션 방법

학위논문(박사) -- 서울대학교대학원 : 융합과학기술대학원 융합과학부(지능형융합시스템전공), 2023. 2. 박재흥.본 학위논문은 자율주행 차량이 주차장에서 위상지도와 비전 센서로 내비게이션을 수행하는 방법들을 제안합니다. 이 환경에서의 자율주행 기술은 완전 자율주행을 완성하는 데 필요하며, 편리하게 이용될 수 있습니다. 이 기술을 구현하기 위해, 경로를 생성하고 이를 현지화 데이터로 추종하는 방법이 일반적으로 연구되고 있습니다. 그러나, 주차장에서는 도로 간 간격이 좁고 장애물이 복잡하게 분포되어 있어 현지화 데이터를 정확하게 얻기 힘듭니다. 이는 실제 경로와 추종하는 경로 사이에 틀어짐을 발생시켜, 차량과 장애물 간 충돌 가능성을 높입니다. 따라서 현지화 데이터로 경로를 추종하는 대신, 낮은 비용을 가지는 비전 센서로 차량이 주행 가능 영역을 향해 주행하는 방법이 제안됩니다. 주차장에는 차선이 없고 다양한 정적/동적 장애물이 복잡하게 있어, 주행 가능/불가능한 영역을 구분하여 점유 격자 지도를 얻는 것이 필요합니다. 또한, 교차로를 내비게이션하기 위해, 전역 계획에 따른 하나의 갈래 도로만이 주행가능 영역으로 구분됩니다. 갈래 도로는 회전된 바운딩 박스 형태로 인식되며 주행가능 영역 인식과 함께 multi-task 네트워크를 통해 얻어집니다. 주행을 위해 모방학습이 사용되며, 이는 모델-기반 모션플래닝 방법보다 파라미터 튜닝 없이도 다양하고 복잡한 환경을 다룰 수 있고 부정확한 인식 결과에도 강인합니다. 아울러, 이미지에서 제어 명령을 구하는 기존 모방학습 방법과 달리, 점유 격자 지도에서 차량이 도달할 look-ahead point를 학습하는 새로운 모방학습 방법이 제안됩니다. 이 point를 사용함으로써, 모방 학습의 성능을 향상시키는 data aggregation (DAgger) 알고리즘을 별도의 조이스틱 없이 자율주행에 적용할 수 있으며, 전문가는 human-in-loop DAgger 훈련 과정에서도 최적의 행동을 잘 선택할 수 있습니다. 추가로, DAgger 변형 알고리즘들은 안전하지 않거나 충돌에 가까운 상황에 대한 데이터를 샘플링하여 DAgger 성능이 향상됩니다. 그러나, 전체 훈련 데이터셋에서 이 상황에 대한 데이터 비율이 적으면, 추가적인 DAgger 수행 및 사람의 노력이 요구됩니다. 이 문제를 다루기 위해, 가중 손실 함수를 사용하는 새로운 DAgger 훈련 방법인 WeightDAgger 알고리즘이 제안되며, 더 적은 DAgger 반복으로 앞서 언급 것과 유사한 상황에서 전문가의 행동을 더 정확하게 모방할 수 있습니다. DAgger를 동적 상황까지 확장하기 위해, 에이전트와 경쟁하는 적대적 정책이 제안되고, 이 정책을 DAgger 알고리즘에 적용하기 위한 훈련 프레임워크가 제안됩니다. 에이전트는 이전 DAgger 훈련 단계에서 훈련되지 않은 다양한 상황에 대해 훈련될 수 있을 뿐만 아니라 쉬운 상황에서 어려운 상황까지 점진적으로 훈련될 수 있습니다. 실내외 주차장에서의 차량 내비게이션 실험을 통해, 모델-기반 모션 플래닝 알고리즘의 한계 및 이를 다룰 수 있는 제안하는 모방학습 방법의 효용성이 분석됩니다. 또한, 시뮬레이션 실험을 통해, 제안된 WeightDAgger가 기존 DAgger 알고리즘들 보다 더 적은 DAgger 수행 및 사람의 노력이 필요함을 보이며, 적대적 정책을 이용한 DAgger 훈련 방법으로 동적 장애물을 안전하게 회피할 수 있음을 보입니다. 추가적으로, 부록에서는 비전 기반 자율 주차 시스템 및 주차 경로를 빠르게 생성할 수 있는 방법이 소개되어, 비전기반 주행 및 주차를 수행하는 자율 발렛 파킹 시스템이 완성됩니다.This thesis proposes methods for performing autonomous navigation with a topological map and a vision sensor in a parking lot. These methods are necessary to complete fully autonomous driving and can be conveniently used by humans. To implement them, a method of generating a path and tracking it with localization data is commonly studied. However, in such environments, the localization data is inaccurate because the distance between roads is narrow, and obstacles are distributed complexly, which increases the possibility of collisions between the vehicle and obstacles. Therefore, instead of tracking the path with the localization data, a method is proposed in which the vehicle drives toward a drivable area obtained by vision having a low-cost. In the parking lot, there are complicated various static/dynamic obstacles and no lanes, so it is necessary to obtain an occupancy grid map by segmenting the drivable/non-drivable areas. To navigating intersections, one branch road according to a global plan is configured as the drivable area. The branch road is detected in a shape of a rotated bounding box and is obtained through a multi-task network that simultaneously recognizes the drivable area. For driving, imitation learning is used, which can handle various and complex environments without parameter tuning and is more robust to handling an inaccurate perception result than model-based motion-planning algorithms. In addition, unlike existing imitation learning methods that obtain control commands from an image, a new imitation learning method is proposed that learns a look-ahead point that a vehicle will reach on an occupancy grid map. By using this point, the data aggregation (DAgger) algorithm that improves the performance of imitation learning can be applied to autonomous navigating without a separate joystick, and the expert can select the optimal action well even in the human-in-loop DAgger training process. Additionally, DAgger variant algorithms improve DAgger's performance by sampling data for unsafe or near-collision situations. However, if the data ratio for these situations in the entire training dataset is small, additional DAgger iteration and human effort are required. To deal with this problem, a new DAgger training method using a weighted loss function (WeightDAgger) is proposed, which can more accurately imitate the expert action in the aforementioned situations with fewer DAgger iterations. To extend DAgger to dynamic situations, an adversarial agent policy competing with the agent is proposed, and a training framework to apply this policy to DAgger is suggested. The agent can be trained for a variety of situations not trained in previous DAgger training steps, as well as progressively trained from easy to difficult situations. Through vehicle navigation experiments in real indoor and outdoor parking lots, limitations of the model-based motion-planning algorithms and the effectiveness of the proposed method to deal with them are analyzed. Besides, it is shown that the proposed WeightDAgger requires less DAgger performance and human effort than the existing DAgger algorithms, and the vehicle can safely avoid dynamic obstacles with the DAgger training framework using the adversarial agent policy. Additionally, the appendix introduces a vision-based autonomous parking system and a method to quickly generate the parking path, completing the vision-based autonomous valet parking system that performs driving as well as parking.1 INTRODUCTION 1 1.1 Autonomous Driving System and Environments 1 1.2 Motivation 4 1.3 Contributions of Thesis 6 1.4 Overview of Thesis 8 2 MULTI-TASK PERCEPTION NETWORK FOR VISION-BASED NAVIGATION 9 2.1 Introduction 9 2.1.1 Related Works 10 2.2 Proposed Method 13 2.2.1 Bird's-Eye-View Image Transform 14 2.2.2 Multi-Task Perception Network 15 2.2.2.1 Drivable Area Segmentation (Occupancy Grid Map (OGM)) 16 2.2.2.2 Rotated Road Bounding Box Detection 18 2.2.3 Intersection Decision 21 2.2.3.1 Road Occupancy Grid Map (OGMroad) 22 2.2.4 Merged Occupancy Grid Map (OGMmer) 23 2.3 Experiment 25 2.3.1 Experimental Setup 25 2.3.1.1 Autonomous Vehicle 25 2.3.1.2 Multi-task Network Setup 27 2.3.1.3 Model-based Branch Road Detection Method 29 2.3.2 Experimental Results 30 2.3.2.1 Quantitative Analysis of Multi-Task Network 30 2.3.2.2 Comparison of Branch Road Detection Method 31 2.4 Conclusion 34 3 DATA AGGREGATION (DAGGER) ALGORITHM WITH LOOK-AHEAD POINT FOR AUTONOMOUS DRIVING IN SEMI-STRUCTURED ENVIRONMENT 35 3.1 Introduction 35 3.2 Related Works & Background 41 3.2.1 DAgger Algorithms for Autonomous Driving 41 3.2.2 Behavior Cloning 42 3.2.3 DAgger Algorithm 43 3.3 Proposed Method 45 3.3.1 DAgger with Look-ahead Point Composition (State & Action) 45 3.3.2 Loss Function 49 3.3.3 Data-sampling Function in DAgger 50 3.3.4 Reasons to Use Look-ahead Point As Action 52 3.4 Experimental Setup 54 3.4.1 Driving Policy Network Training 54 3.4.2 Model-based Motion-Planning Algorithms 56 3.5 Experimental Result 57 3.5.1 Quantitative Analysis of Driving Policy 58 3.5.1.1 Collision Rate 58 3.5.1.2 Safe Distance Range Ratio 59 3.5.2 Qualitative Analysis of Driving Policy 60 3.5.2.1 Limitations of Tentacle Algorithm 60 3.5.2.2 Limitations of VVF Algorithm 61 3.5.2.3 Limitations of Both Tentacle and VVF 62 3.5.2.4 Driving Results on Noisy Occupancy Grid Map 63 3.5.2.5 Intersection Navigation 65 3.6 Conclusion 68 4 WEIGHT DAGGER ALGORITHM FOR REDUCING IMITATION LEARNING ITERATIONS 70 4.1 Introduction 70 4.2 Related Works & Background 71 4.3 Proposed Method 74 4.3.1 Weighted Loss Function in WeightDAgger 75 4.3.2 Weight Update Process in Entire Training Dataset 78 4.4 Experiments 80 4.4.1 Experimental Setup 80 4.4.2 Experimental Results 82 4.4.2.1 Ablation Study According to τ 82 4.4.2.2 Ablation Study According to ε 83 4.4.2.3 Ablation Study According to α 84 4.4.2.4 Driving Test Results 85 4.4.3 Walking Robot Experiments 86 4.5 Conclusion 87 5 DAGGER USING ADVERSARIAL AGENT POLICY FOR DYNAMIC SITUATIONS 89 5.1 Introduction 89 5.2 Related Works & Background 91 5.2.1 Motion-planning Algorithms for Dynamic Situations 91 5.2.2 DAgger Algorithm for Dynamic Situation 93 5.3 Proposed Method 95 5.3.1 DAgger Training Framework Using Adversarial Agent Policy 95 5.3.2 Applying to Oncoming Dynamic Obstacle Avoidance Task 97 5.3.2.1 Ego Agent Policy 98 5.3.2.2 Adversarial Agent Policy 100 5.4 Experiments 101 5.4.1 Experimental Setup 101 5.4.1.1 Ego Agent Policy Training 102 5.4.1.2 Adversarial Agent Policy Training 103 5.4.2 Experimental Result 103 5.4.2.1 Performance of Adversarial Agent Policy 103 5.4.2.2 Ego Agent Policy Performance Comparisons Trained with / without Adversarial Agent Policy 104 5.5 Conclusion 106 6 CONCLUSIONS 107 Appendix A 110 A.1 Vision-based Re-plannable Autonomous Parking System 110 A.1.1 Parking Spot Detection 112 A.1.2 Re-planning Method 113 A.2 Biased Target-tree* with RRT* Algorithm for Fast Parking Path Planning 115 A.2.1 Introduction 115 A.2.2 Proposed Method 117 A.2.3 Experiments 119 Abstract (In Korean) 143 Acknowledgement 145박

Deciphering the Legal Framework for Locally Addressing Issues Interwoven with Outward Expansion from America's Central Cities

American urbanism has come to be defined by migration from deteriorating urban development to new suburban development resulting in population decline within America's urban cores, or central cities. Population decline sets in motion certain self-reinforcing forces, or issues, likely to perpetuate it. These include the withdrawal of high- and middle-income households, a decline in the central city's tax base accompanied by rising local taxes and deteriorating public services, a dwindling consumer base to support utility infrastructure maintenance and improvement, and a rise in criminal activity. Federal, state and local governments have been involved in a variety of "urban renewal" strategies via studies, regulations, tax incentives and even investments of public funds, largely to no avail. During this time, what were once thought to be only urban issues have now also outwardly migrated to the suburbs. While some may assert that the birthplace of modern U.S. Supreme Court jurisprudence defining governmental authority to regulate land use is Euclid, the U.S. Supreme Court outlines in this same case that the true origin of this power is the power of sovereignty, the power to govern men and things within the limits of government's dominion, except in so far as it has been restricted by the Constitution of the United States. The Court explains that the nature and extent of these powers evolve as government is confronted with new issues requiring intervention. The evolution of government's regulatory powers and how these powers have been guided and constrained is defined by the application of Constitutional principles, statutes and ordinances. From Colonial times until the Civil War, state and local government regulation existed apart from U.S. Constitutional restraint. However, with the passage of the Fourteenth Amendment, the United States Supreme Court was charged to ensure state and local legislation complied with guaranteed rights under the U.S. Constitution. The Court in Mugler defined regulatory authority as the "police powers." Therein, state and local governments possess the authority to determine what measures are necessary to protect the public health, safety and welfare. The Court held that valid police power regulation does not violate individual liberty or property rights. Instead of defining this power's reach, the Court chose in this and subsequent case law only to retroactively invalidate regulation bearing no substantial relation to these powers. These powers were broadly interpreted and government operated with only the threat of regulatory invalidation until First English, where the Court determined government may have to compensate where regulation extends beyond these powers. The Court ruled in Penn. Central with recent confirmation in Ark. Game and Fish Comm'n that regulation effects a taking where it interferes with "distinct investment-backed expectations." Since there can be no investment-backed expectation in failure, government regulation designed to promote success should not run afoul of this constraint. Academically proffered philosophies and factor approaches involving residential and commercial developments can be objectively examined for co-relationship with developments identified as successful or challenged within the marketplace. A code based upon development philosophies and factor approaches objectively verified as associated with successful developments would therefore not be arbitrary and unreasonable as having no substantial relation to the general welfare. Such code provisions could be designed to be applicable to all similarly situated property and to produce the widespread public benefit of promoting development success and preventing the negative community-wide effects of development failure. Such a code should not be found to exceed government's regulatory police powers, for there can be no developer economic interest supported by "distinct investment-backed expectations" in development failure

Trajectory planning for automated driving in dynamic environments

Considering the last decades, the trend in the automotive industry to continuously increase the level of automation of vehicles is evident. A lot of research and development effort has been invested to improve upon driving safety and comfort in traffic. Nowadays, advanced driver assistance systems, and the development of automated driving functions in particular, represent one of the main areas of innovation in automotive engineering. In order to cope with challenges arising from complex dynamic environments the automated vehicle needs to perform comprehensive cognitive tasks that come along with the presence of other traffic participants and the necessity to adhere to prevailing traffic regulations. As a consequence, the automated driving task is decomposed into several sub problems. In the functional architecture of automated vehicles, motion planning that addresses the generation of a comfortable and safe trajectory is a key component that directly affects the overall driving performance. This thesis is about the development of a trajectory planning approach suitable to deal with dynamic environments. A two level hierarchical trajectory planning framework is proposed that unites the capability of optimality and spline interpolation and explicitly considers the aspect of contradicting planning objectives. The framework is designed to work in receding horizon fashion by performing cyclic replanning and hence accounts for the dynamic character of the environment. The hierarchization into two separate levels of optimization leads to an approach that covers basic driving functionality on low level, while required high level behavior is still prioritized. The presented framework relies on a spline-based trajectory representation with an underlying optimal interpolation strategy. The optimal trajectory with respect to a certain situation is found by joint optimization on high and low level. A continuous and a discrete trajectory optimization variant to generate an optimal trajectory with respect to high level objectives are presented that basically differ in the definition of possible solutions in terms of the optimal decision variables. Constraints like drivability incorporated by exploiting the flatness property of the applied vehicle model and accurate collision avoidance checking are considered explicitly to comply to essential requirements for automated driving. To evaluate the quality of the trajectory in terms of the associated driving behavior, several objectives are defined. For dedicated objectives a curvilinear frame is used, which enables a precise formulation of the desired vehicle behavior with respect to driving applications in structured environments. Hence, this measure permits to formulate objectives independent of road curvature, extending the scope of the applied trajectory planning approach to a wide range of scenarios. Evaluation works out the distinct characteristic features of the two presented high level optimization approaches, showing the achieved performance at the example of typical (highway) traffic scenarios. It is shown that both, the continuous as well as the discrete approach, are suitable to solve the trajectory generation problem supporting the idea of creating a generic trajectory planning framework for automated driving

Managing stormwater more sustainably using green infrastructure and low impact development

As cities continue to develop and densify, there is usually a notable increase in impermeable surface areas. With the introduction of more impermeable surfaces, significantly less rainwater is able to infiltrate back into the ground. When rainwater travels over impermeable surface areas, the runoff picks up toxic pollutants. This polluted water, hereafter referred to as “stormwater”, is generally conveyed into storm networks and eventually discharged into receiving outfall areas. When large volumes of polluted stormwater are discharged at high velocities, this can result in the pollution and erosion of receiving areas. As cities continue to grow, and with climate change on the rise, sustainably managing stormwater has become increasingly more important in today’s urban environment. Relying only on conventional stormwater management practices can be problematic, since today’s stormwater management solutions should be designed to respond to climate change, and the changing urban landscape. Using lesson-drawing and the voluntary transfer of information from the City of Philadelphia, this thesis suggests the use of green infrastructure, and low impact development in order manage rainwater as close to the source as possible. As a guiding principle, this thesis encourages planners, engineers, civil designers, and landowners to build natural processes back into the altered urban environment and use green infrastructure and low impact development whenever possible to manage stormwater more sustainably

Context Exploitation in Data Fusion

Complex and dynamic environments constitute a challenge for existing tracking algorithms. For this reason, modern solutions are trying to utilize any available information which could help to constrain, improve or explain the measurements. So called Context Information (CI) is understood as information that surrounds an element of interest, whose knowledge may help understanding the (estimated) situation and also in reacting to that situation. However, context discovery and exploitation are still largely unexplored research topics. Until now, the context has been extensively exploited as a parameter in system and measurement models which led to the development of numerous approaches for the linear or non-linear constrained estimation and target tracking. More specifically, the spatial or static context is the most common source of the ambient information, i.e. features, utilized for recursive enhancement of the state variables either in the prediction or the measurement update of the filters. In the case of multiple model estimators, context can not only be related to the state but also to a certain mode of the filter. Common practice for multiple model scenarios is to represent states and context as a joint distribution of Gaussian mixtures. These approaches are commonly referred as the join tracking and classification. Alternatively, the usefulness of context was also demonstrated in aiding the measurement data association. Process of formulating a hypothesis, which assigns a particular measurement to the track, is traditionally governed by the empirical knowledge of the noise characteristics of sensors and operating environment, i.e. probability of detection, false alarm, clutter noise, which can be further enhanced by conditioning on context. We believe that interactions between the environment and the object could be classified into actions, activities and intents, and formed into structured graphs with contextual links translated into arcs. By learning the environment model we will be able to make prediction on the target\u2019s future actions based on its past observation. Probability of target future action could be utilized in the fusion process to adjust tracker confidence on measurements. By incorporating contextual knowledge of the environment, in the form of a likelihood function, in the filter measurement update step, we have been able to reduce uncertainties of the tracking solution and improve the consistency of the track. The promising results demonstrate that the fusion of CI brings a significant performance improvement in comparison to the regular tracking approaches

Understanding the Legal Construct Regulating Government Intervention into City Decline and Degeneration in America

An overview of critical academic thought concerning the character and attributes of American urban development establishes that the presence of unsuccessful, or challenged, development is a transcending problem necessitating government regulation in response. Challenged developments were observed frequently materializing in areas exhibiting urban decline and degeneration, including outward migration. It was conjectured that this cycle of outward migration and urban decline and degeneration might be part of an overall development cycle experienced by more than current day cities. History was probed for evidence of commonality. Cycles of urban decline and degeneration appeared within Mesopotamia, Egypt, the Greek city-states, and the Roman Empire. The form of government, whether a benevolent priest-king, dictator, democratic assembly or republic council appears extraneous. The mere presence of governmental regulation, such as comprehensive planning, zoning, building codes, advanced development techniques or sophisticated legal concepts for the protection of individual rights, did not purport to dissuade or ameliorate these cycles throughout the ages. Historical accounts attributed successful urban concentration to the presence of safety and security, convenience, and quality of life. Conversely, when one or more of these factors were diminished or compromised, cycles of urban decline and degeneration seemed to emerge. Field research was conducted to ascertain how these historical observations fared in the modern context. Residential and commercial developments differentiated as successful and challenged within the fifty (50) fastest growing counties across the United States between 2000 and 2010 pursuant to the U.S. Census Bureau were surveyed to explore the presence of governmental regulation and procedures as well as factors affecting safety and security, convenience, and quality of life. Consistent with historical observations, only items connected with safety and security, convenience and quality of life emerged from this process. Based upon this knowledge, local governments may be prompted to intervene at the development stage of residential and commercial developments in an attempt to counter, forestall or at least lessen the impact of the cycle of outward migration and urban decline and degeneration. While this could be attempted ad hoc, a more prudent approach might be to re-examine and re-constitute existing zoning, subdivision and development regulations and procedures in light of the differential characteristics between successful verses challenged developments. However, such an undertaking does not happen in a legal "state of nature." A synthesis of the jurisprudence that defines the limits of and restraints upon current governmental regulation reveals that land use regulation in America centers around the interaction between the authority of a local government to act, pursuant to "police power" authority granted that local government from the state, and whether that government action violates an individual's Constitutional rights. These Constitutional rights center around the privileges and immunities of citizens, equal protections of the laws and due process clauses of the Fourteenth Amendment and include "regulatory takings" under the theory of inverse condemnation. The United States Supreme Court has undertaken the long and arduous task of defining this interaction. A summation of that current definition is contained in Arkansas Game and Fish Comm'n v. United States where the Court expounded that when regulation or temporary physical invasion by government interferes with private property, time is a factor in determining the existence of a compensable taking. Also relevant is the degree to which the invasion is intended or is the foreseeable result of authorized government action. So too, is the character of the land at issue and the owner's "reasonable investment-backed expectations" regarding the land's use. Severity of the interference figures in the calculus as well. While a single act may not be enough, a continuance of them in sufficient number and for a sufficient time may prove a taking. Every successive trespass adds to the force of the evidence. This current understanding of the interaction between the exercise of government regulation and takings jurisprudence lays the groundwork for thoughtful and legally permissible implementation and application of zoning, subdivision and developmental regulations and processes aimed at addressing the cycle of outward migration and urban decline and degeneration at the initial development stage as well as subsequently thereto