도심 교차로에서의 자율주행을 위한 주변 차량 경로 예측 및 거동 계획 알고리즘

학위논문(박사)--서울대학교 대학원 :공과대학 기계항공공학부,2020. 2. 이경수.차랑용 센싱 및 처리기술이 발달함에 따라 자동차 기술 연구가 수동 안전 기술에서 능동 안전 기술로 초점이 확장되고 있다. 최근, 주요 자동차 제작사들은 능동형 차간거리 제어, 차선 유지 보조, 그리고 긴급 자동 제동과 같은 능동 안전 기술이 이미 상업화하고 있다. 이러한 기술적 진보는 사상률 제로를 달성하기 위하여 기술 연구 분야를 능동 안전 기술을 넘어서 자율주행 시스템으로 확장시키고 있다. 특히, 도심 도로는 인도, 사각지대, 주차차량, 이륜차, 보행자 등과 같은 교통 위험 요소를 많이 갖고 있기 때문에 고속도로보다 사고 발생률과 사상률이 높으며, 이는 도심 도로에서의 자율주행은 핵심 이슈가 되고 있다. 많은 프로젝트들이 자율주행의 환경적, 인구학적, 사회적, 그리고 경제적 측면에서의 자율주행의 효과를 평가하기 위해 수행되었거나 수행 중에 있다. 예를 들어, 유럽의 AdaptIVE는 다양한 자율주행 기능을 개발하였으며, 구체적인 평가 방법론을 개발하였다. 또한, CityMobil2는 유럽 전역의 9개의 다른 환경에서 무인 지능형 차량을 성공적으로 통합하였다. 일본에서는 2014년 5월에 시작된 Automated Driving System Research Project는 자율주행 시스템과 차세대 도심 교통 수단의 개발 및 검증에 초점을 맞추었다. 기존 연구들에 대한 조사를 통해 자율주행 시스템은 교통 참여자들의 안전도를 향상시키고, 교통 혼잡을 감소시키며, 운전자 편의성을 증진시키는 것이 증명되었다. 다양한 방법론들이 인지, 거동 계획, 그리고 제어와 같은 도심 도로 자율주행차의 핵심 기술들을 개발하기 위하여 사용되었다. 하지만 많은 최신의 자율주행 연구들은 각 기술의 개발을 별개로 고려하여 진행해왔다. 결과적으로 통합적인 관점에서의 자율주행 기술 설계는 아직 충분히 고려되어 않았다. 따라서, 본 논문은 복잡한 도심 도로 환경에서 라이다, 카메라, GPS, 그리고 간단한 경로 맵에 기반한 완전 자율주행 알고리즘을 개발하는 것을 목표로 하였다. 제안된 자율주행 알고리즘은 비통제 교차로를 포함한 도심 도로 상황을 차량 거동 예측기와 모델 예측 제어 기법에 기반하여 설계되었다. 본 논문은 동적, 정적 환경 표현 및 종횡방향 거동 계획을 중점적으로 다루었다. 본 논문은 도심 도로 자율주행을 위한 거동 계획 알고리즘의 개요를 제시하였으며, 실제 교통 상황에서의 실험 결과는 제안된 알고리즘의 효과성과 운전자 거동과의 유사성을 보여주었다. 실차 실험 결과는 비통제 교차로를 포함한 도심 시나리오에서의 강건한 성능을 보여주었다.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

Perception Intelligence Integrated Vehicle-to-Vehicle Optical Camera Communication.

Ubiquitous usage of cameras and LEDs in modern road and aerial vehicles open up endless opportunities for novel applications in intelligent machine navigation, communication, and networking. To this end, in this thesis work, we hypothesize the benefit of dual-mode usage of vehicular built-in cameras through novel machine perception capabilities combined with optical camera communication (OCC). Current key conception of understanding a line-of-sight (LOS) scenery is from the aspect of object, event, and road situation detection. However, the idea of blending the non-line-of-sight (NLOS) information with the LOS information to achieve a see-through vision virtually is new. This improves the assistive driving performance by enabling a machine to see beyond occlusion. Another aspect of OCC in the vehicular setup is to understand the nature of mobility and its impact on the optical communication channel quality. The research questions gathered from both the car-car mobility modelling, and evaluating a working setup of OCC communication channel can also be inherited to aerial vehicular situations like drone-drone OCC. The aim of this thesis is to answer the research questions along these new application domains, particularly, (i) how to enable a virtual see-through perception in the car assisting system that alerts the human driver about the visible and invisible critical driving events to help drive more safely, (ii) how transmitter-receiver cars behaves while in the mobility and the overall channel performance of OCC in motion modality, (iii) how to help rescue lost Unmanned Aerial Vehicles (UAVs) through coordinated localization with fusion of OCC and WiFi, (iv) how to model and simulate an in-field drone swarm operation experience to design and validate UAV coordinated localization for group of positioning distressed drones. In this regard, in this thesis, we present the end-to-end system design, proposed novel algorithms to solve the challenges in applying such a system, and evaluation results through experimentation and/or simulation

Seamless Interactions Between Humans and Mobility Systems

As mobility systems, including vehicles and roadside infrastructure, enter a period of rapid and profound change, it is important to enhance interactions between people and mobility systems. Seamless human—mobility system interactions can promote widespread deployment of engaging applications, which are crucial for driving safety and efficiency. The ever-increasing penetration rate of ubiquitous computing devices, such as smartphones and wearable devices, can facilitate realization of this goal. Although researchers and developers have attempted to adapt ubiquitous sensors for mobility applications (e.g., navigation apps), these solutions often suffer from limited usability and can be risk-prone. The root causes of these limitations include the low sensing modality and limited computational power available in ubiquitous computing devices. We address these challenges by developing and demonstrating that novel sensing techniques and machine learning can be applied to extract essential, safety-critical information from drivers natural driving behavior, even actions as subtle as steering maneuvers (e.g., left-/righthand turns and lane changes). We first show how ubiquitous sensors can be used to detect steering maneuvers regardless of disturbances to sensing devices. Next, by focusing on turning maneuvers, we characterize drivers driving patterns using a quantifiable metric. Then, we demonstrate how microscopic analyses of crowdsourced ubiquitous sensory data can be used to infer critical macroscopic contextual information, such as risks present at road intersections. Finally, we use ubiquitous sensors to profile a driver’s behavioral patterns on a large scale; such sensors are found to be essential to the analysis and improvement of drivers driving behavior.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163127/1/chendy_1.pd

ASSESSING THE IMPACT OF BICYCLE TREATMENTS ON BICYCLE SAFETY: A MULTI-METHODS APPROACH

Compared to other modes, bicyclists are disproportionally affected by crashes considering their low mode share. There is evidence that crashes between bicyclists and motorized vehicle take place at road segments and signalized intersections where bicycle treatments (e.g., bike lanes) are present, urging for in-dept analysis of the safety impact of the various bicycle treatment types. Additionally, it is important to identify sensor types that have the potential to advance field data collection and traffic monitoring in multi-modal road environments. In this dissertation, three approaches, namely crash analysis, traffic conflict analysis, and analysis of driver speeding and glancing behavior, were implemented to investigate the safety impact of bicycle treatments at the segment- and the intersection-levels on bicycle safety. Prediction models were developed to predict bicycle-motorized vehicle crashes at road segments and signalized intersections, and traffic conflicts between straight-going bicyclists and right-turning vehicles at signalized intersections. Driver speeding and glancing behavior was analysed for the segment and the intersection levels. A mode classification framework to classify trajectories recorded using a radar-based sensor was developed to test the feasibility of using radar-based sensors in field studies. The findings of this dissertation contribute to bicycle safety research in terms of quantifying the safety impact of various bicycle treatment types and how to assess and also, by showing how to assess bicycle safety. The findings of this research have the potential to stand as a valuable tool for transportation policymakers and officials in charge of establishing safe bicycle networks