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Design and Evaluation of Lane Change Decision for Automated Driving Vehicle Using Stochastic Predictive Control : 자율 주행 차량을 위한 확률 예측 제어 기법 기반 차선 변경 판단 알고리즘 개발
DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | 이경수 | - |
dc.contributor.author | 서종상 | - |
dc.date.accessioned | 2017-07-13T06:27:11Z | - |
dc.date.available | 2017-07-13T06:27:11Z | - |
dc.date.issued | 2016-08 | - |
dc.identifier.other | 000000137270 | - |
dc.identifier.uri | https://hdl.handle.net/10371/118563 | - |
dc.description | 학위논문 (박사)-- 서울대학교 대학원 : 기계항공공학부, 2016. 8. 이경수. | - |
dc.description.abstract | The traffic accidents caused by human error, such as distraction, drowsiness, or mistakes, account for 94 percent of all traffic accidents over last decades. Since safe driving is a goal of road-traffic-vehicle environments, major automakers develop driver assistance and active safety system as key technologies since the early 1990s. For instance, parking assistance system (PAS), lane keeping assistance system (LKAS), smart cruise control (SCC), and automated emergency braking (AEB) already have been developed and commercialized by major automakers. Moreover, in recent years, an interest of automotive industry is changed from the development of active safety to that of automated driving system capable of sensing surrounding environments and driving itself. Google starts to operate an automated driving vehicle on real roads using a pre-measured precise map and environmental information from a laser scanner. The electric vehicle mass production company, Tesla, has applied the autopilot mode to Model S vehicle. BMW has succeeded driving a vehicle autonomously in real traffic from Munich to Ingolstadt in German with robustness, and safety. Mercedes-Benz developed Intelligent Drive system and followed the route with fully autonomous driving from Mannheim to Pforzheim in German. These trends of automated driving vehicle development promote not only safety of passengers but also convenience. However, the current state-of-the-art in automated driving technology demands sophisticated decision to manage various and complex traffic circumstances.
This dissertation focused on the development of an automated driving control algorithm which can determine appropriate vehicle motion to deal with complex situations such as merging roads in the highway. In order to enhance decision to drive a vehicle safely, the potential behaviors of surrounding vehicles, the current and predicted states with sensor uncertainties of surrounding vehicles should be considered. Based on the prediction of surrounding vehicles, the lane change or keeping mode is determined by risk monitoring. A safe driving envelope which indicates the safe drivable area is also defined in consideration of prediction states of other traffics. The subject vehicle plans lane change or keeping motion such as accelerating, overtaking, and braking. To obtain desired control inputs which have been planned in advance, the predictive control problem is formulated. However, for automated driving vehicles in dynamic road environments, the uncertainty has to be considered from states of vehicle dynamic model and actual sensing values. For this reason, Stochastic Model Predictive Control with adaptive uncertainty propagation is developed to improve performance. The performance of the proposed algorithm is validated via computer simulations and vehicle tests. Automated driving with the proposed algorithm shows smooth and safe driving behavior in various road traffic situations, such as lane keeping with preceding vehicle following, lane change in a multi-vehicle environment. The effectiveness of the proposed automated driving algorithm is evaluated via vehicle tests. Test results show the robust performance on a motorway scenario. | - |
dc.description.tableofcontents | Chapter 1 Introduction 1
1.1. Background and Motivation 1 1.2. Previous Researches 4 1.3. Thesis Objectives 8 1.4. Thesis Outline 9 Chapter 2 Overall Architecture of an Automated Driving System based on Stochastic Model Predictive Control 10 Chapter 3 Environment Representation 12 3.1. Probabilistic Prediction of Surrounding Vehicles Behavior 14 3.2. Collision Probability 20 Chapter 4 Vehicle Dynamics Model 24 Chapter 5 Motion Planning for Lane Change and Keeping Decision 29 5.1. Classification of Lane Change Situation 31 5.2. Lane Change and Keeping Mode Decision 34 5.2.1. Lane Change Timing and Direction 34 5.2.2. Lane Change Risk Monitoring 45 5.3. Dynamics Limit Decision 49 5.4. Safe Driving Envelope Definition 51 5.5. Target States Decision 59 Chapter 6 Stochastic Model Predictive Control based Vehicle Control 68 6.1. Disturbance Analysis 69 6.2. Stochastic MPC Problem Formulation 71 6.3. Closed-loop Paradigm Approach 75 6.4. Adaptive Uncertainty Propagation 81 Chapter 7 Evaluation 87 7.1. Computer Simulations 88 7.1.1. Comparison between Probabilistic and Deterministic Prediction 88 7.1.2. Constrained Scenario Simulation 93 7.2. ECU-in-the-loop evaluation using a vehicle traffic simulator 101 7.2.1. Configurations of a vehicle traffic simulator 101 7.2.2. Evaluation of real-time performance 103 7.2.3. Comparison between a human driver and automated driving controller 110 7.3. Vehicle Tests 116 Chapter 8 Conclusions and Future Works 123 Bibliography 125 국문 초록 132 | - |
dc.format | application/pdf | - |
dc.format.extent | 7630815 bytes | - |
dc.format.medium | application/pdf | - |
dc.language.iso | en | - |
dc.publisher | 서울대학교 대학원 | - |
dc.subject | Stochastic Model Predictive Control | - |
dc.subject | Lane Change | - |
dc.subject | Automated Driving | - |
dc.subject | Driving Mode Decision | - |
dc.subject.ddc | 621 | - |
dc.title | Design and Evaluation of Lane Change Decision for Automated Driving Vehicle Using Stochastic Predictive Control | - |
dc.title.alternative | 자율 주행 차량을 위한 확률 예측 제어 기법 기반 차선 변경 판단 알고리즘 개발 | - |
dc.type | Thesis | - |
dc.contributor.AlternativeAuthor | Suh, Jongsang | - |
dc.description.degree | Doctor | - |
dc.citation.pages | viii, 134 | - |
dc.contributor.affiliation | 공과대학 기계항공공학부 | - |
dc.date.awarded | 2016-08 | - |
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