학위논문 (박사)-- 서울대학교 대학원 : 기계항공공학부, 2016. 2. 이경수. ; Recently, the interest of automotive researches changes from the passive safety system to the active safety system and, by extension, automated driving system due to advances in sensing technologies. For example, active safety applications, such as vehicle stability control (VSC), adaptive cruise control (ACC), lane keeping assistance (LKA) and lane change assistance (LCA) system), automated parking assist system (APA) and blind spot intervention (BSI), already have been commercialized by major automakers Furthermore, there are various ongoing projects which are trying to achieve the zero fatality. Several research teams around the world are continuously advancing the field of autonomous driving. And some of major automakers have been researching to integrate individual active safety system for the enhancement of safety. GM is trying to develop and introduce ‘Super Cruise’ system which can drive on the highway without human driver’s intervention. Toyota has undertaken researches to develop ‘Automatic Highway Driving Assist’ technology. The BMW managed to drive 100% automated in real traffic on the freeway from Munich to Ingolstadt, showing a robust, comfortable, and safe driving behavior, even during multiple automated LC maneuvers and the Mercedes Benz developed ‘Intelligent Drive’ system and followed the route from Mannheim to Pforzheim, Germany, in fully autonomous manner From a careful review of considerable amount of literature, automated driving technology has the potential to reduce the environmental impact of driving, reduce traffic jams, and increase the safety of motor vehicle travel. However, the current state-of-the-art in automated vehicle technology requires precise, expensive sensors such as differential global positioning systems, and highly accurate inertial navigation systems and scanning laser rangefinders. While the cost of these sensors is going down, integrating them into cars will increase the price and represent yet another barrier to adoption. Therefore, this dissertation focused on developing a fully automated driving algorithm which is capable of automated driving in complex scenarios while a chosen sensor configuration is closer to current automotive serial production in terms of cost and technical maturity than in many autonomous vehicles presented earlier. Mainly three research issues are considered: an environment representation, a motion planning, and a vehicle control. In the remainder of this paper, we will provide an overview of the overall architecture of the proposed automated driving control algorithm and the experimental results which shown the effectiveness of the proposed automated driving algorithm. The effectiveness of the proposed automated driving algorithm is evaluated via vehicle tests. Test results show the robust performance on an inner-city street scenario. ; Chapter 1 Introduction 1 1.1. Background and Motivation 1 1.2. Previous Researches 4 1.3. Thesis Objectives 7 1.4. Thesis Outline 8 Chapter 2 Overview of an Automated Driving System 9 Chapter 3 Environment Representation 12 3.1. Driving Corridor Decision 14 3.2. Static Obstacle Map Construction 19 Chapter 4 Moving Object Tracking and Estimation 21 4.1. Problem Formulation 22 4.1.1. Stochastic hybrid system 22 4.1.2. Coordinate Systems 24 4.1.3. Standard Process Model 25 4.1.4. Standard Measurement Model 28 4.2. Selection of Multiple Model Set and Parameter Design 31 4.2.1. Set of Multiple Process Model 31 4.2.2. Set of Multiple Measurement Model 33 4.2.3. Event Dependent Transition Probability Matrix 35 4.3. IMM/EKF Multi Target State estimation 40 4.3.1. Host Vehicle Filter 41 4.3.2. IMM/EKF based Filtering 42 4.3.3. Track Management 45 4.4. Vehicle Tests based Performance Evaluation 47 4.4.1. Configuration of Vehicle Tests 47 4.4.2. Implementation and Evaluation 49 4.4.3. Comparison with Model-switching/EKF 54 4.4.4. Experimental Results with Multi-target Situation 57 Chapter 5 . Safety Driving Envelope Decision and Motion Optimization 63 5.1. Multi-traffic Prediction 64 5.1.1. Lane Keeping Behavior Model 66 5.1.2. Vehicle Predictor 68 5.1.3. Test Data based Implementation and Performance Evaluation 72 5.2. Safety Driving Envelope Decision 83 5.3. Model Predictive Control based Motion Planning 86 Chapter 6 Vehicle Tests based Performance Evaluation 90 6.1. Test-Data based Simulation 91 6.2. Vehicle Tests: Automated Driving on Urban Roads 98 Chapter 7 Conclusions 106 Bibliography 108 Abstract in Korean 114 ; Doctor
Automated Driving System with Guaranteed Safety based on Generic Environment Representation and Model Predictive Control
2016-01-01
Theses
Electronic Resource
English
DDC: | 629 |
European Patent Office | 2024
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