2 research outputs found
λμ¬ κ΅μ°¨λ‘μμμ μμ¨μ£Όνμ μν μ£Όλ³ μ°¨λ κ²½λ‘ μμΈ‘ λ° κ±°λ κ³ν μκ³ λ¦¬μ¦
νμλ
Όλ¬Έ(λ°μ¬)--μμΈλνκ΅ λνμ :곡과λν κΈ°κ³ν곡곡νλΆ,2020. 2. μ΄κ²½μ.μ°¨λμ© μΌμ± λ° μ²λ¦¬κΈ°μ μ΄ λ°λ¬ν¨μ λ°λΌ μλμ°¨ κΈ°μ μ°κ΅¬κ° μλ μμ κΈ°μ μμ λ₯λ μμ κΈ°μ λ‘ μ΄μ μ΄ νμ₯λκ³ μλ€. μ΅κ·Ό, μ£Όμ μλμ°¨ μ μμ¬λ€μ λ₯λν μ°¨κ°κ±°λ¦¬ μ μ΄, μ°¨μ μ μ§ λ³΄μ‘°, κ·Έλ¦¬κ³ κΈ΄κΈ μλ μ λκ³Ό κ°μ λ₯λ μμ κΈ°μ μ΄ μ΄λ―Έ μμ
ννκ³ μλ€. μ΄λ¬ν κΈ°μ μ μ§λ³΄λ μ¬μλ₯ μ λ‘λ₯Ό λ¬μ±νκΈ° μνμ¬ κΈ°μ μ°κ΅¬ λΆμΌλ₯Ό λ₯λ μμ κΈ°μ μ λμ΄μ μμ¨μ£Όν μμ€ν
μΌλ‘ νμ₯μν€κ³ μλ€. νΉν, λμ¬ λλ‘λ μΈλ, μ¬κ°μ§λ, μ£Όμ°¨μ°¨λ, μ΄λ₯μ°¨, 보νμ λ±κ³Ό κ°μ κ΅ν΅ μν μμλ₯Ό λ§μ΄ κ°κ³ μκΈ° λλ¬Έμ κ³ μλλ‘λ³΄λ€ μ¬κ³ λ°μλ₯ κ³Ό μ¬μλ₯ μ΄ λμΌλ©°, μ΄λ λμ¬ λλ‘μμμ μμ¨μ£Όνμ ν΅μ¬ μ΄μκ° λκ³ μλ€. λ§μ νλ‘μ νΈλ€μ΄ μμ¨μ£Όνμ νκ²½μ , μΈκ΅¬νμ , μ¬νμ , κ·Έλ¦¬κ³ κ²½μ μ μΈ‘λ©΄μμμ μμ¨μ£Όνμ ν¨κ³Όλ₯Ό νκ°νκΈ° μν΄ μνλμκ±°λ μν μ€μ μλ€. μλ₯Ό λ€μ΄, μ λ½μ AdaptIVEλ λ€μν μμ¨μ£Όν κΈ°λ₯μ κ°λ°νμμΌλ©°, ꡬ체μ μΈ νκ° λ°©λ²λ‘ μ κ°λ°νμλ€. λν, CityMobil2λ μ λ½ μ μμ 9κ°μ λ€λ₯Έ νκ²½μμ λ¬΄μΈ μ§λ₯ν μ°¨λμ μ±κ³΅μ μΌλ‘ ν΅ν©νμλ€. μΌλ³Έμμλ 2014λ
5μμ μμλ Automated Driving System Research Projectλ μμ¨μ£Όν μμ€ν
κ³Ό μ°¨μΈλ λμ¬ κ΅ν΅ μλ¨μ κ°λ° λ° κ²μ¦μ μ΄μ μ λ§μΆμλ€.
κΈ°μ‘΄ μ°κ΅¬λ€μ λν μ‘°μ¬λ₯Ό ν΅ν΄ μμ¨μ£Όν μμ€ν
μ κ΅ν΅ μ°Έμ¬μλ€μ μμ λλ₯Ό ν₯μμν€κ³ , κ΅ν΅ νΌμ‘μ κ°μμν€λ©°, μ΄μ μ νΈμμ±μ μ¦μ§μν€λ κ²μ΄ μ¦λͺ
λμλ€. λ€μν λ°©λ²λ‘ λ€μ΄ μΈμ§, κ±°λ κ³ν, κ·Έλ¦¬κ³ μ μ΄μ κ°μ λμ¬ λλ‘ μμ¨μ£Όνμ°¨μ ν΅μ¬ κΈ°μ λ€μ κ°λ°νκΈ° μνμ¬ μ¬μ©λμλ€. νμ§λ§ λ§μ μ΅μ μ μμ¨μ£Όν μ°κ΅¬λ€μ κ° κΈ°μ μ κ°λ°μ λ³κ°λ‘ κ³ λ €νμ¬ μ§νν΄μλ€. κ²°κ³Όμ μΌλ‘ ν΅ν©μ μΈ κ΄μ μμμ μμ¨μ£Όν κΈ°μ μ€κ³λ μμ§ μΆ©λΆν κ³ λ €λμ΄ μμλ€.
λ°λΌμ, λ³Έ λ
Όλ¬Έμ 볡μ‘ν λμ¬ λλ‘ νκ²½μμ λΌμ΄λ€, μΉ΄λ©λΌ, GPS, κ·Έλ¦¬κ³ κ°λ¨ν κ²½λ‘ λ§΅μ κΈ°λ°ν μμ μμ¨μ£Όν μκ³ λ¦¬μ¦μ κ°λ°νλ κ²μ λͺ©νλ‘ νμλ€. μ μλ μμ¨μ£Όν μκ³ λ¦¬μ¦μ λΉν΅μ κ΅μ°¨λ‘λ₯Ό ν¬ν¨ν λμ¬ λλ‘ μν©μ μ°¨λ κ±°λ μμΈ‘κΈ°μ λͺ¨λΈ μμΈ‘ μ μ΄ κΈ°λ²μ κΈ°λ°νμ¬ μ€κ³λμλ€. λ³Έ λ
Όλ¬Έμ λμ , μ μ νκ²½ νν λ° μ’
ν‘λ°©ν₯ κ±°λ κ³νμ μ€μ μ μΌλ‘ λ€λ£¨μλ€.
λ³Έ λ
Όλ¬Έμ λμ¬ λλ‘ μμ¨μ£Όνμ μν κ±°λ κ³ν μκ³ λ¦¬μ¦μ κ°μλ₯Ό μ μνμμΌλ©°, μ€μ κ΅ν΅ μν©μμμ μ€ν κ²°κ³Όλ μ μλ μκ³ λ¦¬μ¦μ ν¨κ³Όμ±κ³Ό μ΄μ μ κ±°λκ³Όμ μ μ¬μ±μ 보μ¬μ£Όμλ€. μ€μ°¨ μ€ν κ²°κ³Όλ λΉν΅μ κ΅μ°¨λ‘λ₯Ό ν¬ν¨ν λμ¬ μλ리μ€μμμ κ°κ±΄ν μ±λ₯μ 보μ¬μ£Όμλ€.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