11 research outputs found
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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
Dynamic Switching State Systems for Visual Tracking
This work addresses the problem of how to capture the dynamics of maneuvering objects for visual tracking. Towards this end, the perspective of recursive Bayesian filters and the perspective of deep learning approaches for state estimation are considered and their functional viewpoints are brought together
Muscle activation patterns in shoulder impingement patients
Introduction: Shoulder impingement is one of the most common presentations of shoulder joint problems 1. It appears to be caused by a reduction in the sub-acromial space as the humerus abducts between 60o -120o β the 'painful arc'. Structures between the humeral head and the acromion are thus pinched causing pain and further pathology 2. Shoulder muscle activity can influence this joint space but it is unclear whether this is a cause or effect in impingement patients. This study aimed to observe muscle activation patterns in normal and impingement shoulder patients and determine if there were any significant differences.
Method: 19 adult subjects were asked to perform shoulder abduction in their symptomatic arm and non-symptomatic. 10 of these subjects (age 47.9 Β± 11.2) were screened for shoulder impingement, and 9 subjects (age 38.9 Β± 14.3) had no history of shoulder pathology. Surface EMG was used to collect data for 6 shoulder muscles (Upper, middle and lower trapezius, serratus anterior, infraspinatus, middle deltoids) which was then filtered and fully rectified. Subjects performed 3 smooth unilateral abduction movements at a cadence of 16 beats of a metronome set at 60bpm, and the mean of their results was recorded. T-tests were used to indicate any statistical significance in the data sets. Significance was set at P<0.05.
Results: There was a significant difference in muscle activation with serratus anterior in particular showing a very low level of activation throughout the range when compared to normal shoulder activation patterns (<30%). Middle deltoid recruitment was significantly reduced between 60-90o in the impingement group (30:58%).Trends were noted in other muscles with upper trapezius and infraspinatus activating more rapidly and erratically (63:25%; 60:27% respectively), and lower trapezius with less recruitment (13:30%) in the patient group, although these did not quite reach significance.
Conclusion: There appears to be some interesting alterations in muscle recruitment patterns in impingement shoulder patients when compared against their own unaffected shoulders and the control group. In particular changes in scapula control (serratus anterior and trapezius) and lateral rotation (infraspinatus), which have direct influence on the sub-acromial space, should be noted. It is still not clear whether these alterations are causative or reactionary, but this finding gives a clear indication to the importance of addressing muscle reeducation as part of a rehabilitation programme in shoulder impingement patients
Reports to the President
A compilation of annual reports including a report from the President of the Massachusetts Institute of Technology, as well as reports from the academic and administrative units of the Institute. The reports outline the year's goals, accomplishments, honors and awards, and future plans