145 research outputs found
Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey
Wireless sensor networks (WSNs) consist of autonomous and resource-limited
devices. The devices cooperate to monitor one or more physical phenomena within
an area of interest. WSNs operate as stochastic systems because of randomness
in the monitored environments. For long service time and low maintenance cost,
WSNs require adaptive and robust methods to address data exchange, topology
formulation, resource and power optimization, sensing coverage and object
detection, and security challenges. In these problems, sensor nodes are to make
optimized decisions from a set of accessible strategies to achieve design
goals. This survey reviews numerous applications of the Markov decision process
(MDP) framework, a powerful decision-making tool to develop adaptive algorithms
and protocols for WSNs. Furthermore, various solution methods are discussed and
compared to serve as a guide for using MDPs in WSNs
Traffic light detection and V2I communications of an autonomous vehicle with the traffic light for an effective intersection navigation using MAVS simulation
Intersection Navigation plays a significant role in autonomous vehicle operation. This paper focuses on enhancing autonomous vehicle intersection navigation through advanced computer vision and Vehicle-to-Infrastructure (V2I) communication systems. The research unfolds in two phases. In the first phase, an approach utilizing YOLOv8s is proposed for precise traffic light detection and recognition, trained on the Small-Scale Traffic Light Dataset (S2TLD). The second phase establishes seamless connectivity between autonomous vehicles and traffic lights in a simulated Mississippi State University Autonomous Vehicle Simulation (MAVS) environment resembling a small city with multiple intersections. This V2I system enables the transmission of Signal Phase and Timing (SPaT) messages to vehicles, providing information on current traffic light phases and time until the next phase change which enables the vehicles to adjust their speed and behavior in real-time. The simulation demonstrates accurate traffic light detection, with vehicles receiving SPaT messages, showcasing the systemโs effectiveness in a multi-intersection scenario
Scene Informer: Anchor-based Occlusion Inference and Trajectory Prediction in Partially Observable Environments
Navigating complex and dynamic environments requires autonomous vehicles
(AVs) to reason about both visible and occluded regions. This involves
predicting the future motion of observed agents, inferring occluded ones, and
modeling their interactions based on vectorized scene representations of the
partially observable environment. However, prior work on occlusion inference
and trajectory prediction have developed in isolation, with the former based on
simplified rasterized methods and the latter assuming full environment
observability. We introduce the Scene Informer, a unified approach for
predicting both observed agent trajectories and inferring occlusions in a
partially observable setting. It uses a transformer to aggregate various input
modalities and facilitate selective queries on occlusions that might intersect
with the AV's planned path. The framework estimates occupancy probabilities and
likely trajectories for occlusions, as well as forecast motion for observed
agents. We explore common observability assumptions in both domains and their
performance impact. Our approach outperforms existing methods in both occupancy
prediction and trajectory prediction in partially observable setting on the
Waymo Open Motion Dataset
Parameterized Decision-making with Multi-modal Perception for Autonomous Driving
Autonomous driving is an emerging technology that has advanced rapidly over
the last decade. Modern transportation is expected to benefit greatly from a
wise decision-making framework of autonomous vehicles, including the
improvement of mobility and the minimization of risks and travel time. However,
existing methods either ignore the complexity of environments only fitting
straight roads, or ignore the impact on surrounding vehicles during
optimization phases, leading to weak environmental adaptability and incomplete
optimization objectives. To address these limitations, we propose a
parameterized decision-making framework with multi-modal perception based on
deep reinforcement learning, called AUTO. We conduct a comprehensive perception
to capture the state features of various traffic participants around the
autonomous vehicle, based on which we design a graph-based model to learn a
state representation of the multi-modal semantic features. To distinguish
between lane-following and lane-changing, we decompose an action of the
autonomous vehicle into a parameterized action structure that first decides
whether to change lanes and then computes an exact action to execute. A hybrid
reward function takes into account aspects of safety, traffic efficiency,
passenger comfort, and impact to guide the framework to generate optimal
actions. In addition, we design a regularization term and a multi-worker
paradigm to enhance the training. Extensive experiments offer evidence that
AUTO can advance state-of-the-art in terms of both macroscopic and microscopic
effectiveness.Comment: IEEE International Conference on Data Engineering (ICDE2024
Towards Learning Feasible Hierarchical Decision-Making Policies in Urban Autonomous Driving
Modern learning-based algorithms, powered by advanced deep structured neural nets, have multifacetedly facilitated automated driving platforms, spanning from scene characterization and perception to low-level control and state estimation schemes. Nonetheless, urban autonomous driving is regarded as a challenging application for machine learning (ML) and artificial intelligence (AI) since the learnt driving policies must handle complex multi-agent driving scenarios with indeterministic intentions of road participants. In the case of unsignalized intersections, automating the decision-making process at these safety-critical environments entails comprehending numerous layers of abstractions associated with learning robust driving behaviors to allow the vehicle to drive safely and efficiently.
Based on our in-depth investigation, we discern that an efficient, yet safe, decision-making scheme for navigating real-world unsignalized intersections does not exist yet. The state-of-the-art schemes lacked practicality to handle real-life complex scenarios as they utilize Low-fidelity vehicle dynamic models which makes them incapable of simulating the real dynamic motion in real-life driving applications. In addition, the conservative behavior of autonomous vehicles, which often overreact to threats which have low likelihood, degrades the overall driving quality and jeopardizes safety. Hence, enhancing driving behavior is essential to attain agile, yet safe, traversing maneuvers in such multi-agent environments. Therefore, the main goal of conducting this PhD research is to develop high-fidelity learning-based frameworks to enhance the autonomous decision-making process at these safety-critical environments.
We focus this PhD dissertation on three correlated and complementary research challenges. In our first research challenge, we conduct an in-depth and comprehensive survey on the state-of-the-art learning-based decision-making schemes with the objective of identifying the main shortcomings and potential research avenues. Based on the research directions concluded, we propose, in Problem II and Problem III, novel learning-based frameworks with the objective of enhancing safety and efficiency at different decision-making levels. In Problem II, we develop a novel sensor-independent state estimation for a safety-critical system in urban driving using deep learning techniques. A neural inference model is developed and trained via deep-learning training techniques to obtain accurate state estimates using indirect measurements of vehicle dynamic states and powertrain states. In Problem III, we propose a novel hierarchical reinforcement learning-based decision-making architecture for learning left-turn policies at four-way unsignalized intersections with feasibility guarantees. The proposed technique involves an integration of two main decision-making layers; a high-level learning-based behavioral planning layer which adopts soft actor-critic principles to learn high-level, non-conservative yet safe, driving behaviors, and a motion planning layer that uses low-level Model Predictive Control (MPC) principles to ensure feasibility of the two-dimensional left-turn maneuver. The high-level layer generates reference signals of velocity and yaw angle for the ego vehicle taking into account safety and collision avoidance with the intersection vehicles, whereas the low-level planning layer solves an optimization problem to track these reference commands considering several vehicle dynamic constraints and ride comfort
๋์ฌ ๊ต์ฐจ๋ก์์์ ์์จ์ฃผํ์ ์ํ ์ฃผ๋ณ ์ฐจ๋ ๊ฒฝ๋ก ์์ธก ๋ฐ ๊ฑฐ๋ ๊ณํ ์๊ณ ๋ฆฌ์ฆ
ํ์๋
ผ๋ฌธ(๋ฐ์ฌ)--์์ธ๋ํ๊ต ๋ํ์ :๊ณต๊ณผ๋ํ ๊ธฐ๊ณํญ๊ณต๊ณตํ๋ถ,2020. 2. ์ด๊ฒฝ์.์ฐจ๋์ฉ ์ผ์ฑ ๋ฐ ์ฒ๋ฆฌ๊ธฐ์ ์ด ๋ฐ๋ฌํจ์ ๋ฐ๋ผ ์๋์ฐจ ๊ธฐ์ ์ฐ๊ตฌ๊ฐ ์๋ ์์ ๊ธฐ์ ์์ ๋ฅ๋ ์์ ๊ธฐ์ ๋ก ์ด์ ์ด ํ์ฅ๋๊ณ ์๋ค. ์ต๊ทผ, ์ฃผ์ ์๋์ฐจ ์ ์์ฌ๋ค์ ๋ฅ๋ํ ์ฐจ๊ฐ๊ฑฐ๋ฆฌ ์ ์ด, ์ฐจ์ ์ ์ง ๋ณด์กฐ, ๊ทธ๋ฆฌ๊ณ ๊ธด๊ธ ์๋ ์ ๋๊ณผ ๊ฐ์ ๋ฅ๋ ์์ ๊ธฐ์ ์ด ์ด๋ฏธ ์์
ํํ๊ณ ์๋ค. ์ด๋ฌํ ๊ธฐ์ ์ ์ง๋ณด๋ ์ฌ์๋ฅ ์ ๋ก๋ฅผ ๋ฌ์ฑํ๊ธฐ ์ํ์ฌ ๊ธฐ์ ์ฐ๊ตฌ ๋ถ์ผ๋ฅผ ๋ฅ๋ ์์ ๊ธฐ์ ์ ๋์ด์ ์์จ์ฃผํ ์์คํ
์ผ๋ก ํ์ฅ์ํค๊ณ ์๋ค. ํนํ, ๋์ฌ ๋๋ก๋ ์ธ๋, ์ฌ๊ฐ์ง๋, ์ฃผ์ฐจ์ฐจ๋, ์ด๋ฅ์ฐจ, ๋ณดํ์ ๋ฑ๊ณผ ๊ฐ์ ๊ตํต ์ํ ์์๋ฅผ ๋ง์ด ๊ฐ๊ณ ์๊ธฐ ๋๋ฌธ์ ๊ณ ์๋๋ก๋ณด๋ค ์ฌ๊ณ ๋ฐ์๋ฅ ๊ณผ ์ฌ์๋ฅ ์ด ๋์ผ๋ฉฐ, ์ด๋ ๋์ฌ ๋๋ก์์์ ์์จ์ฃผํ์ ํต์ฌ ์ด์๊ฐ ๋๊ณ ์๋ค. ๋ง์ ํ๋ก์ ํธ๋ค์ด ์์จ์ฃผํ์ ํ๊ฒฝ์ , ์ธ๊ตฌํ์ , ์ฌํ์ , ๊ทธ๋ฆฌ๊ณ ๊ฒฝ์ ์ ์ธก๋ฉด์์์ ์์จ์ฃผํ์ ํจ๊ณผ๋ฅผ ํ๊ฐํ๊ธฐ ์ํด ์ํ๋์๊ฑฐ๋ ์ํ ์ค์ ์๋ค. ์๋ฅผ ๋ค์ด, ์ ๋ฝ์ AdaptIVE๋ ๋ค์ํ ์์จ์ฃผํ ๊ธฐ๋ฅ์ ๊ฐ๋ฐํ์์ผ๋ฉฐ, ๊ตฌ์ฒด์ ์ธ ํ๊ฐ ๋ฐฉ๋ฒ๋ก ์ ๊ฐ๋ฐํ์๋ค. ๋ํ, CityMobil2๋ ์ ๋ฝ ์ ์ญ์ 9๊ฐ์ ๋ค๋ฅธ ํ๊ฒฝ์์ ๋ฌด์ธ ์ง๋ฅํ ์ฐจ๋์ ์ฑ๊ณต์ ์ผ๋ก ํตํฉํ์๋ค. ์ผ๋ณธ์์๋ 2014๋
5์์ ์์๋ Automated Driving System Research Project๋ ์์จ์ฃผํ ์์คํ
๊ณผ ์ฐจ์ธ๋ ๋์ฌ ๊ตํต ์๋จ์ ๊ฐ๋ฐ ๋ฐ ๊ฒ์ฆ์ ์ด์ ์ ๋ง์ถ์๋ค.
๊ธฐ์กด ์ฐ๊ตฌ๋ค์ ๋ํ ์กฐ์ฌ๋ฅผ ํตํด ์์จ์ฃผํ ์์คํ
์ ๊ตํต ์ฐธ์ฌ์๋ค์ ์์ ๋๋ฅผ ํฅ์์ํค๊ณ , ๊ตํต ํผ์ก์ ๊ฐ์์ํค๋ฉฐ, ์ด์ ์ ํธ์์ฑ์ ์ฆ์ง์ํค๋ ๊ฒ์ด ์ฆ๋ช
๋์๋ค. ๋ค์ํ ๋ฐฉ๋ฒ๋ก ๋ค์ด ์ธ์ง, ๊ฑฐ๋ ๊ณํ, ๊ทธ๋ฆฌ๊ณ ์ ์ด์ ๊ฐ์ ๋์ฌ ๋๋ก ์์จ์ฃผํ์ฐจ์ ํต์ฌ ๊ธฐ์ ๋ค์ ๊ฐ๋ฐํ๊ธฐ ์ํ์ฌ ์ฌ์ฉ๋์๋ค. ํ์ง๋ง ๋ง์ ์ต์ ์ ์์จ์ฃผํ ์ฐ๊ตฌ๋ค์ ๊ฐ ๊ธฐ์ ์ ๊ฐ๋ฐ์ ๋ณ๊ฐ๋ก ๊ณ ๋ คํ์ฌ ์งํํด์๋ค. ๊ฒฐ๊ณผ์ ์ผ๋ก ํตํฉ์ ์ธ ๊ด์ ์์์ ์์จ์ฃผํ ๊ธฐ์ ์ค๊ณ๋ ์์ง ์ถฉ๋ถํ ๊ณ ๋ ค๋์ด ์์๋ค.
๋ฐ๋ผ์, ๋ณธ ๋
ผ๋ฌธ์ ๋ณต์กํ ๋์ฌ ๋๋ก ํ๊ฒฝ์์ ๋ผ์ด๋ค, ์นด๋ฉ๋ผ, GPS, ๊ทธ๋ฆฌ๊ณ ๊ฐ๋จํ ๊ฒฝ๋ก ๋งต์ ๊ธฐ๋ฐํ ์์ ์์จ์ฃผํ ์๊ณ ๋ฆฌ์ฆ์ ๊ฐ๋ฐํ๋ ๊ฒ์ ๋ชฉํ๋ก ํ์๋ค. ์ ์๋ ์์จ์ฃผํ ์๊ณ ๋ฆฌ์ฆ์ ๋นํต์ ๊ต์ฐจ๋ก๋ฅผ ํฌํจํ ๋์ฌ ๋๋ก ์ํฉ์ ์ฐจ๋ ๊ฑฐ๋ ์์ธก๊ธฐ์ ๋ชจ๋ธ ์์ธก ์ ์ด ๊ธฐ๋ฒ์ ๊ธฐ๋ฐํ์ฌ ์ค๊ณ๋์๋ค. ๋ณธ ๋
ผ๋ฌธ์ ๋์ , ์ ์ ํ๊ฒฝ ํํ ๋ฐ ์ข
ํก๋ฐฉํฅ ๊ฑฐ๋ ๊ณํ์ ์ค์ ์ ์ผ๋ก ๋ค๋ฃจ์๋ค.
๋ณธ ๋
ผ๋ฌธ์ ๋์ฌ ๋๋ก ์์จ์ฃผํ์ ์ํ ๊ฑฐ๋ ๊ณํ ์๊ณ ๋ฆฌ์ฆ์ ๊ฐ์๋ฅผ ์ ์ํ์์ผ๋ฉฐ, ์ค์ ๊ตํต ์ํฉ์์์ ์คํ ๊ฒฐ๊ณผ๋ ์ ์๋ ์๊ณ ๋ฆฌ์ฆ์ ํจ๊ณผ์ฑ๊ณผ ์ด์ ์ ๊ฑฐ๋๊ณผ์ ์ ์ฌ์ฑ์ ๋ณด์ฌ์ฃผ์๋ค. ์ค์ฐจ ์คํ ๊ฒฐ๊ณผ๋ ๋นํต์ ๊ต์ฐจ๋ก๋ฅผ ํฌํจํ ๋์ฌ ์๋๋ฆฌ์ค์์์ ๊ฐ๊ฑดํ ์ฑ๋ฅ์ ๋ณด์ฌ์ฃผ์๋ค.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
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