3,733 research outputs found

    Navigating Occluded Intersections with Autonomous Vehicles using Deep Reinforcement Learning

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    Providing an efficient strategy to navigate safely through unsignaled intersections is a difficult task that requires determining the intent of other drivers. We explore the effectiveness of Deep Reinforcement Learning to handle intersection problems. Using recent advances in Deep RL, we are able to learn policies that surpass the performance of a commonly-used heuristic approach in several metrics including task completion time and goal success rate and have limited ability to generalize. We then explore a system's ability to learn active sensing behaviors to enable navigating safely in the case of occlusions. Our analysis, provides insight into the intersection handling problem, the solutions learned by the network point out several shortcomings of current rule-based methods, and the failures of our current deep reinforcement learning system point to future research directions.Comment: IEEE International Conference on Robotics and Automation (ICRA 2018

    Decision-Making for Automated Vehicles Using a Hierarchical Behavior-Based Arbitration Scheme

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    Behavior planning and decision-making are some of the biggest challenges for highly automated systems. A fully automated vehicle (AV) is confronted with numerous tactical and strategical choices. Most state-of-the-art AV platforms implement tactical and strategical behavior generation using finite state machines. However, these usually result in poor explainability, maintainability and scalability. Research in robotics has raised many architectures to mitigate these problems, most interestingly behavior-based systems and hybrid derivatives. Inspired by these approaches, we propose a hierarchical behavior-based architecture for tactical and strategical behavior generation in automated driving. It is a generalizing and scalable decision-making framework, utilizing modular behavior blocks to compose more complex behaviors in a bottom-up approach. The system is capable of combining a variety of scenario- and methodology-specific solutions, like POMDPs, RRT* or learning-based behavior, into one understandable and traceable architecture. We extend the hierarchical behavior-based arbitration concept to address scenarios where multiple behavior options are applicable but have no clear priority against each other. Then, we formulate the behavior generation stack for automated driving in urban and highway environments, incorporating parking and emergency behaviors as well. Finally, we illustrate our design in an explanatory evaluation

    ๋„์‹ฌ ๊ต์ฐจ๋กœ์—์„œ์˜ ์ž์œจ์ฃผํ–‰์„ ์œ„ํ•œ ์ฃผ๋ณ€ ์ฐจ๋Ÿ‰ ๊ฒฝ๋กœ ์˜ˆ์ธก ๋ฐ ๊ฑฐ๋™ ๊ณ„ํš ์•Œ๊ณ ๋ฆฌ์ฆ˜

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :๊ณต๊ณผ๋Œ€ํ•™ ๊ธฐ๊ณ„ํ•ญ๊ณต๊ณตํ•™๋ถ€,2020. 2. ์ด๊ฒฝ์ˆ˜.์ฐจ๋ž‘์šฉ ์„ผ์‹ฑ ๋ฐ ์ฒ˜๋ฆฌ๊ธฐ์ˆ ์ด ๋ฐœ๋‹ฌํ•จ์— ๋”ฐ๋ผ ์ž๋™์ฐจ ๊ธฐ์ˆ  ์—ฐ๊ตฌ๊ฐ€ ์ˆ˜๋™ ์•ˆ์ „ ๊ธฐ์ˆ ์—์„œ ๋Šฅ๋™ ์•ˆ์ „ ๊ธฐ์ˆ ๋กœ ์ดˆ์ ์ด ํ™•์žฅ๋˜๊ณ  ์žˆ๋‹ค. ์ตœ๊ทผ, ์ฃผ์š” ์ž๋™์ฐจ ์ œ์ž‘์‚ฌ๋“ค์€ ๋Šฅ๋™ํ˜• ์ฐจ๊ฐ„๊ฑฐ๋ฆฌ ์ œ์–ด, ์ฐจ์„  ์œ ์ง€ ๋ณด์กฐ, ๊ทธ๋ฆฌ๊ณ  ๊ธด๊ธ‰ ์ž๋™ ์ œ๋™๊ณผ ๊ฐ™์€ ๋Šฅ๋™ ์•ˆ์ „ ๊ธฐ์ˆ ์ด ์ด๋ฏธ ์ƒ์—…ํ™”ํ•˜๊ณ  ์žˆ๋‹ค. ์ด๋Ÿฌํ•œ ๊ธฐ์ˆ ์  ์ง„๋ณด๋Š” ์‚ฌ์ƒ๋ฅ  ์ œ๋กœ๋ฅผ ๋‹ฌ์„ฑํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ๊ธฐ์ˆ  ์—ฐ๊ตฌ ๋ถ„์•ผ๋ฅผ ๋Šฅ๋™ ์•ˆ์ „ ๊ธฐ์ˆ ์„ ๋„˜์–ด์„œ ์ž์œจ์ฃผํ–‰ ์‹œ์Šคํ…œ์œผ๋กœ ํ™•์žฅ์‹œํ‚ค๊ณ  ์žˆ๋‹ค. ํŠนํžˆ, ๋„์‹ฌ ๋„๋กœ๋Š” ์ธ๋„, ์‚ฌ๊ฐ์ง€๋Œ€, ์ฃผ์ฐจ์ฐจ๋Ÿ‰, ์ด๋ฅœ์ฐจ, ๋ณดํ–‰์ž ๋“ฑ๊ณผ ๊ฐ™์€ ๊ตํ†ต ์œ„ํ—˜ ์š”์†Œ๋ฅผ ๋งŽ์ด ๊ฐ–๊ณ  ์žˆ๊ธฐ ๋•Œ๋ฌธ์— ๊ณ ์†๋„๋กœ๋ณด๋‹ค ์‚ฌ๊ณ  ๋ฐœ์ƒ๋ฅ ๊ณผ ์‚ฌ์ƒ๋ฅ ์ด ๋†’์œผ๋ฉฐ, ์ด๋Š” ๋„์‹ฌ ๋„๋กœ์—์„œ์˜ ์ž์œจ์ฃผํ–‰์€ ํ•ต์‹ฌ ์ด์Šˆ๊ฐ€ ๋˜๊ณ  ์žˆ๋‹ค. ๋งŽ์€ ํ”„๋กœ์ ํŠธ๋“ค์ด ์ž์œจ์ฃผํ–‰์˜ ํ™˜๊ฒฝ์ , ์ธ๊ตฌํ•™์ , ์‚ฌํšŒ์ , ๊ทธ๋ฆฌ๊ณ  ๊ฒฝ์ œ์  ์ธก๋ฉด์—์„œ์˜ ์ž์œจ์ฃผํ–‰์˜ ํšจ๊ณผ๋ฅผ ํ‰๊ฐ€ํ•˜๊ธฐ ์œ„ํ•ด ์ˆ˜ํ–‰๋˜์—ˆ๊ฑฐ๋‚˜ ์ˆ˜ํ–‰ ์ค‘์— ์žˆ๋‹ค. ์˜ˆ๋ฅผ ๋“ค์–ด, ์œ ๋Ÿฝ์˜ AdaptIVE๋Š” ๋‹ค์–‘ํ•œ ์ž์œจ์ฃผํ–‰ ๊ธฐ๋Šฅ์„ ๊ฐœ๋ฐœํ•˜์˜€์œผ๋ฉฐ, ๊ตฌ์ฒด์ ์ธ ํ‰๊ฐ€ ๋ฐฉ๋ฒ•๋ก ์„ ๊ฐœ๋ฐœํ•˜์˜€๋‹ค. ๋˜ํ•œ, CityMobil2๋Š” ์œ ๋Ÿฝ ์ „์—ญ์˜ 9๊ฐœ์˜ ๋‹ค๋ฅธ ํ™˜๊ฒฝ์—์„œ ๋ฌด์ธ ์ง€๋Šฅํ˜• ์ฐจ๋Ÿ‰์„ ์„ฑ๊ณต์ ์œผ๋กœ ํ†ตํ•ฉํ•˜์˜€๋‹ค. ์ผ๋ณธ์—์„œ๋Š” 2014๋…„ 5์›”์— ์‹œ์ž‘๋œ Automated Driving System Research Project๋Š” ์ž์œจ์ฃผํ–‰ ์‹œ์Šคํ…œ๊ณผ ์ฐจ์„ธ๋Œ€ ๋„์‹ฌ ๊ตํ†ต ์ˆ˜๋‹จ์˜ ๊ฐœ๋ฐœ ๋ฐ ๊ฒ€์ฆ์— ์ดˆ์ ์„ ๋งž์ถ”์—ˆ๋‹ค. ๊ธฐ์กด ์—ฐ๊ตฌ๋“ค์— ๋Œ€ํ•œ ์กฐ์‚ฌ๋ฅผ ํ†ตํ•ด ์ž์œจ์ฃผํ–‰ ์‹œ์Šคํ…œ์€ ๊ตํ†ต ์ฐธ์—ฌ์ž๋“ค์˜ ์•ˆ์ „๋„๋ฅผ ํ–ฅ์ƒ์‹œํ‚ค๊ณ , ๊ตํ†ต ํ˜ผ์žก์„ ๊ฐ์†Œ์‹œํ‚ค๋ฉฐ, ์šด์ „์ž ํŽธ์˜์„ฑ์„ ์ฆ์ง„์‹œํ‚ค๋Š” ๊ฒƒ์ด ์ฆ๋ช…๋˜์—ˆ๋‹ค. ๋‹ค์–‘ํ•œ ๋ฐฉ๋ฒ•๋ก ๋“ค์ด ์ธ์ง€, ๊ฑฐ๋™ ๊ณ„ํš, ๊ทธ๋ฆฌ๊ณ  ์ œ์–ด์™€ ๊ฐ™์€ ๋„์‹ฌ ๋„๋กœ ์ž์œจ์ฃผํ–‰์ฐจ์˜ ํ•ต์‹ฌ ๊ธฐ์ˆ ๋“ค์„ ๊ฐœ๋ฐœํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ์‚ฌ์šฉ๋˜์—ˆ๋‹ค. ํ•˜์ง€๋งŒ ๋งŽ์€ ์ตœ์‹ ์˜ ์ž์œจ์ฃผํ–‰ ์—ฐ๊ตฌ๋“ค์€ ๊ฐ ๊ธฐ์ˆ ์˜ ๊ฐœ๋ฐœ์„ ๋ณ„๊ฐœ๋กœ ๊ณ ๋ คํ•˜์—ฌ ์ง„ํ–‰ํ•ด์™”๋‹ค. ๊ฒฐ๊ณผ์ ์œผ๋กœ ํ†ตํ•ฉ์ ์ธ ๊ด€์ ์—์„œ์˜ ์ž์œจ์ฃผํ–‰ ๊ธฐ์ˆ  ์„ค๊ณ„๋Š” ์•„์ง ์ถฉ๋ถ„ํžˆ ๊ณ ๋ ค๋˜์–ด ์•Š์•˜๋‹ค. ๋”ฐ๋ผ์„œ, ๋ณธ ๋…ผ๋ฌธ์€ ๋ณต์žกํ•œ ๋„์‹ฌ ๋„๋กœ ํ™˜๊ฒฝ์—์„œ ๋ผ์ด๋‹ค, ์นด๋ฉ”๋ผ, GPS, ๊ทธ๋ฆฌ๊ณ  ๊ฐ„๋‹จํ•œ ๊ฒฝ๋กœ ๋งต์— ๊ธฐ๋ฐ˜ํ•œ ์™„์ „ ์ž์œจ์ฃผํ–‰ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ๊ฐœ๋ฐœํ•˜๋Š” ๊ฒƒ์„ ๋ชฉํ‘œ๋กœ ํ•˜์˜€๋‹ค. ์ œ์•ˆ๋œ ์ž์œจ์ฃผํ–‰ ์•Œ๊ณ ๋ฆฌ์ฆ˜์€ ๋น„ํ†ต์ œ ๊ต์ฐจ๋กœ๋ฅผ ํฌํ•จํ•œ ๋„์‹ฌ ๋„๋กœ ์ƒํ™ฉ์„ ์ฐจ๋Ÿ‰ ๊ฑฐ๋™ ์˜ˆ์ธก๊ธฐ์™€ ๋ชจ๋ธ ์˜ˆ์ธก ์ œ์–ด ๊ธฐ๋ฒ•์— ๊ธฐ๋ฐ˜ํ•˜์—ฌ ์„ค๊ณ„๋˜์—ˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์€ ๋™์ , ์ •์  ํ™˜๊ฒฝ ํ‘œํ˜„ ๋ฐ ์ข…ํšก๋ฐฉํ–ฅ ๊ฑฐ๋™ ๊ณ„ํš์„ ์ค‘์ ์ ์œผ๋กœ ๋‹ค๋ฃจ์—ˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์€ ๋„์‹ฌ ๋„๋กœ ์ž์œจ์ฃผํ–‰์„ ์œ„ํ•œ ๊ฑฐ๋™ ๊ณ„ํš ์•Œ๊ณ ๋ฆฌ์ฆ˜์˜ ๊ฐœ์š”๋ฅผ ์ œ์‹œํ•˜์˜€์œผ๋ฉฐ, ์‹ค์ œ ๊ตํ†ต ์ƒํ™ฉ์—์„œ์˜ ์‹คํ—˜ ๊ฒฐ๊ณผ๋Š” ์ œ์•ˆ๋œ ์•Œ๊ณ ๋ฆฌ์ฆ˜์˜ ํšจ๊ณผ์„ฑ๊ณผ ์šด์ „์ž ๊ฑฐ๋™๊ณผ์˜ ์œ ์‚ฌ์„ฑ์„ ๋ณด์—ฌ์ฃผ์—ˆ๋‹ค. ์‹ค์ฐจ ์‹คํ—˜ ๊ฒฐ๊ณผ๋Š” ๋น„ํ†ต์ œ ๊ต์ฐจ๋กœ๋ฅผ ํฌํ•จํ•œ ๋„์‹ฌ ์‹œ๋‚˜๋ฆฌ์˜ค์—์„œ์˜ ๊ฐ•๊ฑดํ•œ ์„ฑ๋Šฅ์„ ๋ณด์—ฌ์ฃผ์—ˆ๋‹ค.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

    Neural Network Predicting Remote Vehicle Movement with Vehicle-to-Vehicle Data

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    This paper presents a neural network developed for predicting the path of a remote vehicle using post facto created vehicle-to-vehicle (V2V) data and uses that prediction to determine whether it is safe for the host vehicle to change lanes. The data was collected in a 2013 experiment involving various drivers traveling on public roads in Ann Arbor, MI. The trips were on suburban roads, city roads and divided highways over a two-day period. The vehicular satellite global positioning system (GPS) data from movement over this period was gathered and post-processed to find vehicle paths within 10 meters of one another. The path traces of the two vehicles were combined to simulate what a V2V network would have provided to properly equipped vehicles if such a network and vehicles existed on real road networks demonstrating natural driving behavior. This research harnesses this data to determine the increased effectiveness of a neural network predicting the future path of remote vehicles and lane change safety when a V2V network is available. The most studied maneuver is overtaking. To a lesser extent, this paper also provides a view into how a neural network predicts remote vehicle behaviors using a host vehicle equipped with only perceptive hardware and no given information from the remote vehicle.Master of Science in EngineeringElectrical Engineering, College of Engineering & Computer ScienceUniversity of Michigan-Dearbornhttps://deepblue.lib.umich.edu/bitstream/2027.42/146791/1/49698122_breg_thesis_embedded (1).pd

    Real-time motion planning methods for autonomous on-road driving: state-of-the-art and future research directions

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    Currently autonomous or self-driving vehicles are at the heart of academia and industry research because of its multi-faceted advantages that includes improved safety, reduced congestion, lower emissions and greater mobility. Software is the key driving factor underpinning autonomy within which planning algorithms that are responsible for mission-critical decision making hold a significant position. While transporting passengers or goods from a given origin to a given destination, motion planning methods incorporate searching for a path to follow, avoiding obstacles and generating the best trajectory that ensures safety, comfort and efficiency. A range of different planning approaches have been proposed in the literature. The purpose of this paper is to review existing approaches and then compare and contrast different methods employed for the motion planning of autonomous on-road driving that consists of (1) finding a path, (2) searching for the safest manoeuvre and (3) determining the most feasible trajectory. Methods developed by researchers in each of these three levels exhibit varying levels of complexity and performance accuracy. This paper presents a critical evaluation of each of these methods, in terms of their advantages/disadvantages, inherent limitations, feasibility, optimality, handling of obstacles and testing operational environments. Based on a critical review of existing methods, research challenges to address current limitations are identified and future research directions are suggested so as to enhance the performance of planning algorithms at all three levels. Some promising areas of future focus have been identified as the use of vehicular communications (V2V and V2I) and the incorporation of transport engineering aspects in order to improve the look-ahead horizon of current sensing technologies that are essential for planning with the aim of reducing the total cost of driverless vehicles. This critical review on planning techniques presented in this paper, along with the associated discussions on their constraints and limitations, seek to assist researchers in accelerating development in the emerging field of autonomous vehicle research
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