6,214 research outputs found

    Recursive Inference for Prediction of Objects in Urban Environments

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    Abstract Future advancements in robotic navigation and mapping rest to a large extent on robust, efficient and more advanced semantic understanding of the surrounding environment. The existing semantic mapping approaches typically consider small number of semantic categories, require complex inference or large number of training examples to achieve desirable performance. In the proposed work we present an efficient approach for predicting locations of generic objects in urban environments by means of semantic segmentation of a video into object and nonobject categories. We exploit widely available exemplars of non-object categories (such as road, buildings, vegetation) and use geometric cues which are indicative of the presence of object boundaries to gather the evidence about objects regardless of their category. We formulate the object/non-object semantic segmentation problem in the Conditional Random Field framework, where the structure of the graph is induced by a minimum spanning tree computed over a 3D point cloud, yielding an efficient algorithm for an exact inference. The chosen 3D representation naturally lends itself for on-line recursive belief updates with a simple soft data association mechanism. We carry out extensive experiments on videos of urban environments acquired by a moving vehicle and show quantitatively and qualitatively the benefits of our proposal.

    Human Motion Trajectory Prediction: A Survey

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    With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand and anticipate human behavior becomes increasingly important. Specifically, predicting future positions of dynamic agents and planning considering such predictions are key tasks for self-driving vehicles, service robots and advanced surveillance systems. This paper provides a survey of human motion trajectory prediction. We review, analyze and structure a large selection of work from different communities and propose a taxonomy that categorizes existing methods based on the motion modeling approach and level of contextual information used. We provide an overview of the existing datasets and performance metrics. We discuss limitations of the state of the art and outline directions for further research.Comment: Submitted to the International Journal of Robotics Research (IJRR), 37 page

    Using Bayesian Programming for Multisensor Multi-Target Tracking in Automative Applications

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    A prerequisite to the design of future Advanced Driver Assistance Systems for cars is a sensing system providing all the information required for high-level driving assistance tasks. Carsense is a European project whose purpose is to develop such a new sensing system. It will combine different sensors (laser, radar and video) and will rely on the fusion of the information coming from these sensors in order to achieve better accuracy, robustness and an increase of the information content. This paper demonstrates the interest of using probabilistic reasoning techniques to address this challenging multi-sensor data fusion problem. The approach used is called Bayesian Programming. It is a general approach based on an implementation of the Bayesian theory. It was introduced rst to design robot control programs but its scope of application is much broader and it can be used whenever one has to deal with problems involving uncertain or incomplete knowledge

    ๋„์‹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

    A tripartite filter design for seamless pedestrian navigation using recursive 2-means clustering and Tukey update

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    Mobile devices are desired to guide users seamlessly to diverse destinations indoors and outdoors. The positioning fixing subsystems often provide poor quality measurements with gaps in an urban environment. No single position fixing technology works continuously. Many sensor fusion variations have been previously trialed to overcome this challenge, including the particle filter that is robust and the Kalman filter which is fast. However, a lack exists, of context aware, seamless systems that are able to use the most fit sensors and methods in the correct context. A novel adaptive and modular tripartite navigation filter design is presented to enable seamless navigation. It consists of a sensor subsystem, a context inference and a navigation filter blocks. A foot-mounted inertial measurement unit (IMU), a Global Navigation Satellite System (GNSS) receiver, Bluetooth Low Energy (BLE) and Ultrawideband (UWB) positioning systems were used in the evaluation implementation of this design. A novel recursive 2-means clustering method was developed to track multiple hypotheses when there are gaps in position fixes. The closest hypothesis to a new position fix is selected when the gap ends. Moreover, when the position fix quality measure is not reliable, a fusion approach using a Tukey-style particle filter measurement update is introduced. Results show the successful operation of the design implementation. The Tukey update improves accuracy by 5% and together with the clustering method the system robustness is enhanced

    Extended Object Tracking: Introduction, Overview and Applications

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    This article provides an elaborate overview of current research in extended object tracking. We provide a clear definition of the extended object tracking problem and discuss its delimitation to other types of object tracking. Next, different aspects of extended object modelling are extensively discussed. Subsequently, we give a tutorial introduction to two basic and well used extended object tracking approaches - the random matrix approach and the Kalman filter-based approach for star-convex shapes. The next part treats the tracking of multiple extended objects and elaborates how the large number of feasible association hypotheses can be tackled using both Random Finite Set (RFS) and Non-RFS multi-object trackers. The article concludes with a summary of current applications, where four example applications involving camera, X-band radar, light detection and ranging (lidar), red-green-blue-depth (RGB-D) sensors are highlighted.Comment: 30 pages, 19 figure
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