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    Managed information gathering and fusion for transient transport problems

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    This paper deals with vehicular traffic management by communication technologies from Traffic Control Center point of view in road networks. The global goal is to manage the urban traffic by road traffic operations, controlling and interventional possibilities in order to minimize the traffic delays and stops and to improve traffic safety on the roads. This paper focuses on transient transport, when the controlling management is crucial. The aim was to detect the beginning time of the transient traffic on the roads, to gather the most appropriate data and to get reliable information for interventional suggestions. More reliable information can be created by information fusion, several fusion techniques are expounded in this paper. A half-automatic solution with Decision Support System has been developed to help with engineers in suggestions of interventions based on real time traffic data. The information fusion has benefits for Decision Support System: the complementary sensors may fill the gaps of one another, the system is able to detect the changing of the percentage of different vehicle types in traffic. An example of detection and interventional suggestion about transient traffic on transport networks of a little town is presented at the end of the paper. The novelty of this paper is the gathering of information - triggered by the state changing from stationer to transient - from ad hoc channels and combining them with information from developed regular channels. --information gathering,information fusion,Kalman filter,transient traffic,Decision Support System

    ๋„์‹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 Study on Recent Developments and Issues with Obstacle Detection Systems for Automated Vehicles

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    This paper reviews current developments and discusses some critical issues with obstacle detection systems for automated vehicles. The concept of autonomous driving is the driver towards future mobility. Obstacle detection systems play a crucial role in implementing and deploying autonomous driving on our roads and city streets. The current review looks at technology and existing systems for obstacle detection. Specifically, we look at the performance of LIDAR, RADAR, vision cameras, ultrasonic sensors, and IR and review their capabilities and behaviour in a number of different situations: during daytime, at night, in extreme weather conditions, in urban areas, in the presence of smooths surfaces, in situations where emergency service vehicles need to be detected and recognised, and in situations where potholes need to be observed and measured. It is suggested that combining different technologies for obstacle detection gives a more accurate representation of the driving environment. In particular, when looking at technological solutions for obstacle detection in extreme weather conditions (rain, snow, fog), and in some specific situations in urban areas (shadows, reflections, potholes, insufficient illumination), although already quite advanced, the current developments appear to be not sophisticated enough to guarantee 100% precision and accuracy, hence further valiant effort is needed

    Automatic Detection of Stains on Lidar Glass Houses and Notice for Cleaning

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    For achieving a sustainable and smarter transportation system, sensor technology needs to be combined with transportation infrastructure. Traffic sensors are significant in todayโ€™s world since the conventional visual inspection is inadequate for steering quality control and traffic safety, efficiency being of utmost importance, high-speed and accuracy automated inspection becomes crucial. No system is perfect, and Lidar is no different.Although Lidar, sensor has gained its popularity with its 360-degree monitoring and visualization, being a relatively new technology, it has its frail spots too. Mainly, on roadside, factors like surface obstacles or environmental condition, influence its performance with uncertainty of cloud point movement from its true value. Therefore, through this study, a standard method based on the difference of offset is proposed to check the quality of data for real road deployment and answer a very foundation question from traffic engineering, about the obstacles recognition on lidar glass houses, and how often to clean the sensor increasing confidence on such systems . In this study, multiple experiments, comparing different conditions of sensor surface was conducted where real time frame was compared to standard frame and the frame offset was used to define a threshold value and over threshold offset time. The experiment was conducted with varied beams, scenarios and further the method was validated with real time traffic data. Sensor itself may have many cavities; we only needed to know the limiting range to accommodate real traffic. This study therefore contributed by developing method to find out influence of stain on sensor automatically and notify related agency, the time to clean the sensor without validation from engineers at intersections at every small interval

    Road Surface Feature Extraction and Reconstruction of Laser Point Clouds for Urban Environment

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    Automakers are developing end-to-end three-dimensional (3D) mapping system for Advanced Driver Assistance Systems (ADAS) and autonomous vehicles (AVs). Using geomatics, artificial intelligence, and SLAM (Simultaneous Localization and Mapping) systems to handle all stages of map creation, sensor calibration and alignment. It is crucial to have a system highly accurate and efficient as it is an essential part of vehicle controls. Such mapping requires significant resources to acquire geographic information (GIS and GPS), optical laser and radar spectroscopy, Lidar, and 3D modeling applications in order to extract roadway features (e.g., lane markings, traffic signs, road-edges) detailed enough to construct a โ€œbase mapโ€. To keep this map current, it is necessary to update changes due to occurring events such as construction changes, traffic patterns, or growth of vegetation. The information of the road play a very important factor in road traffic safety and it is essential for for guiding autonomous vehicles (AVs), and prediction of upcoming road situations within AVs. The data size of the map is extensive due to the level of information provided with different sensor modalities for that reason a data optimization and extraction from three-dimensional (3D) mobile laser scanning (MLS) point clouds is presented in this thesis. The research shows the proposed hybrid filter configuration together with the dynamic developed mechanism provides significant reduction of the point cloud data with reduced computational or size constraints. The results obtained in this work are proven by a real-world system

    Challenges of bridge maintenance inspection

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    Bridges are amongst the largest, most expensive and complex structures, which makes them crucial and valuable transportation asset for modern infrastructure. Bridge inspection is a crucial component of monitoring and maintaining these complex structures. It provides a safety assessment and condition documentation on a regular basis, noting maintenance actions needed to counteract defects like cracks, corrosion and spalling. This paper presents the challenges with existing bridge maintenance inspection as well as an overview on proposed methods to overcome these challenges by automating inspection using computer vision methods. As a conclusion, existing methods for automated bridge inspection are able to detect one class of damage type based on images. A multiclass approach that also considers the 3D geometry, as inspectors do, is missing

    Uses and Challenges of Collecting LiDAR Data from a Growing Autonomous Vehicle Fleet: Implications for Infrastructure Planning and Inspection Practices

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    Autonomous vehicles (AVs) that utilize LiDAR (Light Detection and Ranging) and other sensing technologies are becoming an inevitable part of transportation industry. Concurrently, transportation agencies are increasingly challenged with the management and tracking of large-scale highway asset inventory. LiDAR has become popular among transportation agencies for highway asset management given its advantage over traditional surveying methods. The affordability of LiDAR technology is increasing day by day. Given this, there will be substantial challenges and opportunities for the utilization of big data resulting from the growth of AVs with LiDAR. A proper understanding of the data size generated from this technology will help agencies in making decisions regarding storage, management, and transmission of the data. The original raw data generated from the sensor shrinks a lot after filtering and processing following the Cache county Road Manual and storing into ASPRS recommended (.las) file format. In this pilot study, it is found that while considering the road centerline as the vehicle trajectory larger portion of the data fall into the right of way section compared to the actual vehicle trajectory in Cache County, UT. And there is a positive relation between the data size and vehicle speed in terms of the travel lanes section given the nature of the selected highway environment
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