Learning to See the Wood for the Trees: Deep Laser Localization in Urban and Natural Environments on a CPU

Localization in challenging, natural environments such as forests or woodlands is an important capability for many applications from guiding a robot navigating along a forest trail to monitoring vegetation growth with handheld sensors. In this work we explore laser-based localization in both urban and natural environments, which is suitable for online applications. We propose a deep learning approach capable of learning meaningful descriptors directly from 3D point clouds by comparing triplets (anchor, positive and negative examples). The approach learns a feature space representation for a set of segmented point clouds that are matched between a current and previous observations. Our learning method is tailored towards loop closure detection resulting in a small model which can be deployed using only a CPU. The proposed learning method would allow the full pipeline to run on robots with limited computational payload such as drones, quadrupeds or UGVs.Comment: Accepted for publication at RA-L/ICRA 2019. More info: https://ori.ox.ac.uk/esm-localizatio

Large-scale Geometric Data Decomposition, Processing and Structured Mesh Generation

Mesh generation is a fundamental and critical problem in geometric data modeling and processing. In most scientific and engineering tasks that involve numerical computations and simulations on 2D/3D regions or on curved geometric objects, discretizing or approximating the geometric data using a polygonal or polyhedral meshes is always the first step of the procedure. The quality of this tessellation often dictates the subsequent computation accuracy, efficiency, and numerical stability. When compared with unstructured meshes, the structured meshes are favored in many scientific/engineering tasks due to their good properties. However, generating high-quality structured mesh remains challenging, especially for complex or large-scale geometric data. In industrial Computer-aided Design/Engineering (CAD/CAE) pipelines, the geometry processing to create a desirable structural mesh of the complex model is the most costly step. This step is semi-manual, and often takes up to several weeks to finish. Several technical challenges remains unsolved in existing structured mesh generation techniques. This dissertation studies the effective generation of structural mesh on large and complex geometric data. We study a general geometric computation paradigm to solve this problem via model partitioning and divide-and-conquer. To apply effective divide-and-conquer, we study two key technical components: the shape decomposition in the divide stage, and the structured meshing in the conquer stage. We test our algorithm on vairous data set, the results demonstrate the efficiency and effectiveness of our framework. The comparisons also show our algorithm outperforms existing partitioning methods in final meshing quality. We also show our pipeline scales up efficiently on HPC environment

Intelligent Computational Transportation

Transportation is commonplace around our world. Numerous researchers dedicate great efforts to vast transportation research topics. The purpose of this dissertation is to investigate and address a couple of transportation problems with respect to geographic discretization, pavement surface automatic examination, and traffic ow simulation, using advanced computational technologies. Many applications require a discretized 2D geographic map such that local information can be accessed efficiently. For example, map matching, which aligns a sequence of observed positions to a real-world road network, needs to find all the nearby road segments to the individual positions. To this end, the map is discretized by cells and each cell retains a list of road segments coincident with this cell. An efficient method is proposed to form such lists for the cells without costly overlapping tests. Furthermore, the method can be easily extended to 3D scenarios for fast triangle mesh voxelization. Pavement surface distress conditions are critical inputs for quantifying roadway infrastructure serviceability. Existing computer-aided automatic examination techniques are mainly based on 2D image analysis or 3D georeferenced data set. The disadvantage of information losses or extremely high costs impedes their effectiveness iv and applicability. In this study, a cost-effective Kinect-based approach is proposed for 3D pavement surface reconstruction and cracking recognition. Various cracking measurements such as alligator cracking, traverse cracking, longitudinal cracking, etc., are identified and recognized for their severity examinations based on associated geometrical features. Smart transportation is one of the core components in modern urbanization processes. Under this context, the Connected Autonomous Vehicle (CAV) system presents a promising solution towards the enhanced traffic safety and mobility through state-of-the-art wireless communications and autonomous driving techniques. Due to the different nature between the CAVs and the conventional Human- Driven-Vehicles (HDVs), it is believed that CAV-enabled transportation systems will revolutionize the existing understanding of network-wide traffic operations and re-establish traffic ow theory. This study presents a new continuum dynamics model for the future CAV-enabled traffic system, realized by encapsulating mutually-coupled vehicle interactions using virtual internal and external forces. A Smoothed Particle Hydrodynamics (SPH)-based numerical simulation and an interactive traffic visualization framework are also developed

Efficient voxelization using projected optimal scanline

In the paper, we propose an efficient algorithm for the surface voxelization of 3D geometrically complex models. Unlike recent techniques relying on triangle-voxel intersection tests, our algorithm exploits the conventional parallel-scanline strategy. Observing that there does not exist an optimal scanline interval in general 3D cases if one wants to use parallel voxelized scanlines to cover the interior of a triangle, we subdivide a triangle into multiple axis-aligned slices and carry out the scanning within each polygonal slice. The theoretical optimal scanline interval can be obtained to maximize the efficiency of the algorithm without missing any voxels on the triangle. Once the collection of scanlines are determined and voxelized, we obtain the surface voxelization. We fine tune the algorithm so that it only involves a few operations of integer additions and comparisons for each voxel generated. Finally, we comprehensively compare our method with the state-of-the-art method in terms of theoretical complexity, runtime performance and the quality of the voxelization on both CPU and GPU of a regular desktop PC, as well as on a mobile device. The results show that our method outperforms the existing method, especially when the resolution of the voxelization is high

SDVRF: Sparse-to-Dense Voxel Region Fusion for Multi-modal 3D Object Detection

In the perception task of autonomous driving, multi-modal methods have become a trend due to the complementary characteristics of LiDAR point clouds and image data. However, the performance of previous methods is usually limited by the sparsity of the point cloud or the noise problem caused by the misalignment between LiDAR and the camera. To solve these two problems, we present a new concept, Voxel Region (VR), which is obtained by projecting the sparse local point clouds in each voxel dynamically. And we propose a novel fusion method, named Sparse-to-Dense Voxel Region Fusion (SDVRF). Specifically, more pixels of the image feature map inside the VR are gathered to supplement the voxel feature extracted from sparse points and achieve denser fusion. Meanwhile, different from prior methods, which project the size-fixed grids, our strategy of generating dynamic regions achieves better alignment and avoids introducing too much background noise. Furthermore, we propose a multi-scale fusion framework to extract more contextual information and capture the features of objects of different sizes. Experiments on the KITTI dataset show that our method improves the performance of different baselines, especially on classes of small size, including Pedestrian and Cyclist.Comment: Submitted to IEEE Transactions on Circuits and Systems for Video Technolog

Boxelization: folding 3D objects into boxes

We present a method for transforming a 3D object into a cube or a box using a continuous folding sequence. Our method produces a single, connected object that can be physically fabricated and folded from one shape to the other. We segment the object into voxels and search for a voxel-tree that can fold from the input shape to the target shape. This involves three major steps: finding a good voxelization, finding the tree structure that can form the input and target shapes' configurations, and finding a non-intersecting folding sequence. We demonstrate our results on several input 3D objects and also physically fabricate some using a 3D printer

자율 주행 시스템의 차량 안전을 위한 적응형 관심 영역 기반 효율적 환경 인지

학위논문(박사)--서울대학교 대학원 :공과대학 기계항공공학부,2020. 2. 이경수.전 세계적으로 자동차 사고로 120 만 명이 사망하기 때문에 교통 사고에 대한 기본적인 예방 조치에 대한 논의가 진행되고 있다. 통계 자료에 따르면 교통 사고의 94 %가 인적 오류에 기인한다. 도로 안전 확보의 관점에서 자율 주행 기술은 이러한 심각한 문제를 해결하는 방법으로써 관심이 높아졌으며, 연구 개발을 통해 단계적 상용화가 이루어지고 있다. 주요 자동차 제조업체는 이미 차선 유지 보조장치 (LKAS: Lane Keeping Assistant System), 적응형 순항 제어 시스템(ACC: Adaptive Cruise Control), 주차 보조 시스템 (PAS: Parking Assistance System), 자동 긴급 제동장치 (AEB: Automated Emergency Braking) 등의 첨단 운전자 보조 시스템 (ADAS)을 개발하고 상용화하였다. 또한 Audi의 Audi AI Traffic Jam Pilot, Tesla의 Autopilot, Mercedes-Benz의 Distronic Plus, 현대자동차의 Highway Driving Assist 및 BMW의 Driving Assistant Plus 와 같은 부분 자율 주행 시스템이 출시되고 있다. 이러한 부분 자율 주행 시스템은 여전히 운전자의 주의가 수반되어야 함에도 불구하고, 안전성을 크게 향상시키는 데 효과적이기 때문에 지속적으로 그 수요가 증가하고 있다. 최근 몇 년간 많은 수의 자율주행 사고가 발생하였으며, 그 빈도수가 빠르게 증가하여 사회적으로 주목받고 있다. 차량 사고는 인명 사고와 직접적으로 연관되기 때문에 자율 주행 차량의 사고들은 자율 주행 기술 신뢰성의 저하를 야기하여 사회적인 불안감을 키운다. 최근 자율 주행 관련 사고들로 인해, 자율주행 차량의 안전성의 보장이 더욱 강조되고 있다. 따라서 본 연구에서는 자율 주행 차량의 거동 제어를 고려하여 자율 주행 시스템 관점에서 차량의 안전성을 우선적으로 확보하는 접근 방식을 제안한다. 또한 자율주행 기술 개발은 단순하게 운전을 대체하는 기술이 아니라, 첨단기술의 집약 체로써 산업적으로 매우 큰 파급력을 가진다고 전망된다. 현재 자율주행 시스템은 기존 자동차 산업의 고전적인 틀에서 확장되어, 다양한 분야의 관점에서 주도적으로 개발이 진행되고 있다. 자율 주행은 다양한 기술의 복합적인 결합으로 구성되기 때문에, 현재 각기 다른 다양한 환경에서 개발이 진행 중이며, 아직 표준화되어 있지 않은 실정이다. 대부분 각 모듈 단위의 지엽적인 성능향상을 추구하는 경향이 있으며, 구성 모듈 간 관계가 고려된 전체 시스템 단위의 접근방식은 미흡한 실정이다. 세부 모듈 단위의 지엽적인 연구 개발은 통합 시, 모듈 간 상호작용으로 인한 영향으로 시스템 관점에서 적절한 성능을 확보하기 어려울 수 있다. 각 모듈의 성능만을 고려한 일방적인 방향의 연구는 한계가 명확하며, 연관된 모듈들의 특성을 고려하여 반영할 필요가 있다. 따라서 자율주행 전체 시스템의 관점에서, 차량 안전을 우선적으로 확보하고 전체 성능을 극대화하는 효과적인 접근 방식을 본 연구에서 제안하고자 한다. 본 연구는 자율 주행 시스템의 안정적이고 높은 성능을 확보하기 위해 전체 시스템 작동 측면에서 구성된 모듈 간의 상호 작용을 고려하여 효율적인 환경 인식 알고리즘을 개발하는데 중점을 둔다. 실질적인 관점에서 효과적인 정보 처리를 수행하고 차량 안전을 확보하기 위해 적응형 관심 영역 (ROI) 기반 계산 부하 관리 전략을 제안한다. 차량의 거동 특성, 도로 설계 표준, 추월 및 차선 변경과 같은 주변 차량의 주행 특성이 적응형 ROI 설계 및 주행 상황에 따른 영역 확장에 반영된다. 또한, 자율 주행 차량의 실질적인 안전을 보장하기 위해 ROI 설계에서 자율 주행 제어를 위한 거동 계획 결과가 고려된다. 보다 넓은 주변 영역에 대한 환경 정보를 확보하기 위해 라이다 데이터는 설계된 ROI별로 분류되며, 영역별 중요도에 따라 연산 과정이 분리되어 수행된다. 목표 시스템을 구성하는 모듈 별 연산 시간이 측정된 데이터 기반으로 통계적으로 분석된다. 운전자의 반응 시간, 산업 표준, 대상 하드웨어 사양 및 센서 성능을 기반으로 결정된 시스템 성능 조건을 고려하여, 안전성을 확보하기 위한 자율 주행 시스템의 적절한 샘플링 주기가 정의된다. 데이터 기반 다중 선형 회귀 분석은 인식 모듈을 구성하는 함수 별 실행 시간을 예측하기 위해 적용되며, 안정적인 실시간 성능을 보장하기 위해 적응형 ROI를 기반으로 자율 주행 안전에 필요한 데이터를 선택적으로 분류하여 연산 부하가 감축된다. 연산 부하 평가 관리에서 환경 인지 모듈과 전체 시스템의 연산 부하가 대상 환경에서의 적절성을 평가하고, 연산 부하 관리에 문제가 있을 때 자율 주행 차량의 거동을 제한하여 시스템 안정성을 유지함으로써 차량 안전성을 확보한다. 제안된 자율주행 인지 전략 및 알고리즘의 성능은 데이터 기반 시뮬레이션 및 실차 실험을 통해 검증되었다. 실험 결과를 통해 제안된 환경 인식 알고리즘은 자율 주행 시스템을 구성하는 모듈 간의 상호 작용을 고려하여 도심 도로 환경에서 자율 주행 차량의 안전성과 시스템의 안정적인 성능을 보장할 수 있음을 확인하였다.Since annually 1.2 million people die from car crashes worldwide, discussions about fundamental preventive measures for traffic accidents are taking place. According to the statistical survey, 94 percent of all traffic accidents are caused by human error. From the perspective of securing road safety, automated driving technology became interesting as a way to solve this serious problem, and its commercialization was considered through a step-by-step application through research and development. Major carmakers already have developed and commercialized advanced driver assistance systems (ADAS), such as lane keeping assistance system (LKAS), adaptive cruise control (ACC), parking assistance system (PAS), automated emergency braking (AEB), and so on. Furthermore, partially automated driving systems are being installed in vehicles and released by carmakers. Audi AI Traffic Jam Pilot (Audi), Autopilot (Tesla), Distronic Plus (Mercedes-Benz), Highway Driving Assist (Hyundai Motor Company), and Driving Assistant Plus (BMW) are typical released examples of the partially automated driving system. These released partially automated driving systems are still must be accompanied by driver attention. Nevertheless, it is proving to be effective in significantly improving safety. In recent years, several automated driving accidents have occurred, and the frequency is rapidly increasing and attracting social attention. Since vehicle accidents are directly related to human casualty, accidents of automated vehicles cause social insecurity by causing a decrease in the reliability of automated driving technology. Due to recent automated driving-related accidents, the safety of the automated vehicle has been emphasized more. Therefore, in this study, we propose an approach to secure vehicle safety in terms of the entire system in consideration of the behavior control of the automated driving vehicle. In addition, the development of automated driving is not merely a replacement technology for driving, but it is expected to have an industrial assembly as integration of high technology. Currently, automated driving systems have been extended from the conventional framework of the existing automotive industry, and are being developed in various fields. Since automated driving is composed of a complex combination of various technologies, development is currently underway in various conditions and has not been standardized yet. Most developments tend to pursue local performance improvement in each module unit, and the overall system unit approaches considering the relationship between component modules is insufficient. Local research and development at the submodule level can be challenging to achieve adequate performance from a system-level due to the effects of module interaction in terms of system integration perspective. The one-way approach that considers only the performance of each module has its limitations. To overcome this problem, it is necessary to consider the characteristics of the modules involved. This dissertation focuses on developing an efficient environment perception algorithm by considering the interaction between configured modules in terms of entire system operation to secure the stable and high performance of an automated driving system. In order to perform effective information processing and secure vehicle safety from a practical perspective, we propose an adaptive ROI based computational load management strategy. The motion characteristics of the subject vehicle, road design standards, and driving tasks of the surrounding vehicles, such as overtaking, and lane change, are reflected in the design of adaptive ROI, and the expansion of the area according to the driving task is considered. Additionally, motion planning results for automated driving are considered in the ROI design in order to guarantee the practical safety of the automated vehicle. In order to secure reasonable and appropriate environment information for the wider areas, lidar sensor data is classified by the designed ROI, and separated processing is conducted according to area importance. Based on the driving data, the calculation time of each module constituting the target system is statistically analyzed. In consideration of the system performance constraint determined by using human reaction time and industry standards, target hardware specification and the performance of sensor, the appropriate sampling time for automated driving system is defined to enhance safety. The data-based multiple linear regression is applied to predict the computation time by each function constituting perception module, and the computational load reduction is applied sequentially by selecting the data essential for automated driving safety based on adaptive ROI to secure the stable real-time execution performance of the system. In computational load assessment, it evaluates whether the computational load of the environmental perception module and entire system are appropriate and restricts the vehicle behavior when there is a problem in the computational load management to ensure vehicle safety by maintaining system stability. The performance of the proposed strategy and algorithms is evaluated through driving data-based simulation and actual vehicle tests. Test results show that the proposed environment recognition algorithm, which considers the interactions between the modules that make up the automated driving system, guarantees the safety of automated vehicle and reliable performance of system in an urban environment scenario.Chapter 1 Introduction 1 1.1. Background and Motivation 1 1.2. Previous Researches 6 1.3. Thesis Objectives 11 1.4. Thesis Outline 13 Chapter 2 Overall Architecture 14 2.1. Automated Driving Architecture 14 2.2. Test Vehicle Configuration 19 Chapter 3 Design of Adaptive ROI and Processing 21 3.1. ROI Definition 25 3.1.1. ROI Design for Normal Driving Condition 30 3.1.2. ROI Design for Lane Change 50 3.1.3. ROI Design for Intersection 56 3.2. Data Processing based on Adaptive ROI 62 3.2.1. Point Cloud Categorization by Adaptive ROI 63 3.2.2. Separated Voxelization 66 3.2.3. Separated Clustering 70 Chapter 4 Environment Perception Algorithm for Automated Driving 75 4.1. Time Delay Compensation of Environment Sensor 77 4.1.1. Algorithm Structure of Time Delay Estimation and Compensation 78 4.1.2. Time Delay Compensation Algorithm 79 4.1.3. Analysis of Processing Delay 84 4.1.4. Test Data based Open-loop Simulation 91 4.2. Environment Representation 96 4.2.1. Static Obstacle Map Construction 98 4.2.2. Lane and Road Boundary Detection 100 4.3. Multiple Object State Estimation and Tracking based on Geometric Model-Free Approach 107 4.3.1. Prediction of Geometric Model-Free Approach 109 4.3.2. Track Management 111 4.3.3. Measurement Update 112 4.3.4. Performance Evaluation via vehicle test 114 Chapter 5 Computational Load Management 117 5.1. Processing Time Analysis of Driving Data 121 5.2. Processing Time Estimation based on Multiple Linear Regression 128 5.2.1. Clustering Processing Time Estimation 129 5.2.2. Multi Object Tracking (MOT) Processing Time Estimation 138 5.2.3. Validation through Data-based Simulation 146 5.3. Computational Load Management 149 5.3.1. Sequential Processing to Computation Load Reduction 151 5.3.2. Restriction of Driving Control 154 5.3.3. Validation through Data-based Simulation 159 Chapter 6 Vehicle Tests based Performance Evaluation 163 6.1. Test-data based Simulation 164 6.2. Vehicle Tests: Urban Automated Driving 171 6.2.1. Test Configuration 171 6.2.2. Motion Planning and Vehicle Control 172 6.2.3. Vehicle Tests Results 174 Chapter 7 Conclusions and Future Works 184 Bibliography 188 Abstract in Korean 200Docto

실시간 자율주행 인지 시스템을 위한 신경 네트워크와 군집화 기반 미학습 물체 감지기 통합

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 기계항공공학부, 2020. 8. 이경수.최근 몇 년간, 센서 기술의 발전과 컴퓨터 공학 분야의 성과들로 인하여 자율주행 연구가 더욱 활발해지고 있다. 자율주행 시스템에 있어서 차량 주변 환경을 인식하는 것은 안전 및 신뢰성 있는 주행을 하기 위해 필요한 가장 중요한 기능이다. 자율주행 시스템은 크게 인지, 판단, 제어로 구성되어 있는데, 인지 모듈은 자율주행 차량이 경로를 설정하고 판단, 제어를 함에 앞서 주변 물체의 위치와 움직임을 파악해야하기 때문에 중요한 정보를 제공한다. 자율주행 인지 모듈은 주행 환경을 파악하기 위해 다양한 센서가 사용된다. 그 중에서도 LiDAR은 현재 많은 자율주행 연구에서 가장 널리 사용되는 센서 중 하나로, 물체의 거리 정보 획득에 있어서 매우 유용하다. 본 논문에서는 LiDAR에서 생성되는 포인트 클라우드 raw 데이터를 활용하여 장애물의 3D 정보를 파악하고 이들을 추적하는 인지 모듈을 제안한다. 인지 모듈의 전체 프레임워크는 크게 세 단계로 구성된다. 1단계는 비지면 포인트 추정을 위한 마스크 생성, 2단계는 특징 추출 및 장애물 감지, 3단계는 장애물 추적으로 구성된다. 현재 대부분의 신경망 기반의 물체 탐지기는 지도학습을 통해 학습된다. 그러나 지도학습 기반 장애물 탐지기는 학습한 장애물을 찾는다는 방법론적 한계를 지니고 있다. 그러나 실제 주행상황에서는 미처 학습하지 못한 물체를 마주하거나 심지어 학습한 물체도 놓칠 수 있다. 인지 모듈의 1단계에서 이러한 지도학습의 방법론적 한계에 대처하기 위해 포인트 클라우드를 일정한 간격으로 구성된 3D 복셀(voxel)로 분할하고, 이로부터 비접지점들을 추출한 뒤 미지의 물체(Unknown object)를 탐지한다. 2단계에서는 각 복셀의 특성을 추출 및 학습하고 네트워크를 학습시킴으로써 객체 감지기를 구성한다. 마지막 3단계에서는 칼만 필터와 헝가리안 알고리즘을 활용한 다중 객체 탐지기를 제안한다. 이렇게 구성된 인지 모듈은 비지면 점들을 추출하여 학습하지 않은 물체에 대해서도 미지의 물체(Unknown object)로 감지하여 실시간으로 장애물 탐지기를 보완한다. 최근 라이다를 활용한 자율주행 용 객체 탐지기에 대한 연구가 활발히 진행되고 있으나 대부분의 연구들은 단일 프레임의 물체 인식에 대해 집중하여 정확도를 올리는 데 집중하고 있다. 그러나 이러한 연구는 감지 중요도와 프레임 간의 감지 연속성 등에 대한 고려가 되어있지 않다는 한계점이 존재한다. 본 논문에서는 실시간 성능을 얻기 위해 이러한 부분을 고려한 성능 지수를 제안하고, 실차 실험을 통해 제안한 인지 모듈을 테스트, 제안한 성능 지수를 통해 평가하였다.In recent few years, the interest in automotive researches on autonomous driving system has been grown up due to advances in sensing technologies and computer science. In the development of autonomous driving system, knowledge about the subject vehicles surroundings is the most essential function for safe and reliable driving. When it comes to making decisions and planning driving scenarios, to know the location and movements of surrounding objects and to distinguish whether an object is a car or pedestrian give valuable information to the autonomous driving system. In the autonomous driving system, various sensors are used to understand the surrounding environment. Since LiDAR gives the distance information of surround objects, it has been the one of the most commonly used sensors in the development of perception system. Despite achievement of the deep neural network research field, its application and research trends on 3D object detection using LiDAR point cloud tend to pursue higher accuracy without considering a practical application. A deep neural-network-based perception module heavily depends on the training dataset, but it is impossible to cover all the possibilities and corner cases. To apply the perception module in actual driving, it needs to detect unknown objects and unlearned objects, which may face on the road. To cope with these problems, in this dissertation, a perception module using LiDAR point cloud is proposed, and its performance is validated via real vehicle test. The whole framework is composed of three stages : stage-1 for the ground estimation playing as a mask for point filtering which are considered as non-ground and stage-2 for feature extraction and object detection, and stage-3 for object tracking. In the first stage, to cope with the methodological limit of supervised learning that only finds learned object, we divide a point cloud into equally spaced 3D voxels the point cloud and extract non-ground points and cluster the points to detect unknown objects. In the second stage, the voxelization is utilized to learn the characteristics of point clouds organized in vertical columns. The trained network can distinguish the object through the extracted features from point clouds. In non-maximum suppression process, we sort the predictions according to IoU between prediction and polygon to select a prediction close to the actual heading angle of the object. The last stage presents a 3D multiple object tracking solution. Through Kalman filter, the learned and unlearned objects next movement is predicted and this prediction updated by measurement detection. Through this process, the proposed object detector complements the detector based on supervised learning by detecting the unlearned object as an unknown object through non-ground point extraction. Recent researches on object detection for autonomous driving have been actively conducted, but recent works tend to focus more on the recognition of the objects at every single frame and developing accurate system. To obtain a real-time performance, this paper focuses on more practical aspects by propose a performance index considering detection priority and detection continuity. The performance of the proposed algorithm has been investigated via real-time vehicle test.Chapter 1 Introduction 1 1.1. Background and Motivation 1 1.2. Overview and Previous Researches 4 1.3. Thesis Objectives 12 1.4. Thesis Outline 14 Chapter 2 Overview of a Perception in Automated Driving 15 Chapter 3 Object Detector 18 3.1. Voxelization & Feature Extraction 22 3.2. Backbone Network 25 3.3. Detection Head & Loss Function Design 28 3.4. Loss Function Design 30 3.5. Data Augmentation 33 3.6. Post Process 39 Chapter 4 Non-Ground Point Clustering 42 4.1. Previous Researches for Ground Removal 44 4.2. Non-Ground Estimation using Voxelization 45 4.3. Non-ground Object Segmentation 50 4.3.1. Object Clustering 52 4.3.2. Bounding Polygon 55 Chapter 5 . Object Tracking 57 5.1. State Prediction and Update 58 5.2. Data Matching Association 60 Chapter 6 Test result for KITTI dataset 62 6.1. Quantitative Analysis 62 6.2. Qualitative Analysis 72 6.3. Additional Training 76 6.3.1. Additional data acquisition 78 6.3.2. Qualitative Analysis 81 Chapter 7 Performance Evaluation 85 7.1. Current Evaluation Metrics 85 7.2. Limitations of Evaluation Metrics 87 7.2.1. Detection Continuity 87 7.2.2. Detection Priority 89 7.3. Criteria for Performance Index 91 Chapter 8 Vehicle Tests based Performance Evaluation 95 8.1. Configuration of Vehicle Tests 95 8.2. Qualitative Analysis 100 8.3. Quantitative Analysis 105 Chapter 9 Conclusions and Future Works 107 Bibliography 109 국문 초록 114Docto