LiDAR aided simulation pipeline for wireless communication in vehicular traffic scenarios

Abstract. Integrated Sensing and Communication (ISAC) is a modern technology under development for Sixth Generation (6G) systems. This thesis focuses on creating a simulation pipeline for dynamic vehicular traffic scenarios and a novel approach to reducing wireless communication overhead with a Light Detection and Ranging (LiDAR) based system. The simulation pipeline can be used to generate data sets for numerous problems. Additionally, the developed error model for vehicle detection algorithms can be used to identify LiDAR performance with respect to different parameters like LiDAR height, range, and laser point density. LiDAR behavior on traffic environment is provided as part of the results in this study. A periodic beam index map is developed by capturing antenna azimuth and elevation angles, which denote maximum Reference Signal Receive Power (RSRP) for a simulated receiver grid on the road and classifying areas using Support Vector Machine (SVM) algorithm to reduce the number of Synchronization Signal Blocks (SSBs) that are needed to be sent in Vehicle to Infrastructure (V2I) communication. This approach effectively reduces the wireless communication overhead in V2I communication

Multi-Sensor Data Fusion for Robust Environment Reconstruction in Autonomous Vehicle Applications

In autonomous vehicle systems, understanding the surrounding environment is mandatory for an intelligent vehicle to make every decision of movement on the road. Knowledge about the neighboring environment enables the vehicle to detect moving objects, especially irregular events such as jaywalking, sudden lane change of the vehicle etc. to avoid collision. This local situation awareness mostly depends on the advanced sensors (e.g. camera, LIDAR, RADAR) added to the vehicle. The main focus of this work is to formulate a problem of reconstructing the vehicle environment using point cloud data from the LIDAR and RGB color images from the camera. Based on a widely used point cloud registration tool such as iterated closest point (ICP), an expectation-maximization (EM)-ICP technique has been proposed to automatically mosaic multiple point cloud sets into a larger one. Motion trajectories of the moving objects are analyzed to address the issue of irregularity detection. Another contribution of this work is the utilization of fusion of color information (from RGB color images captured by the camera) with the three-dimensional point cloud data for better representation of the environment. For better understanding of the surrounding environment, histogram of oriented gradient (HOG) based techniques are exploited to detect pedestrians and vehicles.;Using both camera and LIDAR, an autonomous vehicle can gather information and reconstruct the map of the surrounding environment up to a certain distance. Capability of communicating and cooperating among vehicles can improve the automated driving decisions by providing extended and more precise view of the surroundings. In this work, a transmission power control algorithm is studied along with the adaptive content control algorithm to achieve a more accurate map of the vehicle environment. To exchange the local sensor data among the vehicles, an adaptive communication scheme is proposed that controls the lengths and the contents of the messages depending on the load of the communication channel. The exchange of this information can extend the tracking region of a vehicle beyond the area sensed by its own sensors. In this experiment, a combined effect of power control, and message length and content control algorithm is exploited to improve the map\u27s accuracy of the surroundings in a cooperative automated vehicle system

LiDAR Lateral Localisation Despite Challenging Occlusion from Traffic

This paper presents a system for improving the robustness of LiDAR lateral localisation systems. This is made possible by including detections of road boundaries which are invisible to the sensor (due to occlusion, e.g. traffic) but can be located by our Occluded Road Boundary Inference Deep Neural Network. We show an example application in which fusion of a camera stream is used to initialise the lateral localisation. We demonstrate over four driven forays through central Oxford - totalling 40 km of driving - a gain in performance that inferring of occluded road boundaries brings.Comment: accepted for publication at the IEEE/ION Position, Location and Navigation Symposium (PLANS) 202

Height Change Feature Based Free Space Detection

In the context of autonomous forklifts, ensuring non-collision during travel, pick, and place operations is crucial. To accomplish this, the forklift must be able to detect and locate areas of free space and potential obstacles in its environment. However, this is particularly challenging in highly dynamic environments, such as factory sites and production halls, due to numerous industrial trucks and workers moving throughout the area. In this paper, we present a novel method for free space detection, which consists of the following steps. We introduce a novel technique for surface normal estimation relying on spherical projected LiDAR data. Subsequently, we employ the estimated surface normals to detect free space. The presented method is a heuristic approach that does not require labeling and can ensure real-time application due to high processing speed. The effectiveness of the proposed method is demonstrated through its application to a real-world dataset obtained on a factory site both indoors and outdoors, and its evaluation on the Semantic KITTI dataset [2]. We achieved a mean Intersection over Union (mIoU) score of 50.90 % on the benchmark dataset, with a processing speed of 105 Hz. In addition, we evaluated our approach on our factory site dataset. Our method achieved a mIoU score of 63.30 % at 54 H

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

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 “base map”. 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

자율주행을 위한 V2X 기반 차량 CDN 설계

학위논문 (석사) -- 서울대학교 대학원 : 공학전문대학원 응용공학과, 2021. 2. 김성우.Recent technical innovation has driven the evolution of autonomous vehicles. To improve safety as well as on-road vehicular experience, vehicles should be connected with each other or to vehicular networks. Some specification groups, e.g., IEEE and 3GPP, have studied and released vehicular communication requirements and architecture. IEEEs Wireless Access in Vehicular Environment focuses on dedicated and short-range communication, while 3GPPs New radio V2X supports not only sidelink but also uplink communication. The 3GPP Release 16, which supports 5G New Radio, offers evolved functionalities such as network slice, Network Function Virtualization, and Software-Defined Networking. In this study, we define and design a vehicular network architecture compliant with 5G core networks. For localization of autonomous driving vehicles, a high-definition map needs to contain the context of trajectory . We also propose new methods by which autonomous vehicles can push and pull map content efficiently, without causing bottlenecks on the network core. We evaluate the performance of V2X and of the proposed caching policy via network simulations. Experimental results indicate that the proposed method improves the performance of vehicular content delivery in real-world road environments.최근들어 기술의 혁신은 자율주행 자동차의 발전을 가속화 하고 있다. 보다 높은 수준의 자율 주행을 구현하기 위해서, 차량은 네트워크를 통해 서로 연결되어 있어야 하고 차량의 안전과 편의성을 향상 시킬 수 있도록 정보를 공유 할 수 있어야 한다. 표준화 단체인 IEEE와 3GPP는 차량 통신 요구사항, 아키텍처를 연구하고 개정해왔다. IEEE가 전용 채널을 통한 근접 지역 통신에 초점을 맞추는 반면에, 3GPP의 New Radio V2X는 Sidelink 뿐만 아니라 Uplink 통신을 동시에 지원한다. 5G 통신을 지원하는 3GPP Release 16은 Network Slice, NFV, SDN과 같은 새로운 통신 기능들을 제공한다. 이 연구에서는 새롭게 정의된 5G Core Network Architecture를 바탕으로 차량 네트워크를 정의하고 설계하였다. 자율주행 자동차의 측위를 위해서, 고해상도 지도는 각 구성요소들의 의미와 속성을 자세하게 포함하고 있어야 한다. 우리는 이 연구에서 V2X 네트워크 상에 HD map을 중계할 수 있는 Edge Server를 제안 함으로써, 중앙에서 발생할 수 있는 병목현상을 줄이고 전송 Delay를 최소화한다. 또한 Edge의 컨텐츠를 등록하고 삭제하는 정책으로 기존의 LRU, LFU가 아닌 새로운 컨텐츠 교체 알고리즘을 제안하였다. 실제 주행 시험과 시뮬레이션을 통한 실험을 통해 전송 품질을 향상시켰으며, Edge 컨텐츠의 활용도를 높였다.I. Introduction 1 II. Related Works 6 2.1 V2X Standardization 6 2.1.1 IEEE WAVE 6 2.1.2 3GPP C-V2X 9 2.2 Geographic Contents 14 2.3 Vehicular Content Centric Network 17 III. System Modeling 20 3.1 NR-V2X Architecture Analysis 20 3.2 Caching Strategy for HD Map Acquisition 23 IV. Evaluation 30 4.1 Contents Replacement Strategy 30 4.2 V2X Characteristics 36 4.3 Edge Performance in Driving on the Road 38 4.4 Edge Performance on 3D Point Clouds Caching for Localization 44 V. Conclusion 47 Bibliography 49 Abstract 54Maste