Sistemas de suporte à condução autónoma adequados a plataforma robótica 4-wheel skid-steer: percepção, movimento e simulação

As competições de robótica móvel desempenham papel preponderante na difusão da ciência e da engenharia ao público em geral. E também um espaço dedicado ao ensaio e comparação de diferentes estratégias e abordagens aos diversos desafios da robótica móvel. Uma das vertentes que tem reunido maior interesse nos promotores deste género de iniciativas e entre o público em geral são as competições de condução autónoma. Tipicamente as Competi¸c˜oes de Condução Autónoma (CCA) tentam reproduzir um ambiente semelhante a uma estrutura rodoviária tradicional, no qual sistemas autónomos deverão dar resposta a um conjunto variado de desafios que vão desde a deteção da faixa de rodagem `a interação com distintos elementos que compõem uma estrutura rodoviária típica, do planeamento trajetórias à localização. O objectivo desta dissertação de mestrado visa documentar o processo de desenho e concepção de uma plataforma robótica móvel do tipo 4-wheel skid-steer para realização de tarefas de condução autónoma em ambiente estruturado numa pista que pretende replicar uma via de circulação automóvel dotada de sinalética básica e alguns obstáculos. Paralelamente, a dissertação pretende também fazer uma análise qualitativa entre o processo de simulação e a sua transposição para uma plataforma robótica física. inferir sobre a diferenças de performance e de comportamento.Mobile robotics competitions play an important role in the diffusion of science and engineering to the general public. It is also a space dedicated to test and compare different strategies and approaches to several challenges of mobile robotics. One of the aspects that has attracted more the interest of promoters for this kind of initiatives and general public is the autonomous driving competitions. Typically, Autonomous Driving Competitions (CCAs) attempt to replicate an environment similar to a traditional road structure, in which autonomous systems should respond to a wide variety of challenges ranging from lane detection to interaction with distinct elements that exist in a typical road structure, from planning trajectories to location. The aim of this master’s thesis is to document the process of designing and endow a 4-wheel skid-steer mobile robotic platform to carry out autonomous driving tasks in a structured environment on a track that intends to replicate a motorized roadway including signs and obstacles. In parallel, the dissertation also intends to make a qualitative analysis between the simulation process and the transposition of the developed algorithm to a physical robotic platform, analysing the differences in performance and behavior

An Online Learning System for Wireless Charging Alignment using Surround-view Fisheye Cameras

Electric Vehicles are increasingly common, with inductive chargepads being considered a convenient and efficient means of charging electric vehicles. However, drivers are typically poor at aligning the vehicle to the necessary accuracy for efficient inductive charging, making the automated alignment of the two charging plates desirable. In parallel to the electrification of the vehicular fleet, automated parking systems that make use of surround-view camera systems are becoming increasingly popular. In this work, we propose a system based on the surround-view camera architecture to detect, localize, and automatically align the vehicle with the inductive chargepad. The visual design of the chargepads is not standardized and not necessarily known beforehand. Therefore, a system that relies on offline training will fail in some situations. Thus, we propose a self-supervised online learning method that leverages the driver's actions when manually aligning the vehicle with the chargepad and combine it with weak supervision from semantic segmentation and depth to learn a classifier to auto-annotate the chargepad in the video for further training. In this way, when faced with a previously unseen chargepad, the driver needs only manually align the vehicle a single time. As the chargepad is flat on the ground, it is not easy to detect it from a distance. Thus, we propose using a Visual SLAM pipeline to learn landmarks relative to the chargepad to enable alignment from a greater range. We demonstrate the working system on an automated vehicle as illustrated in the video at https://youtu.be/_cLCmkW4UYo. To encourage further research, we will share a chargepad dataset used in this work.Comment: Accepted for publication at IEEE Transactions on Intelligent Transportation System

An Overview about Emerging Technologies of Autonomous Driving

Since DARPA started Grand Challenges in 2004 and Urban Challenges in 2007, autonomous driving has been the most active field of AI applications. This paper gives an overview about technical aspects of autonomous driving technologies and open problems. We investigate the major fields of self-driving systems, such as perception, mapping and localization, prediction, planning and control, simulation, V2X and safety etc. Especially we elaborate on all these issues in a framework of data closed loop, a popular platform to solve the long tailed autonomous driving problems

A Study of V2V Communication on VANET: Characteristic, Challenges and Research Trends

Vehicle to Vehicle (V2V) communication is a specific type of communication on Vehicular Ad Hoc Network (VANET)  that attracts the great interest of researchers, industries, and government attention in due to its essential application to improve safety driving purposes for the next generation of vehicles. Our paper is a systematic study of V2V communication in VANET that cover the particular research issue, and trends from the recent works of literature. We begin the article with a brief V2V communication concept and the V2V application to safety purposes and non-safety purposes; then, we analyze several problems of V2V communication for VANET related to safety issues and non-safety issues. Next, we provide the trends of the V2V communication application for VANET. Finally, provide SWOT analysis as a discussion to identify opportunities and challenges of V2V communication for VANET in the future. The paper does not include a technical explanation. Still, the article describes the general perspective of VANET to the reader, especially for the beginner reader, who intends to learn about the topic

“Deep sensor fusion architecture for point-cloud semantic segmentation”

Este trabajo de grado desarrolla un completo abordaje del analisis de datos y su procesamiento para obtener una mejor toma de decisiones, presentando así una arquitectura neuronal multimodal basada CNN, comprende explicaciones precisas de los sistemas que integra y realiza una evaluacion del comportamiento en el entorno.Los sistemas de conducción autónoma integran procedimientos realmente complejos, para los cuales la percepción del entorno del vehículo es una fuente de información clave para tomar decisiones durante maniobras en tiempo real. La segmentación semántica de los datos obtenidos de los sensores LiDAR ha desempeñado un papel importante en la consolidación de una representación densa de los objetos y eventos circundantes. Aunque se han hecho grandes avances para resolver esta tarea, creemos que hay una infrautilización de estrategias que aprovechas la fusión de sensores. Presentamos una arquitectura neuronal multimodal, basada en CNNs que es alimentada por las señales de entrada 2D del LiDAR y de la cámara, computa una representación profunda de ambos sensores, y predice un mapeo de etiquetas para el problema de segmentación de puntos en 3D. Evaluamos la arquitectura propuesta en un conjunto de datos derivados del popular dataset KITTI, que contempla clases semánticas comunes ( coche, peatón y ciclista). Nuestro modelo supera a los métodos existentes y muestra una mejora en el refinamiento de las máscaras de segmentación.Self-driving systems are composed by really complex pipelines in which perceiving the vehicle surroundings is a key source of information used to take real-time maneuver decisions. Semantic segmentation on LiDAR sensor data has played a big role in the consolidation of a dense understanding of the surrounding objects and events. Although great advances have been made for this task, we believe there is an under-exploitation of sensor fusion strategies. We present a multimodal neural architecture, based on CNNs that consumes 2D input signals from LiDAR and camera, computes a deep representation leveraging straightness from both sensors, and predicts a label mapping for the 3D point-wise segmentation problem. We evaluated the proposed architecture in a derived dataset from the KITTI vision benchmark suite which contemplates common semantic classes(i.e. car, pedestrian and cyclist). Our model outperforms existing methods and shows improvement in the segmentation masks refinement.MaestríaMagíster en Ingeniería de Sistemas y ComputaciónTable of Contents Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1 Problem statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.2 Goals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.3 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.4 Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2 Autonomous vehicle perception systems . . . . . . . . . . . . . . . . . . . . 6 2.1 Semantic segmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.2 Autonomous vehicles sensing . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.2.1 Camera . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.2.2 LiDAR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.2.3 Radar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.2.4 Ultrasonic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.3 Point clouds semantic segmentation . . . . . . . . . . . . . . . . . . . . . . . 12 2.3.1 Raw pointcloud . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 2.3.2 Voxelization of pointclouds . . . . . . . . . . . . . . . . . . . . . . . . . . 15 2.3.3 Point cloud projections . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 2.3.4 Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 3 Deep multimodal learning for semantic segmentation . . . . . . . . . . . . . 19 3.1 Method overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 3.2 Point cloud transformation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 3.3 Multimodal fusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 3.3.1 RGB modality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 3.3.2 LiDAR modality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 3.3.3 Fusion step . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 3.3.4 Decoding part . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 3.3.5 Optimization statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 4 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 4.1 KITTI dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 4.2 Evaluation metric . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 4.3 Experimental setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 4.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 4.4.1 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

Pre-Trained Driving in Localized Surroundings with Semantic Radar Information and Machine Learning

Entlang der Signalverarbeitungskette von Radar Detektionen bis zur Fahrzeugansteuerung, diskutiert diese Arbeit eine semantischen Radar Segmentierung, einen darauf aufbauenden Radar SLAM, sowie eine im Verbund realisierte autonome Parkfunktion. Die Radarsegmentierung der (statischen) Umgebung wird durch ein Radar-spezifisches neuronales Netzwerk RadarNet erreicht. Diese Segmentierung ermöglicht die Entwicklung des semantischen Radar Graph-SLAM SERALOC. Auf der Grundlage der semantischen Radar SLAM Karte wird eine beispielhafte autonome Parkfunktionalität in einem realen Versuchsträger umgesetzt. Entlang eines aufgezeichneten Referenzfades parkt die Funktion ausschließlich auf Basis der Radar Wahrnehmung mit bisher unerreichter Positioniergenauigkeit. Im ersten Schritt wird ein Datensatz von 8.2 · 10^6 punktweise semantisch gelabelten Radarpunktwolken über eine Strecke von 2507.35m generiert. Es sind keine vergleichbaren Datensätze dieser Annotationsebene und Radarspezifikation öffentlich verfügbar. Das überwachte Training der semantischen Segmentierung RadarNet erreicht 28.97% mIoU auf sechs Klassen. Außerdem wird ein automatisiertes Radar-Labeling-Framework SeRaLF vorgestellt, welches das Radarlabeling multimodal mittels Referenzkameras und LiDAR unterstützt. Für die kohärente Kartierung wird ein Radarsignal-Vorfilter auf der Grundlage einer Aktivierungskarte entworfen, welcher Rauschen und andere dynamische Mehrwegreflektionen unterdrückt. Ein speziell für Radar angepasstes Graph-SLAM-Frontend mit Radar-Odometrie Kanten zwischen Teil-Karten und semantisch separater NDT Registrierung setzt die vorgefilterten semantischen Radarscans zu einer konsistenten metrischen Karte zusammen. Die Kartierungsgenauigkeit und die Datenassoziation werden somit erhöht und der erste semantische Radar Graph-SLAM für beliebige statische Umgebungen realisiert. Integriert in ein reales Testfahrzeug, wird das Zusammenspiel der live RadarNet Segmentierung und des semantischen Radar Graph-SLAM anhand einer rein Radar-basierten autonomen Parkfunktionalität evaluiert. Im Durchschnitt über 42 autonome Parkmanöver (∅3.73 km/h) bei durchschnittlicher Manöverlänge von ∅172.75m wird ein Median absoluter Posenfehler von 0.235m und End-Posenfehler von 0.2443m erreicht, der vergleichbare Radar-Lokalisierungsergebnisse um ≈ 50% übertrifft. Die Kartengenauigkeit von veränderlichen, neukartierten Orten über eine Kartierungsdistanz von ∅165m ergibt eine ≈ 56%-ige Kartenkonsistenz bei einer Abweichung von ∅0.163m. Für das autonome Parken wurde ein gegebener Trajektorienplaner und Regleransatz verwendet

ROS2 versus AUTOSAR: automated PARKING system case-study

Vehicles are complex systems as they combine several engineering disciplines, such as mechanical, electric, electronic, software and telecommunication. In the last decades, most innovations in the automotive domain have been achieved as a combination of electronics and software. Consequently, the software development and deployment has resulted a highly sophisticated engineering process to manage and to integrate. With the introduction of artificial intelligence, automated driving has become a reality. However it has additionally increased the requirements on the system design. One widely accepted approach to manage complexity is to divide the system into subsystems through a well-defined architecture. The architecture of an autonomous system must be suitable to guarantee that the self-driving functionality remains safe in a broad range of operational domains. The challenge is how to design the architecture of the system to be reliable and resilient to changing context. The automotive industry has well established standards and development practices, but it is open to explore and integrate solutions from other domains like Internet of Things and Robotics. In the area of autonomous systems, the capabilities of the robotics middleware ROS2 have been used for prototyping purposes. It is an open question whether ROS2 is suitable for automotive safety relevant applications. This master thesis addresses this challenge through evaluating the possible application of ROS2 in the automotive domain. The development consists of implementing an architecture for an autonomous driving function case-study, an Automated Parking System, which adapts to its context by switching between different operational modes. The Automated Parking System has been implemented and validated in a simulation environment. The experiment results show which benefits bring ROS2 compared with the automotive standardised architecture AUTOSAR