Near-field Perception for Low-Speed Vehicle Automation using Surround-view Fisheye Cameras

Cameras are the primary sensor in automated driving systems. They provide high information density and are optimal for detecting road infrastructure cues laid out for human vision. Surround-view camera systems typically comprise of four fisheye cameras with 190{\deg}+ field of view covering the entire 360{\deg} around the vehicle focused on near-field sensing. They are the principal sensors for low-speed, high accuracy, and close-range sensing applications, such as automated parking, traffic jam assistance, and low-speed emergency braking. In this work, we provide a detailed survey of such vision systems, setting up the survey in the context of an architecture that can be decomposed into four modular components namely Recognition, Reconstruction, Relocalization, and Reorganization. We jointly call this the 4R Architecture. We discuss how each component accomplishes a specific aspect and provide a positional argument that they can be synergized to form a complete perception system for low-speed automation. We support this argument by presenting results from previous works and by presenting architecture proposals for such a system. Qualitative results are presented in the video at https://youtu.be/ae8bCOF77uY.Comment: Accepted for publication at IEEE Transactions on Intelligent Transportation System

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

Model Predictive Control System Design of a passenger car for Valet Parking Scenario

A recent expansion of passenger cars’ automated functions has led to increasingly challenging design problems for the engineers. Among this the development of Automated Valet Parking is the latest addition. The system represents the next evolution of automated system giving the vehicle greater autonomy: the efforts of most automotive OEMs go towards achieving market deployment of such automated function. To this end the focus of each OEM is on taking part to this competitive endeavor and succeed by developing a proprietary solution with the support of hardware and software suppliers. Within this framework the present work aims at developing an effective control strategy for the considered scenarios. In order to reach this goal a Model Predictive Control approach is employed taking advantage of previous works within the automotive OEM in the automated driving field. The control algorithm is developed in a Simulink® simulation according to the requirements of the application and tested; results show the control strategy successfully drives the vehicle on the predefined path

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

Autonomous Ground Vehicle Prototype via Steering-, Throttle-, and Brake-by Wire Modules

This MQP furthered previous developments working towards a vision-based autonomous ground vehicle. Through modular construction and engineering, all aspects of a self-driving vehicle were retrofitted onto our 1995 golf cart in conjunction with sensors, cameras, and computational power. This year the team improved upon the existing steering system, ruggedized the braking system, and automated the accelerator input. A graduate student partnered with our MQP team and provided software for the stereoscopic vision systems, image processing, and mobile path planning. This platform is now prepared for future teams to develop software, mobile applications, and use the autonomous vehicle for various user applications

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

Laser-Based Control Law For Autonomous Parallel And Perpendicular Parking

International audienceThis paper addresses the perpendicular and parallel parking problems of car-like vehicles for both forward and reverse maneuvers in one trial by extending the work presented in [1] using a multi sensor based controller with a weighted control scheme. The perception problem is discussed briefly considering a Velodyne VLP-16 and a SICK LMS151 as the sensors providing the required exteroceptive information. The results obtained from simulations and real experimentation for different parking scenarios show the validity and potential of the proposed approach. Furthermore, it is shown that, despite the need of handling several constraints for collision avoidance, the required computation time of the proposed approach is small enough to be used online

Midtown Ventura Wellness District Urban Design Concept Plan

This report resulted from an academic exercise by a team of graduate-level students from Cal Poly San Luis Obispo’s City and Regional Planning Department during the Spring Quarter, 2016. With the support and encouragement of the City of Buonaventura, the team proposed a development concept and an urban design concept plan for the Midtown that explore the notion of wellness in a holistic manner, and are consistent with the General Plan and the city’s economic strategy. This plan was motivated by the results of the 2005 Midtown Charrette and the 2013 technical assistance workshop by Urban Land Institute which suggested the potential for a special district around the concept of wellness. This idea was motivated by the expansion of two important hospital sites, the Community Memorial Hospital and the Ventura County Medical Center located at close proximity to each other. Totalling over $600 million dollars of investment in facilities and infrastructure, the redevelopment of both sites together with the concentration of medical-related uses around them generates an important community and economic hub in Midtown. Communities across the nation are recognizing the critical link between the built environment and public health. Consciously improving the physical design of communities has the potential to reverse downward trends in people’s overall health and life span. Designing and planning healthy communities is a process that involves bringing together a wide range of stakeholders who can incorporate community values and implement best practices to actualize them

The future of the urban street in the united states: visions of alternative mobilities in the twenty-first century

This dissertation is concerned with the present and future of urban streets in the United States. The goal is to document and analyze current visions, policies, and strategies related to the form and use of American urban streets. The dissertation examines current mobility trends and offers a framework for organizing visions of the future of urban streets, evaluating them through three lenses: safety, comfort, and delight: assessing physical conditions in accordance with livability standards toward sustainable development. At the same time, it demonstrates the way 12 scenarios (NACTO Blueprint for Autonomous Urbanism, Sidewalk Labs: Quayside Project, Public Square by FXCollaborative, AIANY Future Street, The National Complete Street Coalition, Vision Zero, Smart Columbus, Waymo by Alphabet, The Hyperloop, Tesla “Autopilot,” Ford City of Tomorrow, SOM City of Tomorrow) have intentionally or unintentionally influenced contemporary use of American urban streets. Ultimately, the study shows that while sustainable alternative mobilities continue to emerge, the dominance of the automobility system has led to a stagnation of sustainable urban street development in the United States