A Fine-Grained Dataset and its Efficient Semantic Segmentation for Unstructured Driving Scenarios

Research in autonomous driving for unstructured environments suffers from a lack of semantically labeled datasets compared to its urban counterpart. Urban and unstructured outdoor environments are challenging due to the varying lighting and weather conditions during a day and across seasons. In this paper, we introduce TAS500, a novel semantic segmentation dataset for autonomous driving in unstructured environments. TAS500 offers fine-grained vegetation and terrain classes to learn drivable surfaces and natural obstacles in outdoor scenes effectively. We evaluate the performance of modern semantic segmentation models with an additional focus on their efficiency. Our experiments demonstrate the advantages of fine-grained semantic classes to improve the overall prediction accuracy, especially along the class boundaries. The dataset and pretrained model are available at mucar3.de/icpr2020-tas500.Comment: Accepted at International Conference on Pattern Recognition 2020 (ICPR). For the associated project page, see https://www.mucar3.de/icpr2020-tas500/index.htm

The GOOSE Dataset for Perception in Unstructured Environments

The potential for deploying autonomous systems can be significantly increased by improving the perception and interpretation of the environment. However, the development of deep learning-based techniques for autonomous systems in unstructured outdoor environments poses challenges due to limited data availability for training and testing. To address this gap, we present the German Outdoor and Offroad Dataset (GOOSE), a comprehensive dataset specifically designed for unstructured outdoor environments. The GOOSE dataset incorporates 10 000 labeled pairs of images and point clouds, which are utilized to train a range of state-of-the-art segmentation models on both image and point cloud data. We open source the dataset, along with an ontology for unstructured terrain, as well as dataset standards and guidelines. This initiative aims to establish a common framework, enabling the seamless inclusion of existing datasets and a fast way to enhance the perception capabilities of various robots operating in unstructured environments. The dataset, pre-trained models for offroad perception, and additional documentation can be found at https://goose-dataset.de/.Comment: Preprint; Submitted to IEEE for revie

Proceedings of the 9th Conference on Autonomous Robot Systems and Competitions

Welcome to ROBOTICA 2009. This is the 9th edition of the conference on Autonomous Robot Systems and Competitions, the third time with IEEE‐Robotics and Automation Society Technical Co‐Sponsorship. Previous editions were held since 2001 in Guimarães, Aveiro, Porto, Lisboa, Coimbra and Algarve. ROBOTICA 2009 is held on the 7th May, 2009, in Castelo Branco , Portugal. ROBOTICA has received 32 paper submissions, from 10 countries, in South America, Asia and Europe. To evaluate each submission, three reviews by paper were performed by the international program committee. 23 papers were published in the proceedings and presented at the conference. Of these, 14 papers were selected for oral presentation and 9 papers were selected for poster presentation. The global acceptance ratio was 72%. After the conference, eighth papers will be published in the Portuguese journal Robótica, and the best student paper will be published in IEEE Multidisciplinary Engineering Education Magazine. Three prizes will be awarded in the conference for: the best conference paper, the best student paper and the best presentation. The last two, sponsored by the IEEE Education Society ‐ Student Activities Committee. We would like to express our thanks to all participants. First of all to the authors, whose quality work is the essence of this conference. Next, to all the members of the international program committee and reviewers, who helped us with their expertise and valuable time. We would also like to deeply thank the invited speaker, Jean Paul Laumond, LAAS‐CNRS France, for their excellent contribution in the field of humanoid robots. Finally, a word of appreciation for the hard work of the secretariat and volunteers. Our deep gratitude goes to the Scientific Organisations that kindly agreed to sponsor the Conference, and made it come true. We look forward to seeing more results of R&D work on Robotics at ROBOTICA 2010, somewhere in Portugal

Progress toward multi‐robot reconnaissance and the MAGIC 2010 competition

Tasks like search‐and‐rescue and urban reconnaissance benefit from large numbers of robots working together, but high levels of autonomy are needed to reduce operator requirements to practical levels. Reducing the reliance of such systems on human operators presents a number of technical challenges, including automatic task allocation, global state and map estimation, robot perception, path planning, communications, and human‐robot interfaces. This paper describes our 14‐robot team, which won the MAGIC 2010 competition. It was designed to perform urban reconnaissance missions. In the paper, we describe a variety of autonomous systems that require minimal human effort to control a large number of autonomously exploring robots. Maintaining a consistent global map, which is essential for autonomous planning and for giving humans situational awareness, required the development of fast loop‐closing, map optimization, and communications algorithms. Key to our approach was a decoupled centralized planning architecture that allowed individual robots to execute tasks myopically, but whose behavior was coordinated centrally. We will describe technical contributions throughout our system that played a significant role in its performance. We will also present results from our system both from the competition and from subsequent quantitative evaluations, pointing out areas in which the system performed well and where interesting research problems remain. © 2012 Wiley Periodicals, Inc.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/93532/1/21426_ftp.pd

Visual Navigation of the Vehicle

Práca pozostáva z rešerše známych riešení navigácie autonómne riadeného vozidla. V práci sú hlbšie popísané možnosti riadenia takéhoto vozidla, a využitie snímačov v autonómne riadených vozidlách. Z rôznych typov snímačov sú vybrané dva typy, ktoré sú najvhodnejšie pre vizuálnu navigáciu vozidla. V práci je 􀄇alej hlbšie popísaná funkcia a spôsob využitia týchto dvoch typov snímačov pri vizuálnej navigácii vozidla. Súčas􀄢ou práce je taktiež program, schopný získa􀄢 a uloži􀄢 dáta z vybraných snímačov.The thesis consists of retrieval of known solutions of autonomous vehicle navigation. Thesis further describes options for controling autonomous vehicle, and using of sensors in autonomous vehicles. From different types of sensors are selected two types, that are most suitable for visual navigation of the vehicle. Thesis describes the function and way of using these two types of sensors in the visual navigation of the vehicle. The program for obtaining and saving data from selected sensors is also part of the thesis.

Vehículos autónomos

Vivimos en una etapa de constante desarrollo tecnológico, y aunque no es el único, uno de los desafíos que más preocupan a las ciudades del siglo XXI es el tráfico. En este trabajo fin de grado estudiaremos una de las posibles soluciones a este problema, la conducción autónoma. Para ello, realizaremos una puesta al día sobre las diferentes etapas que han existido sobre el tema, tratando desde los antecedentes hasta los futuros desarrollos, abordando también las diferentes investigaciones existentes o los sistemas de ayudas a la conducción disponibles.Departamento de Ingeniería EléctricaGrado en Ingeniería Eléctric

Object-level fusion for surround environment perception in automated driving applications

Driver assistance systems have increasingly relied on more sensors for new functions. As advanced driver assistance system continue to improve towards automated driving, new methods are required for processing the data in an efficient and economical manner from the sensors for such complex systems. The detection of dynamic objects is one of the most important aspects required by advanced driver assistance systems and automated driving. In this thesis, an environment model approach for the detection of dynamic objects is presented in order to realize an effective method for sensor data fusion. A scalable high-level fusion architecture is developed for fusing object data from several sensors in a single system, where processing occurs in three levels: sensor, fusion and application. A complete and consistent object model which includes the object’s dynamic state, existence probability and classification is defined as a sensor-independent and generic interface for sensor data fusion across all three processing levels. Novel algorithms are developed for object data association and fusion at the fusion-level of the architecture. An asynchronous sensor-to-global fusion strategy is applied in order to process sensor data immediately within the high-level fusion architecture, giving driver assistance systems the most up-to-date information about the vehicle’s environment. Track-to-track fusion algorithms are uniquely applied for dynamic state fusion, where the information matrix fusion algorithm produces results comparable to a low-level central Kalman filter approach. The existence probability of an object is fused using a novel approach based on the Dempster-Shafer evidence theory, where the individual sensor’s existence estimation performance is considered during the fusion process. A similar novel approach with the Dempster-Shafer evidence theory is also applied to the fusion of an object’s classification. The developed high-level sensor data fusion architecture and its algorithms are evaluated using a prototype vehicle equipped with 12 sensors for surround environment perception. A thorough evaluation of the complete object model is performed on a closed test track using vehicles equipped with hardware for generating an accurate ground truth. Existence and classification performance is evaluated using labeled data sets from real traffic scenarios. The evaluation demonstrates the accuracy and effectiveness of the proposed sensor data fusion approach. The work presented in this thesis has additionally been extensively used in several research projects as the dynamic object detection platform for automated driving applications on highways in real traffic

Υποστήριξη της διδακτικής διαδικασίας του μαθήματος «Εφαρμογές Πληροφορικής»

Στόχος της παρούσας πτυχιακής εργασίας είναι η υποστήριξη της διδακτικής και μαθησιακής διαδικασίας του μαθήματος επιλογής «Εφαρμογές Πληροφορικής», που διδάσκεται στην Α΄ τάξη του Γενικού Λυκείου. Στο πλαίσιο της συγκεκριμένης πτυχιακής σχεδιάσαμε δραστηριότητες και φύλλα εργασίας. Με τα φύλλα αυτά εργασίας υποστηρίξαμε το μάθημα «Εφαρμογές Πληροφορικής» το οποίο διεξήχθη στο Πρότυπο ΓΕΛ της Βαρβακείου Σχολής, με υπεύθυνο καθηγητή τον κ. Βεργίνη Ηλία όπου παρακολουθήσαμε και υποστηρίξαμε και τα αντίστοιχα εβδομαδιαία εργαστήρια. Η δομή της παρούσας πτυχιακής είναι η παρακάτω: Στο πρώτο κεφάλαιο, παρουσιάζεται το πρόγραμμα σπουδών του μαθήματος καθώς και οι ιδιαίτερες απαιτήσεις και οι στόχοι του. Στο δεύτερο κεφάλαιο παρουσιάζονται ιστορικά στοιχεία για το πρόγραμμα Alice και γίνεται αναφορά σε ορισμένες έννοιες του αντικειμενοστραφούς προγραμματισμού. Επιπλέον παρουσιάζονται δραστηριότητες και φύλλα εργασίας, τα οποία χρησιμοποιήθηκαν με στόχο να μάθουν οι μαθητές εύκολα και ευχάριστα αντικειμενοστραφή προγραμματισμό. Στο τρίτο κεφάλαιο, παρουσιάζεται μία εισαγωγή στα περιβάλλοντα Pivot και Game Maker, τα οποία δεν αποτελούν μέρος του προγράμματος σπουδών, καθώς και δραστηριότητες οι οποίες βοήθησαν στην κατανόηση της λειτουργίας του εκάστοτε περιβάλλοντος. Στο τέταρτο κεφάλαιο, παρατίθεται μια εισαγωγή στην οποία γίνεται μία αναδρομή της εξέλιξης του προγράμματος Appinventor. Όμοια με το δεύτερο κεφάλαιο, σχεδιάσαμε φύλλα εργασίας, με σκοπό την εξοικείωση των μαθητών με την ανάπτυξη εφαρμογών. Ακόμη, στο πέμπτο κεφάλαιο, γίνεται μια σύνοψη της παρούσας πτυχιακής εργασίας και παρατίθενται ορισμένα συμπεράσματα που προέκυψαν μετά από την ενασχόληση των μαθητών με τα προαναφερθέντα περιβάλλοντα. Τέλος, το ΠΑΡΑΡΤΗΜΑ Ι περιλαμβάνει μία αξιολόγηση των μαθητών σχετικά με το περιβάλλον Alice αλλά και τον τρόπο διδασκαλίας του, ενώ στα ΠΑΡΑΡΤΗΜΑΤΑ ΙΙ και ΙΙΙ παρουσιάζονται αντίστοιχα οι εργασίες των μαθητών και οι χριστουγεννιάτικες κάρτες που δημιούργησαν.The aim of this BA thesis, is to support the educational process of the High School first grade optional course “IT Applications”. Within this BA thesis, we designed activities and worksheets. With these worksheets, we supported the course " IT Applications", which was conducted in the Varvakio High School, with Professor Verginis Ilias, where we attended and supported the weekly lessons. The structure of this BA thesis is the following: The first chapter, presents the course curriculum and its specific requirements and objectives. The second chapter provides some historical information about the Alice educational environment. Furthermore, certain concepts of the object-oriented programming are presented. We designed activities and worksheets so that students can learn easily and pleasantly object-oriented programming. The third chapter presents an introduction to the Pivot and Game Maker environments, which are not part of the curriculum, in addition we designed activities that helped students in understanding the function of each one. In the fourth section there is an introduction to the Appinventor program development. Similar to the second chapter, we designed worksheets, in order to familiarize students with the development of applications. Also, the fifth chapter provides some conclusions about the abovementioned environments. Finally, ANNEX I contains a students’ evaluation on Alice environment and their opinion on the way of teaching, whereas ANNEX II and III present students’ work on Alice environment and some Christmas cards which they created

Real-time motion planning methods for autonomous on-road driving: State-of-the-art and future research directions

Open access articleCurrently autonomous or self-driving vehicles are at the heart of academia and industry research because of its multi-faceted advantages that includes improved safety, reduced congestion,lower emissions and greater mobility. Software is the key driving factor underpinning autonomy within which planning algorithms that are responsible for mission-critical decision making hold a significant position. While transporting passengers or goods from a given origin to a given destination, motion planning methods incorporate searching for a path to follow, avoiding obstacles and generating the best trajectory that ensures safety, comfort and efficiency. A range of different planning approaches have been proposed in the literature. The purpose of this paper is to review existing approaches and then compare and contrast different methods employed for the motion planning of autonomous on-road driving that consists of (1) finding a path, (2) searching for the safest manoeuvre and (3) determining the most feasible trajectory. Methods developed by researchers in each of these three levels exhibit varying levels of complexity and performance accuracy. This paper presents a critical evaluation of each of these methods, in terms of their advantages/disadvantages, inherent limitations, feasibility, optimality, handling of obstacles and testing operational environments. Based on a critical review of existing methods, research challenges to address current limitations are identified and future research directions are suggested so as to enhance the performance of planning algorithms at all three levels. Some promising areas of future focus have been identified as the use of vehicular communications (V2V and V2I) and the incorporation of transport engineering aspects in order to improve the look-ahead horizon of current sensing technologies that are essential for planning with the aim of reducing the total cost of driverless vehicles. This critical review on planning techniques presented in this paper, along with the associated discussions on their constraints and limitations, seek to assist researchers in accelerating development in the emerging field of autonomous vehicle research

Real-time motion planning methods for autonomous on-road driving: state-of-the-art and future research directions

Currently autonomous or self-driving vehicles are at the heart of academia and industry research because of its multi-faceted advantages that includes improved safety, reduced congestion, lower emissions and greater mobility. Software is the key driving factor underpinning autonomy within which planning algorithms that are responsible for mission-critical decision making hold a significant position. While transporting passengers or goods from a given origin to a given destination, motion planning methods incorporate searching for a path to follow, avoiding obstacles and generating the best trajectory that ensures safety, comfort and efficiency. A range of different planning approaches have been proposed in the literature. The purpose of this paper is to review existing approaches and then compare and contrast different methods employed for the motion planning of autonomous on-road driving that consists of (1) finding a path, (2) searching for the safest manoeuvre and (3) determining the most feasible trajectory. Methods developed by researchers in each of these three levels exhibit varying levels of complexity and performance accuracy. This paper presents a critical evaluation of each of these methods, in terms of their advantages/disadvantages, inherent limitations, feasibility, optimality, handling of obstacles and testing operational environments. Based on a critical review of existing methods, research challenges to address current limitations are identified and future research directions are suggested so as to enhance the performance of planning algorithms at all three levels. Some promising areas of future focus have been identified as the use of vehicular communications (V2V and V2I) and the incorporation of transport engineering aspects in order to improve the look-ahead horizon of current sensing technologies that are essential for planning with the aim of reducing the total cost of driverless vehicles. This critical review on planning techniques presented in this paper, along with the associated discussions on their constraints and limitations, seek to assist researchers in accelerating development in the emerging field of autonomous vehicle research