FusionPlanner: A Multi-task Motion Planner for Mining Trucks using Multi-sensor Fusion Method

In recent years, significant achievements have been made in motion planning for intelligent vehicles. However, as a typical unstructured environment, open-pit mining attracts limited attention due to its complex operational conditions and adverse environmental factors. A comprehensive paradigm for unmanned transportation in open-pit mines is proposed in this research, including a simulation platform, a testing benchmark, and a trustworthy and robust motion planner. \textcolor{red}{Firstly, we propose a multi-task motion planning algorithm, called FusionPlanner, for autonomous mining trucks by the Multi-sensor fusion method to adapt both lateral and longitudinal control tasks for unmanned transportation. Then, we develop a novel benchmark called MiningNav, which offers three validation approaches to evaluate the trustworthiness and robustness of well-trained algorithms in transportation roads of open-pit mines. Finally, we introduce the Parallel Mining Simulator (PMS), a new high-fidelity simulator specifically designed for open-pit mining scenarios. PMS enables the users to manage and control open-pit mine transportation from both the single-truck control and multi-truck scheduling perspectives.} \textcolor{red}{The performance of FusionPlanner is tested by MiningNav in PMS, and the empirical results demonstrate a significant reduction in the number of collisions and takeovers of our planner. We anticipate our unmanned transportation paradigm will bring mining trucks one step closer to trustworthiness and robustness in continuous round-the-clock unmanned transportation.Comment: 2Pages, 10 figure

Transformation-adversarial network for road detection in LIDAR rings, and model-free evidential road grid mapping.

International audienceWe propose a deep learning approach to perform road-detection in LIDAR scans, at the point level. Instead of processing a full LIDAR point-cloud, LIDAR rings can be processed individually. To account for the geometrical diversity among LIDAR rings, an homothety rescaling factor can be predicted during the classification, to realign all the LIDAR rings and facilitate the training. This scale factor is learnt in a semi-supervised fashion. A performant classification can then be achieved with a relatively simple system. Furthermore, evidential mass values can be generated for each point from an observation of the conflict at the output of the network, which enables the classification results to be fused in evidential grids. Experiments are done on real-life LIDAR scans that were labelled from a lane-level centimetric map, to evaluate the classification performances

Simultaneous Localization and Mapping (SLAM) for Autonomous Driving: Concept and Analysis

The Simultaneous Localization and Mapping (SLAM) technique has achieved astonishing progress over the last few decades and has generated considerable interest in the autonomous driving community. With its conceptual roots in navigation and mapping, SLAM outperforms some traditional positioning and localization techniques since it can support more reliable and robust localization, planning, and controlling to meet some key criteria for autonomous driving. In this study the authors first give an overview of the different SLAM implementation approaches and then discuss the applications of SLAM for autonomous driving with respect to different driving scenarios, vehicle system components and the characteristics of the SLAM approaches. The authors then discuss some challenging issues and current solutions when applying SLAM for autonomous driving. Some quantitative quality analysis means to evaluate the characteristics and performance of SLAM systems and to monitor the risk in SLAM estimation are reviewed. In addition, this study describes a real-world road test to demonstrate a multi-sensor-based modernized SLAM procedure for autonomous driving. The numerical results show that a high-precision 3D point cloud map can be generated by the SLAM procedure with the integration of Lidar and GNSS/INS. Online four–five cm accuracy localization solution can be achieved based on this pre-generated map and online Lidar scan matching with a tightly fused inertial system

Exploitation des données cartographiques pour la perception de véhicules intelligents

This thesis is situated in the domains of robotics and data fusion, and concerns geographic information systems. We study the utility of adding digital maps, which model the urban environment in which the vehicle evolves, as a virtual sensor improving the perception results. Indeed, the maps contain a phenomenal quantity of information about the environment : its geometry, topology and additional contextual information. In this work, we extract road surface geometry and building models in order to deduce the context and the characteristics of each detected object. Our method is based on an extension of occupancy grids : the evidential perception grids. It permits to model explicitly the uncertainty related to the map and sensor data. By this means, the approach presents also the advantage of representing homogeneously the data originating from various sources : lidar, camera or maps. The maps are handled on equal terms with the physical sensors. This approach allows us to add geographic information without imputing unduly importance to it, which is essential in presence of errors. In our approach, the information fusion result, stored in a perception grid, is used to predict the stateof environment on the next instant. The fact of estimating the characteristics of dynamic elements does not satisfy the hypothesis of static world. Therefore, it is necessary to adjust the level of certainty attributed to these pieces of information. We do so by applying the temporal discounting. Due to the fact that existing methods are not well suited for this application, we propose a family of discoun toperators that take into account the type of handled information. The studied algorithms have been validated through tests on real data. We have thus developed the prototypes in Matlab and the C++ software based on Pacpus framework. Thanks to them, we present the results of experiments performed in real conditions.La plupart des logiciels contrôlant les véhicules intelligents traite de la compréhension de la scène. De nombreuses méthodes existent actuellement pour percevoir les obstacles de façon automatique. La majorité d’entre elles emploie ainsi les capteurs extéroceptifs comme des caméras ou des lidars. Cette thèse porte sur les domaines de la robotique et de la fusion d’information et s’intéresse aux systèmes d’information géographique. Nous étudions ainsi l’utilité d’ajouter des cartes numériques, qui cartographient le milieu urbain dans lequel évolue le véhicule, en tant que capteur virtuel améliorant les résultats de perception. Les cartes contiennent en effet une quantité phénoménale d’information sur l’environnement : sa géométrie, sa topologie ainsi que d’autres informations contextuelles. Dans nos travaux, nous avons extrait la géométrie des routes et des modèles de bâtiments afin de déduire le contexte et les caractéristiques de chaque objet détecté. Notre méthode se base sur une extension de grilles d’occupations : les grilles de perception crédibilistes. Elle permet de modéliser explicitement les incertitudes liées aux données de cartes et de capteurs. Elle présente également l’avantage de représenter de façon uniforme les données provenant de différentes sources : lidar, caméra ou cartes. Les cartes sont traitées de la même façon que les capteurs physiques. Cette démarche permet d’ajouter les informations géographiques sans pour autant leur donner trop d’importance, ce qui est essentiel en présence d’erreurs. Dans notre approche, le résultat de la fusion d’information contenu dans une grille de perception est utilisé pour prédire l’état de l’environnement à l’instant suivant. Le fait d’estimer les caractéristiques des éléments dynamiques ne satisfait donc plus l’hypothèse du monde statique. Par conséquent, il est nécessaire d’ajuster le niveau de certitude attribué à ces informations. Nous y parvenons en appliquant l’affaiblissement temporel. Étant donné que les méthodes existantes n’étaient pas adaptées à cette application, nous proposons une famille d’opérateurs d’affaiblissement prenant en compte le type d’information traitée. Les algorithmes étudiés ont été validés par des tests sur des données réelles. Nous avons donc développé des prototypes en Matlab et des logiciels en C++ basés sur la plate-forme Pacpus. Grâce à eux nous présentons les résultats des expériences effectués en conditions réelles

Detecting and Tracking Vulnerable Road Users\u27 Trajectories Using Different Types of Sensors Fusion

Vulnerable road user (VRU) detection and tracking has been a key challenge in transportation research. Different types of sensors such as the camera, LiDAR, and inertial measurement units (IMUs) have been used for this purpose. For detection and tracking with the camera, it is necessary to perform calibration to obtain correct GPS trajectories. This method is often tedious and necessitates accurate ground truth data. Moreover, if the camera performs any pan-tilt-zoom function, it is usually necessary to recalibrate the camera. In this thesis, we propose camera calibration using an auxiliary sensor: ultra-wideband (UWB). USBs are capable of tracking a road user with ten-centimeter-level accuracy. Once a VRU with a UWB traverses in the camera view, the UWB GPS data is fused with the camera to perform real-time calibration. As the experimental results in this thesis have shown, the camera is able to output better trajectories after calibration. It is expected that the use of UWB is needed only once to fuse the data and determine the correct trajectories at the same intersection and location of the camera. All other trajectories collected by the camera can be corrected using the same adjustment. In addition, data analysis was conducted to evaluate the performance of the UWB sensors. This study also predicted pedestrian trajectories using data fused by the UWB and smartphone sensors. UWB GPS coordinates are very accurate although it lacks other sensor parameters such as accelerometer, gyroscope, etc. The smartphone data have been used in this scenario to augment the UWB data. The two datasets were merged on the basis of the closest timestamp. The resulting dataset has precise latitude and longitude from UWB as well as the accelerometer, gyroscope, and speed data from smartphones making the fused dataset accurate and rich in terms of parameters. The fused dataset was then used to predict the GPS coordinates of pedestrians and scooters using LSTM

Fail-Safe Vehicle Pose Estimation in Lane-Level Maps Using Pose Graph Optimization

Die hochgenaue Posenschätzung autonomer Fahrzeuge sowohl in HD-Karten als auch spurrelativ ist unerlässlich um eine sichere Fahrzeugführung zu gewährleisten. Für die Serienfertigung wird aus Kosten- und Platzgründen bewusst auf hochgenaue, teure Einzelsensorik verzichtet und stattdessen auf eine Vielzahl von Sensoren, die neben der Posenschätzung auch von anderen Modulen verwendet werden können, zurückgegriffen. Im Fokus dieser Arbeit steht die Unsicherheitsschätzung, Bewertung und Fusion dieser Sensordaten. Die Optimierung von Posengraphen zur Fusion von Sensordaten zeichnet sich, im Gegensatz zu klassischen Filterverfahren, wie Kalman oder Partikelfilter, durch seine Robustheit gegenüber Fehlmessungen und der Flexibilität in der Modellierung aus. Die Optimierung eines Posengraphen wurde erstmalig auf mobilen Roboterplattformen zur Lösung sogenannter SLAM-Probleme angewendet. Diese Verfahren wurden immer weiter entwickelt und im speziellen auch zur rein kamerabasierten Lokalisierung autonomer Fahrzeuge in 3D-Punktwolken erfolgreich emonstriert. Für die Entwicklung und Freigabe sicherheitsrelevanter Systeme nach ISO 26262 wird neben der Genauigkeit jedoch auch eine Aussage über die Qualität und Ausfallsicherheit dieser Systeme gefordert. Diese Arbeit befasst sich, neben der Schätzung der karten- und spurrelativen Pose, auch mit der Schätzung der Posenunsicherheit und der Integrität der Sensordaten zueinander. Auf Grundlage dieser Arbeit wird eine Abschätzung der Ausfallsicherheit des Lokalisierungsmoduls ermöglicht. Motiviert durch das Projekt Ko-HAF werden zur Lokalisierung in HD-Karten lediglich Spurmarkierungen verwendet. Die speichereffiziente Darstellung dieser Karten ermöglicht eine hochfrequente Aktualisierung der Karteninhalte durch eine Fahrzeugflotte. Der vorgestellte Ansatz wurde prototypisch auf einem Opel Insignia umgesetzt. Der Testträger wurde um eine Front- und Heckkamera sowie einen GNSS-Empfänger erweitert. Zunächst werden die Schätzung der karten-und spurrelativen Fahrzeugpose, der GNSS-Signalauswertung sowie der Bewegungsschätzung des Fahrzeugs vorgestellt. Durch einen Vergleich der Schätzungen zueinander werden die Unsicherheiten der einzelnen Module berechnet. Das Lokalisierungsproblem wird dann durch einen Optimierer gelöst. Mithilfe der berechneten Unsicherheiten wird in einem nachgelagerten Schritt eine Bewertung der einzelnen Module durchgeführt. Zur Bewertung des Ansatzes wurden sowohl hochdynamische Manöver auf einer Teststrecke als auch Fahrten auf öffentlichen Autobahnen ausgewertet

The Proceedings of 14th Australian Digital Forensics Conference, 5-6 December 2016, Edith Cowan University, Perth, Australia

Conference Foreword This is the fifth year that the Australian Digital Forensics Conference has been held under the banner of the Security Research Institute, which is in part due to the success of the security conference program at ECU. As with previous years, the conference continues to see a quality papers with a number from local and international authors. 11 papers were submitted and following a double blind peer review process, 8 were accepted for final presentation and publication. Conferences such as these are simply not possible without willing volunteers who follow through with the commitment they have initially made, and I would like to take this opportunity to thank the conference committee for their tireless efforts in this regard. These efforts have included but not been limited to the reviewing and editing of the conference papers, and helping with the planning, organisation and execution of the conference. Particular thanks go to those international reviewers who took the time to review papers for the conference, irrespective of the fact that they are unable to attend this year. To our sponsors and supporters a vote of thanks for both the financial and moral support provided to the conference. Finally, to the student volunteers and staff of the ECU Security Research Institute, your efforts as always are appreciated and invaluable. Yours sincerely, Conference Chair Professor Craig Valli Director, Security Research Institut

DRONE DELIVERY OF CBNRECy – DEW WEAPONS Emerging Threats of Mini-Weapons of Mass Destruction and Disruption (WMDD)

Drone Delivery of CBNRECy – DEW Weapons: Emerging Threats of Mini-Weapons of Mass Destruction and Disruption (WMDD) is our sixth textbook in a series covering the world of UASs and UUVs. Our textbook takes on a whole new purview for UAS / CUAS/ UUV (drones) – how they can be used to deploy Weapons of Mass Destruction and Deception against CBRNE and civilian targets of opportunity. We are concerned with the future use of these inexpensive devices and their availability to maleficent actors. Our work suggests that UASs in air and underwater UUVs will be the future of military and civilian terrorist operations. UAS / UUVs can deliver a huge punch for a low investment and minimize human casualties.https://newprairiepress.org/ebooks/1046/thumbnail.jp