Enhancing Mobile Object Classification Using Geo-referenced Maps and Evidential Grids

Evidential grids have recently shown interesting properties for mobile object perception. Evidential grids are a generalisation of Bayesian occupancy grids using Dempster- Shafer theory. In particular, these grids can handle efficiently partial information. The novelty of this article is to propose a perception scheme enhanced by geo-referenced maps used as an additional source of information, which is fused with a sensor grid. The paper presents the key stages of such a data fusion process. An adaptation of conjunctive combination rule is presented to refine the analysis of the conflicting information. The method uses temporal accumulation to make the distinction between stationary and mobile objects, and applies contextual discounting for modelling information obsolescence. As a result, the method is able to better characterise the occupied cells by differentiating, for instance, moving objects, parked cars, urban infrastructure and buildings. Experiments carried out on real- world data illustrate the benefits of such an approach.Comment: 6 pp. arXiv admin note: substantial text overlap with arXiv:1207.101

Recent Advances in Indoor Localization Systems and Technologies

Despite the enormous technical progress seen in the past few years, the maturity of indoor localization technologies has not yet reached the level of GNSS solutions. The 23 selected papers in this book present the recent advances and new developments in indoor localization systems and technologies, propose novel or improved methods with increased performance, provide insight into various aspects of quality control, and also introduce some unorthodox positioning methods

Development and evaluation of low cost 2-d lidar based traffic data collection methods

Traffic data collection is one of the essential components of a transportation planning exercise. Granular traffic data such as volume count, vehicle classification, speed measurement, and occupancy, allows managing transportation systems more effectively. For effective traffic operation and management, authorities require deploying many sensors across the network. Moreover, the ascending efforts to achieve smart transportation aspects put immense pressure on planning authorities to deploy more sensors to cover an extensive network. This research focuses on the development and evaluation of inexpensive data collection methodology by using two-dimensional (2-D) Light Detection and Ranging (LiDAR) technology. LiDAR is adopted since it is economical and easily accessible technology. Moreover, its 360-degree visibility and accurate distance information make it more reliable. To collect traffic count data, the proposed method integrates a Continuous Wavelet Transform (CWT), and Support Vector Machine (SVM) into a single framework. Proof-of-Concept (POC) test is conducted in three different places in Newark, New Jersey to examine the performance of the proposed method. The POC test results demonstrate that the proposed method achieves acceptable performances, resulting in 83% ~ 94% accuracy. It is discovered that the proposed method\u27s accuracy is affected by the color of the exterior surface of a vehicle since some colored surfaces do not produce enough reflective rays. It is noticed that the blue and black colors are less reflective, while white-colored surfaces produce high reflective rays. A methodology is proposed that comprises K-means clustering, inverse sensor model, and Kalman filter to obtain trajectories of the vehicles at the intersections. The primary purpose of vehicle detection and tracking is to obtain the turning movement counts at an intersection. A K-means clustering is an unsupervised machine learning technique that clusters the data into different groups by analyzing the smallest mean of a data point from the centroid. The ultimate objective of applying K-mean clustering is to identify the difference between pedestrians and vehicles. An inverse sensor model is a state model of occupancy grid mapping that localizes the detected vehicles on the grid map. A constant velocity model based Kalman filter is defined to track the trajectory of the vehicles. The data are collected from two intersections located in Newark, New Jersey, to study the accuracy of the proposed method. The results show that the proposed method has an average accuracy of 83.75%. Furthermore, the obtained R-squared value for localization of the vehicles on the grid map is ranging between 0.87 to 0.89. Furthermore, a primary cost comparison is made to study the cost efficiency of the developed methodology. The cost comparison shows that the proposed methodology based on 2-D LiDAR technology can achieve acceptable accuracy at a low price and be considered a smart city concept to conduct extensive scale data collection

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

Advances in Intelligent Vehicle Control

This book is a printed edition of the Special Issue Advances in Intelligent Vehicle Control that was published in the journal Sensors. It presents a collection of eleven papers that covers a range of topics, such as the development of intelligent control algorithms for active safety systems, smart sensors, and intelligent and efficient driving. The contributions presented in these papers can serve as useful tools for researchers who are interested in new vehicle technology and in the improvement of vehicle control systems

Advanced Sensing and Control for Connected and Automated Vehicles

Connected and automated vehicles (CAVs) are a transformative technology that is expected to change and improve the safety and efficiency of mobility. As the main functional components of CAVs, advanced sensing technologies and control algorithms, which gather environmental information, process data, and control vehicle motion, are of great importance. The development of novel sensing technologies for CAVs has become a hotspot in recent years. Thanks to improved sensing technologies, CAVs are able to interpret sensory information to further detect obstacles, localize their positions, navigate themselves, and interact with other surrounding vehicles in the dynamic environment. Furthermore, leveraging computer vision and other sensing methods, in-cabin humans’ body activities, facial emotions, and even mental states can also be recognized. Therefore, the aim of this Special Issue has been to gather contributions that illustrate the interest in the sensing and control of CAVs

Novel AI-assisted computational solutions for GPR data interpretation and electromagnetic data fusion to detect buried utilities

This research presents a number of novel computational solutions using artificial intelligence (AI) to interpret ground penetrating radar (GPR) data as well as fusing GPR data with data from other sensing modalities, including electromagnetic conductivity (EMC) and electromagnetic locating (EML). The application of the proposed computational solution is predominantly for detecting and locating buried utilities (e.g. pipes and cables) and ground anomalies (e.g. ground disturbances) in the shallow subsurface environment although the work can be extended to detect other buried anomalies. Processing GPR data is usually a subjective and time-consuming practise which involves expert intervention. Thus, the quality of the interpretation of such data depends on user experience and knowledge. Whilst several numerical approaches are available in the literature for post-processing GPR data, they all suffer from various shortcomings including lack of accuracy and/or excessive computational time. The issue is similar (or often worse) for data fusion between GPR and other sensors e.g. EMC and EML. To tackle some of these issues, in this research, four new computational procedures were developed. Three of these computational procedures are based on Kalman Filtering (KF), a less-studied approach to process GPR radargrams despite its great potential in efficient data analysis, and genetic algorithm (GA) as a machine learning based global optimisation tool. The final computational procedure combines finite element modelling and genetic algorithm to infer fused EML-GPR data. For the first two numerical methods, new algorithms were developed to optimise KF parameters using GA to remove noises from GPR radargrams and detect targets. The proposed procedures were validated against data from field and their performance was assessed against additional unseen dataset different to that of the validation to identify their potential limitations. Furthermore, their performances were compared against existing GPR data processing methods and differences were highlighted. The other two computational packages focused on data fusion from GPR and EMC/EML. The first of these two, extended the above KF algorithm to fuse data from GPR and EML as well as GPR and EMC. The results showed that the proposed data fusion algorithm significantly enhanced the quality of locating conductors and conductive regions in the subsurface compared to the individual techniques which were either incapable of defining the material of the buried target or the geometry of conductive anomalies. Finally, a novel inversion algorithm was developed by integrating finite element modelling of a coupled magnetic field and GA for detecting and locating buried live cables using GPR and EML. It was demonstrated that the proposed inversion can successfully detect the location of the buried cables as well as their intensity

Vehicle geo-localization based on IMM-UKF data fusion using a GPS receiver, a video camera, and a 3D city model

International audienceVehicle geo-localization in urban areas remains to be challenging problems in urban areas. For this purpose, Global Positioning System (GPS) receiver is usually the main sensor. But, the use of GPS alone is not sufficient in many urban environments due to wave multi-path. In order to provide accurate and robust localization, GPS has so to be helped with other sensors like dead-reckoned sensors, map data, cameras or LIDAR. In this paper, a new observation of the absolute pose of the vehicle is proposed to back up GPS measurements. The proposed approach exploits a virtual 3D model managed by a 3D geographical information system (3D GIS) and a video camera. The concept is to register the acquired image to the 3D model that is geo-localized. For that, two images have to be matched: the real image and the virtual image. The real image is acquired by the on board camera and provides the real view of the scene viewed by the vehicle. The virtual image is provided by the 3D GIS. The developed method is composed of three parts. The first part consists in detecting and matching the feature points of the real image and of the virtual image. Two methods: SIFT (Scale Invariant Feature Transform) and Harris corner detector are compared. The second part concerns the position computation using POSIT algorithm and the previously matched features set. The third part concerns the data fusion using IMM-UKF (Interacting Multiple Model-Unscented Kalman Filter). The proposed approach has been tested on a real sequence and the obtained results proved the feasibility and robustness of the approach