Reliable Positioning and Journey Planning for Intelligent Transport Systems

Safety and reliability of intelligent transport systems applications require positioning accuracy at the sub-meter level with availability and integrity above 99%. At present, no single positioning sensor can meet these requirements in particular in the urban environment. Possible sensors that can be used for this task are first reviewed. Next, a suggested integrated system of low-cost real-time kinematic (RTK) GNSS, inertial measurement units (IMU) and vehicle odometer is discussed. To ensure positioning integrity, a method for fault detection in GNSS observations and computation of the protection levels (PL) that bound the position errors at a pre-set risk probability of the integrated sensors are presented. A case study is performed for demonstration. Moreover, to save energy, reduce pollution, and to improve the economy of the trip, proper journey planning is required. A new approach is introduced using 3D city models to predict the route with the best positioning integrity, availability and precision for route selection among different possible routes. The practical demonstration shows that effectiveness of this method. Finally, the potential of using the next generation SBAS for ITS applications was tested using kinematic tests carried out in various environments characterized by different levels of sky-visibility that may affect observations from GNSS

AN ARTIFICIAL INTELLIGENCE APPROACH TO THE PROCESSING OF RADAR RETURN SIGNALS FOR TARGET DETECTION

Most of the operating vessel traffic management systems experience problems, such as track loss and track swap, which may cause confusion to the traffic regulators and lead to potential hazards in the harbour operation. The reason is mainly due to the limited adaptive capabilities of the algorithms used in the detection process. The decision on whether a target is present is usually based on the magnitude of the returning echoes. Such a method has a low efficiency in discriminating between the target and clutter, especially when the signal to noise ratio is low. The performance of radar target detection depends on the features, which can be used to discriminate between clutter and targets. To have a significant improvement in the detection of weak targets, more obvious discriminating features must be identified and extracted. This research investigates conventional Constant False Alarm Rate (CFAR) algorithms and introduces the approach of applying ar1ificial intelligence methods to the target detection problems. Previous research has been unde11aken to improve the detection capability of the radar system in the heavy clutter environment and many new CFAR algorithms, which are based on amplitude information only, have been developed. This research studies these algorithms and proposes that it is feasible to design and develop an advanced target detection system that is capable of discriminating targets from clutters by learning the .different features extracted from radar returns. The approach adopted for this further work into target detection was the use of neural networks. Results presented show that such a network is able to learn particular features of specific radar return signals, e.g. rain clutter, sea clutter, target, and to decide if a target is present in a finite window of data. The work includes a study of the characteristics of radar signals and identification of the features that can be used in the process of effective detection. The use of a general purpose marine radar has allowed the collection of live signals from the Plymouth harbour for analysis, training and validation. The approach of using data from the real environment has enabled the developed detection system to be exposed to real clutter conditions that cannot be obtained when using simulated data. The performance of the neural network detection system is evaluated with further recorded data and the results obtained are compared with the conventional CFAR algorithms. It is shown that the neural system can learn the features of specific radar signals and provide a superior performance in detecting targets from clutters. Areas for further research and development arc presented; these include the use of a sophisticated recording system, high speed processors and the potential for target classification

A Priority Rate-Based Routing Protocol for wireless multimedia sensor networks

The development of affordable hardware has made it possible to transmit multimedia data over a wireless medium using sensor devices. Deployed sensors span larger geographical areas, generating different kinds of traffic that need to be communicated either in real-time or non-real-time mode to the sink. The tiny sized design of sensor nodes has made them even more attractive in various environments as they can be left unattended for longer periods. Since sensor nodes are equipped with limited resources, newer energy-efficient protocols and architectures are required in order to meet requirements within their limited capabilities when dealing with multimedia data. This is because multimedia applications are characterized by strict quality of service requirements that distinctively differentiate them from other data types during transmission. However, the large volume of data produced by the sensor nodes can easily cause traffic congestion making it difficult to meet these requirements. Congestion has negative impacts on the data transmitted as well as the sensor network at large. Failure to control congestion will affect the quality of multimedia data received at the sink and further shorten the system lifetime. Next generation wireless sensor networks are predicted to deploy a different model where service is allocated to multimedia while bearing congestion in mind. Applying traditional wireless sensor routing algorithms to wireless multimedia sensor networks may lead to high delay and poor visual quality for multimedia applications. In this research, a Priority Rate-Based Routing Protocol (PRRP) that assigns priorities to traffic depending on their service requirements is proposed. PRRP detects congestion by using adaptive random early detection (A-RED) and a priority rate-based adjustment technique to control congestion. We study the performance of our proposed multi-path routing algorithm for real-time traffic when mixed with three non real-time traffic each with a different priority: high, medium or low. Simulation results show that the proposed algorithm performs better when compared to two existing algorithms, PCCP and PBRC-SD, in terms of queueing delay, packet loss and throughput

Intelligent embedded systems platform for vehicular cyber-physical systems

Intelligent vehicular cyber-physical systems (ICPSs) increase the reliability, efficiency and adaptability of urban mobility systems. Notably, ICPSs enable autonomous transportation in smart cities, exemplified by the emerging fields of self-driving cars and advanced air mobility. Nonetheless, the deployment of ICPSs raises legitimate concerns surrounding safety assurance, cybersecurity threats, communication reliability, and data management. Addressing these issues often necessitates specialised platforms to cater to the heterogeneity and complexity of ICPSs. To address this challenge, this paper presents a comprehensive CPS to explore, develop and test ICPSs and intelligent vehicular algorithms. A customisable embedded system is realised using a field programmable gate array, which is connected to a supervisory computer to enable networked operations and support advanced multi-agent algorithms. The platform remains compatible with multiple vehicular sensors, communication protocols and human–machine interfaces, essential for a vehicle to perceive its surroundings, communicate with collaborative systems, and interact with its occupants. The proposed CPS thereby offers a practical resource to advance ICPS development, comprehension, and experimentation in both educational and research settings. By bridging the gap between theory and practice, this tool empowers users to overcome the complexities of ICPSs and contribute to the emerging fields of autonomous transportation and intelligent vehicular systems

Mathematical modeling for partial object detection.

From a computer vision point of view, the image is a scene consisting of objects of interest and a background represented by everything else in the image. The relations and interactions among these objects are the key factors for scene understanding. In this dissertation, a mathematical model is designed for the detection of partially occluded faces captured in unconstrained real life conditions. The proposed model novelty comes from explicitly considering certain objects that are common to occlude faces and embedding them in the face model. This enables the detection of faces in difficult settings and provides more information to subsequent analysis in addition to the bounding box of the face. In the proposed Selective Part Models (SPM), the face is modelled as a collection of parts that can be selected from the visible regular facial parts and some of the occluding objects which commonly interact with faces such as sunglasses, caps, hands, shoulders, and other faces. With the face detection being the first step in the face recognition pipeline, the proposed model does not only detect partially occluded faces efficiently but it also suggests the occluded parts to be excluded from the subsequent recognition step. The model was tested on several recent face detection databases and benchmarks and achieved state of the art performance. In addition, detailed analysis for the performance with respect to different types of occlusion were provided. Moreover, a new database was collected for evaluating face detectors focusing on the partial occlusion problem. This dissertation highlights the importance of explicitly handling the partial occlusion problem in face detection and shows its efficiency in enhancing both the face detection performance and the subsequent recognition performance of partially occluded faces. The broader impact of the proposed detector exceeds the common security applications by using it for human robot interaction. The humanoid robot Nao is used to help in teaching children with autism and the proposed detector is used to achieve natural interaction between the robot and the children by detecting their faces which can be used for recognition or more interestingly for adaptive interaction by analyzing their expressions

Asynchronous Visualization of Spatiotemporal Information for Multiple Moving Targets

In the modern information age, the quantity and complexity of spatiotemporal data is increasing both rapidly and continuously. Sensor systems with multiple feeds that gather multidimensional spatiotemporal data will result in information clusters and overload, as well as a high cognitive load for users of these systems. To meet future safety-critical situations and enhance time-critical decision-making missions in dynamic environments, and to support the easy and effective managing, browsing, and searching of spatiotemporal data in a dynamic environment, we propose an asynchronous, scalable, and comprehensive spatiotemporal data organization, display, and interaction method that allows operators to navigate through spatiotemporal information rather than through the environments being examined, and to maintain all necessary global and local situation awareness. To empirically prove the viability of our approach, we developed the Event-Lens system, which generates asynchronous prioritized images to provide the operator with a manageable, comprehensive view of the information that is collected by multiple sensors. The user study and interaction mode experiments were designed and conducted. The Event-Lens system was discovered to have a consistent advantage in multiple moving-target marking-task performance measures. It was also found that participants’ attentional control, spatial ability, and action video gaming experience affected their overall performance

Extending Comprehensive Maritime Awareness to Disconnected Vessels and Users

After the attacks of 9/11, increased security became a national priority that resulted in a focus on National Maritime Security. Maritime Domain Awareness (MDA) is an initiative developed by the Coast Guard, in partnership with the U.S. Navy and other agencies to increase awareness in the maritime domain in support of maritime security [Morgan and Wimmer, 2005]. The purpose of MDA is to generate actionable intelligence obtained via the collection, fusion and dissemination of information from U.S. joint forces, U.S. government agencies, international coalition partners and commercial entities. This actionable intelligence is the cornerstone of successful counterterrorist and maritime law enforcement operations and is critical to Maritime Security [Morgan and Wimmer, 2005]. The U.S. Navy, as a partner in the development and creation of MDA, has tasked its subordinate commands to identify and define capabilities to support this program. One effort sponsored is the Comprehensive Maritime Awareness (CMA) Joint Capabilities Technology Demonstration (JCTD) [CMA Architecture Team, 2007]. This project supports the CMA JCTD efforts by proposing a deployable system to enable a disconnected vessel to connect to the CMA network. A disconnected user can be seen as a merchant ship, hospital ship or any vessel that is not currently connected to the CMA network. This project's proposed deployable system, as a subset to the CMA network, facilitates information sharing in support of humanitarian efforts worldwide.http://archive.org/details/extendingcompreh109456932N