In order for an autonomous vehicle to navigate its environment, it must be able to sense and perceive the environment in a meaningful way. The specific tasks or environments expected to be encountered may dictate the exact choices of sensor or system organization, but the general sensor systems for autonomous vehicles involve perceiving the environment and determining what is an obstacle or objective, or assisting the vehicle in tracking its movements through the environment. Sensing can be performed either actively or passively. Passive sensors function as self-contained measurement instruments. They are used to perceive an aspect of the environment or the system’s relation to the environment without broadcasting or emitting any form of radiation. Active sensors function by emitting a known signal and sensing how the signal is influenced by the environment. The most common type of active sensing is time of flight, wherein the round trip time of a known pulse or burst is measured, and is used for distance measurement and object tracking. Other approaches include measuring the change of a constant (not pulsed) signal. These include “structured light” sensors and some types of radar. Active sensors are often prone to interference in some manner, be it from uncontrollable environmental factors like weather or from one another when used multiple times on the same platform or in a given space.
An Autonomous navigation drone with help of object recognition and GPS coordination was designed and implemented. Each component was tested and verified to be working as intended. Test flights have been conducted and the results confirmed that the hex copter can fly in a stable manner. The drone also has obstacle avoidance capability to avoid collision. Autonomous flight was successfully completed by giving the required GPS coordinates of the places. Live object detection was also done. The tuning of the PID control system was accomplished using custom test benches. Three different PID control systems for pitch, roll, and yaw had been tuned carefully for proper stabilization. The hex copter weighs 1800 grams, maintains an average flight duration of 10 minutes, and can be controlled within a range of 300 meters. The different flight modes such as stabilize, altitude hold, position hold and auto mode, programmed into flight controller were tested successfully. Inspection of high structures, humanitarian purposes or search and rescue missions can be easily performed by this drone.
From the inception of computer based learning techniques to omnipresent ubiquitous learning environments, learning methodologies have seen a remarkable paradigm shift. Earlier, class room based teaching and learning was confined to students interacting with teachers and vice versa. Learners were totally dependent on their trainers and the knowledge was confined within the scope of their trainer’s intellect. With the advent of e-learning or computer based learning, students were able to shell out of their class rooms and communicate directly with their instructor and peers, using the chat messaging and discussion forums. It gave them an opportunity to work collaboratively in virtual teams using online resources. Then came the era of mobile learning where users carry portable learning devices like cellular phones and PDA’s which connect to internet over wireless communication technologies. It enables the users to learn anytime and anywhere by providing them access to shared resources, independently from their physical location i.e. they are free to move while learning. However Pervasive learning uses information about the context of learning from the learning environment where small devices such as sensors, pads and badges are embedded and communicate mutually. It requires heavy setup, thus they offer very low mobility. Traditional class room type and e- learning approaches to learning , typically require a human supervisor to design the learning architecture and protocols, select the training examples, choose the learning algorithm, set the learning parameters, decide when to stop learning, and choose the way
Given the disruptive potential of autonomous vehicles and world-wide interest in their military applications, it is of the utmost strategic importance that the United States and its allies lead in the research, development, and adoption of autonomoussystems. Given its recent history of technological supremacy, it may seem that the U.S. Department of Defense is well positioned to maintain its preeminence. However, the current acquisitions process is too costly and slow to keep pace with the rapid progress being made in the field [10, 11, 12]. For example, the Long Range Anti-Ship Missile (LRASM) is a prime case study for the noncompetitive autonomoussystems acquisitions process in action. The system itself is an extremely capable autonomous munition, capable of navigating in GPS denied environments, discriminating targets from non-targets, evasive maneuver, and targeted strikes . However, its “acceler- ated acquisition” is still in progress after 7 years and an estimated program cost of 1 billion dollars for a mere 110 units . If these metrics are indicative of future procurement programs, they set a low bar for adversaries to meet or exceed.
systems, vehicles as well as infrastructure devices can interact and exchange data with each other. This capability is used to implement intelligent transportation systems applications. Data confidentiality and integrity need to be preserved in unverified and untrusted environments. In this paper, we propose a solution that provides (a) role-based and attribute-based access control to encrypted data and (b) encrypted search over encrypted data. Vehicle Records contain sensitive information about the owners and vehicles in encrypted form with attached access control policies and policy enforcement engine. Our solution supports decentralized and distributed data exchange, which is essential in V2X systems, where a Central Authority is not required to enforce access control policies. Furthermore, we facilitate querying encrypted Vehicle Records through Structured Query Language (SQL) queries. Vehicle Records are stored in a database in untrusted V2X cloud environment that is prone to provide the attackers with a large attack surface. Big datasets, stored in cloud, can be used for data analysis, such as traffic pattern analysis. Our solution protects sensitive vehicle and owner information from curious or malicious information cloud administrators. Support of indexing improves performance of queries that are forwarded to relevant encrypted Vehicle Records, which are stored in the cloud. We measure the performance overhead of our security solution based on self-protecting Vehicle Records with encrypted search capabilities in V2X communication systems and analyze the effect of security over safety.
strategy which generates suitable reference trajectories for tackling the path tracking (trajectory tracking) problem. The LOS guidance is used again to generate the heading reference trajectories and minimize the cross-track er- ror while the new guidance law minimizes the along-track error by generating appropriate reference trajectories for the velocity controller. The vehicle is assigned to pursue a virtual vehicle (or particle) moving on the path. Initially, the novel guidance methodology is based on the vehicle’s kinematics formu- lated in an absolute velocities context. Cascaded nonlinear systems theory is employed to show stability of the total system, including the guidance system and the heading and velocity controllers. Then the approach is extended so as to account for the influence of ocean currents. This is achieved by combining the guidance law formulated in absolute velocity kinematics with the indirect adaptive control scheme from Chapter 9 . In this way, it is possible to guar- antee the estimation of all the parameters of the current in two dimensions, that is velocity and orientation w.r.t. the inertial frame, instead of just the effect of the current in a specific direction. The guidance system developed in absolute velocities is based on [ 159 ], whereas the formulation using relative velocities is unpublished.
concluded that autoethnography was the best fit. Caroline Ellis, Tony Adams and Arthur Bochner (2011) describe autoethnography is an approach to research that seeks to describe and analyze personal experience in order to understand cultural experience. As a method, they explain, autoethnography is a combination of autobiography and ethnography where researchers “retrospectively and selectively write about epiphanies that stem from, or are made possible by, being part of a culture and/or by possessing a particular cultural identity” (2011, 2.). Further they explained that co-constructed narratives as a form of autoethnography illustrates “the meanings of relational experiences, particularly how people collaboratively cope with the ambiguities, uncertainties, and contradictions of being friends, family, and/or intimate partners” (2011, 4.1) These descriptions resonated deeply with our desire to find a research method that would allow us to explore the implications of the culture that was formed in our learning community for generating hope and agency. We wanted to retrospectively reflect on, analyze and consider the effects produced in the culture of our learning community, and we wanted to do it together. We did not want it to be done to us. With a degree of postcolonial sensibilities, we placed judgments about validity and reliability in our own hands, and in our own reflections and analyses.
The market entrance of shared autonomous vehicles (SAV) may have disruptive effects on current transport systems and may lead to their total transformation. For many small and medium-sized cities, a full replacement of public transport services by these systems seems to be possible. For a transport system operator, such a system requires a bigger fleet of vehicles than before, however, vehicles are less expensive and fewer staff is needed for the actual operation. In this paper, we are using a simulation-based approach to evaluate the service quality and operating cost of a demand responsive transit (DRT) system for the city of Cottbus (100 000 inhabitants), Germany. The simulation model used is based on an existing MATSim model of the region that depicts a typical work day. Results suggest, that the current public transport system may be replaced by a system of 300 to 400 DRT vehicles, depending on their operational mode. Compared to previous, schedule based public transport, passengers do not need to transfer, and their overall travel times may be reduced significantly. Results for the cost comparison are preliminary, but results suggest that an autonomous DRT system is not necessarily more expensive than the current public transport system.
Various people’s contributions have been used to build and maintain linguistic resources. Wiktionary adopted crowdsourcing to build and maintain its content (Meyer & Gurevych 2012), and this allows the collection of data in a fast and cheap manner. However, the quality of the work produced by this method might be undermined by workers who are interested in the number of tasks completed rather than in the quality of the results (Eickhoff & de Vries 2013). Nevertheless, according to Morita and Ishida (2009), collaborative translation produces high-quality results. In order to successfully employ the metaphor of collaboration, we need to design systems that facilitate communication between people and organize them in teams with a range of expertise (Kittur et al. 2013). Furthermore, people should identify themselves with the group they collaborate with and believe that their effort is impor- tant for the community (Rashid et al. 2006; Munro 2010).
There are unusual challenges in ethics for RAS. Perhaps the issue can best be summarised as needing to consider “technically informed ethics”. The technology of RAS raises issues that have an ethical dimension, and perhaps uniquely so due to the possibility of moving human decision-making which is implicitly ethically informed to computer systems. Further, if seeking solutions to these problems – ethically aligned design, to use the IEEE’s terminology – then the solutions must be technically meaningful, capable of realisation, capable of assurance, and suitable as a basis for regulation.
Autonomous Underwater Vehicle (AUV) is capable of performing point-to-point data collection underneath the thick winter ice sheet. The advantages of AUV instead of remote operable vehicles (ROVs) or towed unmanned vehicles are the lower cost involved and a better quality during the inspection missions. Modeling, system identification and control of these vehicles are still major active areas of research and development. The design and development of the vehicle consisted of ctrical system, as well as the integration of subsystems. The present work discusses the sensor and communication systems of a typical AUV. The overall design
(http://caffe.berkeleyvision.org/)). However, these libraries have not been developed using the rigorous processes one would associate with safety-critical software. Furthermore, the role of these frameworks in typical system architectures means it is difficult, if not impossible, to sandbox them and treat them as Software Of Uncertain Provenance (SOUP). It should be noted that this is, in effect, a marketplace problem rather than a technical one. Perhaps in the future we will see “certified” machine learning frameworks, in the same way there are “certified” op- erating systems today.
 Tsikalakis, A. and Hatziargyriou, N. (2011) Centralized Control for Optimizing Mi- crogrids Operation. Proceedings of the IEEE Power and Energy Society General Meeting , San Diego, 24-29 July 2011, 1-8. https://doi.org/10.1109/PES.2011.6039737  Hassan, M.A., Worku, M.Y. and Abido, M.A. (2018) Optimal Design and Real Time Implementation of Autonomous Microgrid Including Active Load. Energies Jour- nal , 11, 1109. https://doi.org/10.3390/en11051109
Transient thermography is a method used successfully in the evaluation of composite materials and aerospace structures. It has the capacity to deliver both qualitative and quantitative results about hidden defects or features in a composite structure. Aircraft must undergo routine maintenance – inspection to check for any critical damage and thus to ensure its safety. This work aims to address the challenge of NDT automated inspection and improve the defects’ detection by suggesting an autonomous thermographic imaging approach using a UAV (Unmanned Aerial Vehicle) active thermographic system. The concept of active thermography is discussed and presented in the inspection of aircraft CFRP panels along with the mission planning for aerial inspection using the UAV for real time inspection. Results indicate that the suggested approach could significantly reduce the inspection time, cost, and workload, whilst potentially increase the probability of detection of defects on aircraft composites.
The final aim of the AVs is to be able to process inputs and make decisions for the action it needs to take, which ranges from assisting the driver to perform a specific function to make all the decisions required to drive itself without any human intervention (Automation Level 0 to 5). At Automation Level 0 output can be seen as guidance or warnings for the driver to prevent risks to safety, but its benefits totally depend on the final action driver decides to take (Winkler, Kazazi, and Vollrath 2018). As Levels of Automation increase, outputs become actions take the form of assistance to the driver. At Levels 1 and 2 the actions taken by the system could be overridden by the human driver, but at higher Automation levels as systems become more reliable the control shifts from the driver to the system.
In some semi-natural environments like fields or orchards, both vision camera and laser range scanner can be used as primary sensors for autonomous navigation. Integration of both machine vision and laser scanner provides more robust guidance for autonomous navigation system of the mobile robot, as well as increases object detection capability. The work of Subramanian et al. (2006) presents an autonomous guidance system based on machine vision and laser radar for guidance and a rotary encoder to provide feedback on the steering angle. The guidance system guided the tractor automatically through straight and curved paths. A combination of laser scanner and camera has been used to develop SLAM-based system (Auat Cheein et al. 2011; Debain et al. 2010). Debain et al. (2010) used Extended Kalman Filter (EKF) for data fusion, whereas Auat Cheein et al. (2011) used Extended Information Filter (EIF) and found that it is more appropriate for real time application because EIF improves the processing time.
Our example programs are really only fragments of some larger program for control of a UA and in each case we have chosen to verify only a single property, demonstrating different ways models of the behaviour of the real world and the planning system can be created in order to allow verification. Obviously a full formal verification of an ethical UA would want to examine the full program, verify against several properties and use the model most appropriate to the full system—i.e., a model based on the construction of the planner, ethical annotation system, and a detailed understanding of the operational environment (all aspects outside the scope of this paper). Our aim has not been to present a verified ethical UA but to demonstrate how our system for reasoning about ethical concerns, can be combined with an existing system in order to verify properties relating to the ethical operation of an autonomous system.