Having been looking in to the subject of future truck interior environment, the first intention was to be inspired of the extreme environment of long haul-age truck driving and improving the user needs. By looking in to the human factors in truck interiors. I wanted to show the benefits of the truck architecture possibilities and recreate it for a new future truck user. By using the main design tools like design research, sketching, full scale projection and quick user testing I could get a quick look into the complexity of current truck driver environment and proceed with developing it further. The final result is a semi-autonomous truck interior that is suited for a new type of future driver. The interior is focused on the user needs and tasks such as; autonomous management of logistics, operating routes with other truck drivers and units, enjoying spare time while not driving and also the possibility to actively taking over the control manually in case of emergency. By separating and dividing the truck interior in different divisions and user modes, such as Operational, Tactical and Strategic, that divides the specific functions above, the architecture can maintain a clear separation between work and leisure for the driver when he or she is driving or not. This way the driver could easily switch between the modes to reduce the cognitive impact of increasing future information cognitive impact, without losing the sense of control and create a safe and comfortable work environment for herself. The concept is based on the knowledge of a flexible future context that is facing the automotive industry by reducing ergonomic impact for the drivers and improving it further through the flexibility to switch between modes.

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The world is becoming more complicated; our lives develop into multiple facets. This master thesis focuses on defining a future typology in vehicle design representing a hybrid lifestyle. New technologies bring convenience to people but sometimes the amount of information exceeds our needs. Jeep Black Label is designed to escape all that in the year 2040. An unplugged lounge experience for the city and a great analogue getaway into nature. A holistic research method was used to understand the context for this vehicle. The design process followed an inside-out approach. First a dynamic interior space was generated based on users’ needs. The nal step was to ideate and choose a meaningful exterior appearance following the goal to communicate brand identity, automation and hybrid driving modes.

The use of autonomous UAV’s is a progressively expanding industry. This thesisfocuses on the landing procedure with the main goal to be independent of visualaids. That means that the landing site can be hidden from the air, the landingcan be done in bad weather conditions and in the dark. In this thesis the use ofradio signals is investigated as an alternative to the visual sensor based systems.A localization system is needed to perform the landing without knowing wherethe landing site is. In this thesis an Extended Kalman Filter (EKF) is derived andused for the localization, based on the received signal strength from a radio beaconat the landing site. There are two main goals that are included in the landing,to land as accurate and as fast as possible. To combine these two goals a simultaneoustrajectory optimization and target estimation problem is set up that can bepartially solved while flying. The optimal solution to this problem produces thepath that the UAV will travel to get the best target localization while still reachingthe target. It is shown that trying to move directly towards the estimated landingsite is not the best strategy. Instead, the optimal trajectory is a spiral that jointlyoptimizes the information from the sensors and minimizes the arrival time.

Autonomous vehicle technology is advancing at a very high pace and self-driving trucks on control-tower operated work sites is already a reality. These autonomous trucks need a highly accurate map of the surroundings for operation and navigation, and it is of great importance to be able to update that map with the ever-changing off-road work site. The autonomous fleet examined have to stop for every update of the site map, which induces unnecessary downtime when updating the site map frequently. The purpose of this work is to contribute to the development of safe configurable maps for autonomous vehicles on off-road sites by identifying and analyzing different map updating methods, proposing the best one, and suggesting how to implement it for this project's case. The result was five different map updating methods, which were evaluated with respect to efficiency and safety. Efficiency was evaluated by comparing total fleet downtime of the proposed solutions with the existing situation. Safety was evaluated by doing a fault tree analysis (FTA) for each proposed solution and comparing the relative size of the fault trees. Proposed Solution III using map tiles was chosen as the most appropriate method to implement for this project's case because it is both efficient and relatively simple. It divides the site map with a grid into smaller rectangular maps and only needs to stop vehicles which are inside the updated tile. The rest of the fleet is able to replace that tile parallel to operation and, therefore, total fleet downtime is significantly reduced. By reaching the stated goal, this work is in line with its original purpose and has contributed to the development of safe configurable maps for autonomous vehicles on off-road sites.

"Autonomous vehicle is an engineering technology that can improve transportation safety, alleviate traffic congestion and reduce carbon emissions. Research on autonomous vehicles can be categorized by functionality, for example, object detection or recognition, path planning, navigation, lane keeping, speed control and driver status monitoring. The research topics can also be categorized by the equipment or techniques used, for example, image processing, computer vision, machine learning, and localization. This dissertation primarily reports on computer vision and machine learning algorithms and their implementations for autonomous vehicles. The vision-based system can effectively detect and accurately recognize multiple objects on the road, such as traffic signs, traffic lights, and pedestrians. In addition, an autonomous lane keeping system has been proposed using end-to-end learning. In this dissertation, a road simulator is built using data collection and augmentation, which can be used for training and evaluating autonomous driving algorithms. The Graphic Processing Unit (GPU) based traffic sign detection and recognition system can detect and recognize 48 traffic signs. The implementation has three stages: pre-processing, feature extraction, and classification. A highly optimized and parallelized version of Histogram of Oriented Gradients (HOG) and Support Vector Machine (SVM) is used. The system can process 27.9 frames per second with the active pixels of a 1,628 by 1,236 resolution, and with the minimal loss of accuracy. In an evaluation using the BelgiumTS dataset, the experimental results indicate that the detection rate is about 91.69% with false positives per window of 3.39e-5, and the recognition rate is about 93.77%. We report on two traffic light detection and recognition systems. The first system detects and recognizes red circular lights only, using image processing and SVM. Its performance is better than that of traditional detectors and it achieves the best performance with 96.97% precision and 99.43% recall. The second system is more complicated. It detects and classifies different types of traffic lights, including green and red lights in both circular and arrow forms. In addition, it employs image processing techniques, such as color extraction and blob detection to locate the candidates. Subsequently, a pre-trained PCA network is used as a multi-class classifier for obtaining frame-by-frame results. Furthermore, an online multi-object tracking technique is applied to overcome occasional misses and a forecasting method is used to filter out false positives. Several additional optimization techniques are employed to improve the detector performance and to handle the traffic light transitions. A multi-spectral data collection system is implemented for pedestrian detection, which includes a thermal camera and a pair of stereo color cameras. The three cameras are first aligned using trifocal tensor, and the aligned data are processed by using computer vision and machine learning techniques. Convolutional channel features (CCF) and the traditional HOG+SVM approach are evaluated over the data captured from the three cameras. Through the use of trifocal tensor and CCF, training becomes more efficient. The proposed system achieves only a 9% log-average miss rate on our dataset. Autonomous lane keeping system employs an end- to-end learning approach for obtaining the proper steering angle for maintaining a car in a lane. The convolutional neural network (CNN) model uses raw image frames as input, and it outputs the steering angles corresponding to the input frames. Unlike the traditional approach, which manually decomposes the problem into several parts, such as lane detection, path planning, and steering control, the model learns to extract useful features on its own and learns to steer from human behavior. More importantly, we find that having a simulator for data augmentation and evaluation is important. We then build the simulator using image projection, vehicle dynamics, and vehicle trajectory tracking. The test results reveal that the model trained with augmented data using the simulator has better performance and achieves about a 98% autonomous driving time on our dataset. Furthermore, a vehicle data collection system is developed for building our own datasets from recorded videos. These datasets are used in the above studies and have been released to the public for autonomous vehicle research. The experimental datasets are available at http://computing.wpi.edu/Dataset.html."

Cells defend their cytosol against pathogen invasion using cell-autonomous immunity. When pathogens enter the cytosol they can damage host endomembranes, causing the mislocalisation of host molecules not normally found in the cytosol that are sensed as Danger Associated Molecular Patterns (DAMPs). Glycans exposed on damaged endomembranes are detected by danger receptors such as Galectin8. Galectin8 is recognised by the autophagy cargo receptor NDP52, specifically targeting the bacteria to autophagy. I hypothesised that other proteins would also be recruited to damaged endomembranes, which may initiate downstream mechanisms involved in cell-autonomous immunity or endomembrane repair. Identifying novel damage recruited proteins (DRPs) is difficult due to the short-lived and dynamic nature of damaged endomembranes. Therefore, I developed an unbiased approach for the identification of novel DRPs by proximity-dependent biotinylation using the ascorbate peroxidise enzyme APEX. This approach preferentially labels proteins located at damaged endomembranes for subsequent identification by TMT mass spectrometry. Four enriched proteins CCDC50, FBXO21, STAMBP and PDCD6 were identified as novel damage recruited proteins, recognising damaged SCVs. An alternative form of cell-autonomous immunity is the induction of cell death, for example by pyroptosis. Cell death destroys the bacteria's replicative niche and exposes them to the extracellular space where they may be phagocytosed. I hypothesised that host cells might tag cytoplasmic bacteria with intracellular opsonins to assist in their phagocytosis following their release from host cells. However, my work revealed that intracellular Salmonella Typhimurium acquire phagocytosis protection, thus becoming internalised by phagocytes less efficiently than control bacteria. Phagocytosis protection was acquired rapidly after S.Typhimurium infection and was not observed with dead bacteria. Phagocytosis protection is only partially reversed by opsonisation in human serum. My results indicate that intracellular S.Typhimurium-induces an evasion mechanism to prevent its subsequent recognition by extracellular phagocytes.

The automation of navigation for different kinds of vehicles is a research problem of great interest. This problem has applications with unmanned aerial vehicles (UAVs) as well as manned vehicles such as cars and planes. The goal of an autonomous vehicle is to navigate safely from one point to another given a set of high-level instructions and data from a set of sensors. This thesis explores an implementation of a modular approach for autonomously driving taxiing planes before proposing methods for object tracking using a LIDAR sensor which can be incorporated into the autonomous driving pipeline. The taxiing algorithm regresses waypoints for the plane to follow given a high-level driving goal such as ”turn left” or ”go straight”, along with RGB images taken from the cockpit and wings. Waypoints are then used with a separate control system to taxi the plane. The training and testing of this autonomous aircraft is done in a photo-realistic simulator which has been adapted for this task. The policy developed in this fashion is capable of learning how to go straight and how to turn. However, the driving policy is not trained to react to other moving objects. To address this issue, and due to the superior reliability of LIDAR over RGB sensors, an object tracking method using only LIDAR point clouds is proposed. The proposed method uses a novel 3D Siamese network to obtain a similarity score between a model and candidate object point clouds. This similarity score is shown to work for tracking by applying it using an exhaustive search and obtaining improved performances when compared with simple baselines. For a realistic application, the similarity score is applied using candidates provided by a search on the BEV projection of the LIDAR point cloud. This method is shown to provide improved tracking results over other search strategies when using a lower number of candidates.

Giving robots the ability to move around autonomously in various real-world environments has long been a major challenge for Artificial Intelligence. New approaches to the design and control of autonomous robots have shown the value of drawing inspiration from the natural world. Animals navigate, perceive and interact with various uncontrolled environments with seemingly little effort. Flying insects, in particular, are quite adept at manoeuvring in complex, unpredictable and possibly hostile environments.Inspired by the miniature machine view of insects, this thesis contributes to the autonomous control of mobile robots through the application of insect-based visual cues and behaviours. The parsimonious, yet robust, solutions offered by insects are directly applicable to the computationally restrictive world of autonomous mobile robots. To this end, two main navigational domains are focussed on: corridor guidance and visual homing.Within a corridor environment, safe navigation is achieved through the application of simple and intuitive behaviours observed in insect, visual navigation. By observing and responding to observed apparent motions in a reactive, yet intelligent way, the robot is able to exhibit useful corridor guidance behaviours at modest expense. Through a combination of both simulation and real-world robot experiments, the feasibility of equipping a mobile robot with the ability to safely navigate in various environments, is demonstrated.It is further shown that the reactive nature of the robot can be augmented to incorporate a map building method that allows previously encountered corridors to be recognised, through the observation of landmarks en route. This allows for a more globally-directed navigational goal.Many animals, including insects such as bees and ants, successfully engage in visual homing. This is achieved through the association of ++ / visual landmarks with a specific location. In this way, the insect is able to 'home in' on a previously visited site by simply moving in such a way as to maximise the match between the currently observed environment and the memorised 'snapshot' of the panorama as seen from the goal. A mobile robot can exploit the very same strategy to simply and reliably return to a previously visited location.This thesis describes a system that allows a mobile robot to home successfully. Specifically, a simple, yet robust, homing scheme that relies only upon the observation of the bearings of visible landmarks, is proposed. It is also shown that this strategy can easily be extended to incorporate other visual cues which may improve overall performance.The homing algorithm described, allows a mobile robot to home incrementally by moving in such a way as to gradually reduce the discrepancy between the current view and the view obtained from the home position. Both simulation and mobile robot experiments are again used to demonstrate the feasibility of the approach.

University of Technology, Sydney. Faculty of Information Technology. / This thesis establishes Virtual Institutions as a comprehensive software engineering technology for the development of 3D Virtual Worlds that require normative regulation of participants’ interactions (such as the commercially-oriented Virtual Worlds). 3D Virtual Worlds technology currently offers somewhat unregulated environments without means to enforce norms of behavior and interaction rules on their inhabitants. Furthermore, existing methodologies for Virtual Worlds development focus primarily on the design side of the “look-and-feel” of the inhabited space. Consequently, in current 3D Virtual Worlds it is difficult to keep track of the deviant behavior of participants and to guarantee a high level of security and predictable overall behavior of the system. The Virtual Institutions Methodology proposed by this dissertation is focused on designing highly secure heterogeneous Virtual Worlds (with humans and autonomous agents participating in them), where the participants behave autonomously and make their decisions freely within the limits imposed by the set of norms of the institution. It is supported by a multilayer model and representational formalisms, and the corresponding tools that facilitate rapid development of norm-governed Virtual Worlds and offer full control over stability and security issues. An important part of the Virtual Institutions Methodology is concerned with the relationship between humans and autonomous agents. In particular, the ways to achieve human-like behavior by learning such behavior from the humans themselves are investigated. It is explained how formal description of the interaction rules together with full observation of the users’ actions help to improve the human-like believability of autonomous agents in Virtual Institutions. The thesis proposes the concept of implicit training, which enables the process of teaching autonomous agents human characteristics without any explicit training efforts required from the humans, and develops the computational support for this new learning method. The benefits of using Virtual Institutions are illustrated through applying this technology to the domain of E-Commerce. It is demonstrated that providing shoppers with a normative environment that offers immersive experience and supports important real world attributes like social interaction, location awareness, advanced visualization, collaborative shopping and impulsive purchases can improve existing practices in E-Commerce portals.