Human–Machine Interface in Transport Systems: An Industrial Overview for More Extended Rail Applications

This paper provides an overview of Human Machine Interface (HMI) design and command systems in commercial or experimental operation across transport modes. It presents and comments on different HMIs from the perspective of vehicle automation equipment and simulators of different application domains. Considering the fields of cognition and automation, this investigation highlights human factors and the experiences of different industries according to industrial and literature reviews. Moreover, to better focus the objectives and extend the investigated industrial panorama, the analysis covers the most effective simulators in operation across various transport modes for the training of operators as well as research in the fields of safety and ergonomics. Special focus is given to new technologies that are potentially applicable in future train cabins, e.g., visual displays and haptic-shared controls. Finally, a synthesis of human factors and their limits regarding support for monitoring or driving assistance is propose

Perception Enhanced Virtual Environment for Maritime Applications

This paper presents the development of a realtimeperception enhanced virtual environment for maritimeapplications which simulates real-time six degrees of freedomship motions (pitch, heave, roll, surge, sway, and yaw) underuser interactions, environmental conditions and various threatscenarios. This simulation system consists of reliable shipmotion prediction system and perception enhanced immersivevirtual environment with greater ecological validity. Thisvirtual environment supports multiple-display viewing that cangreatly enhance user perception and we developed the ecologicalenvironment for strong sensation of immersion. In this virtualenvironment it is possible to incorporate real world ships,geographical sceneries, several environmental conditions andwide range of visibility and illumination effects. This system canbe used for both entertainment and educational applications suchas consol level computer games, teaching & learning applicationsand various virtual reality applications. Especially this framework can be used to create immersive multi user environments

Close Formation Flight Missions Using Vision-Based Position Detection System

In this thesis, a formation flight architecture is described along with the implementation and evaluation of a state-of-the-art vision-based algorithm for solving the problem of estimating and tracking a leader vehicle within a close-formation configuration. A vision-based algorithm that uses Darknet architecture and a formation flight control law to track and follow a leader with desired clearance in forward, lateral directions are developed and implemented. The architecture is run on a flight computer that handles the process in real-time while integrating navigation sensors and a stereo camera. Numerical simulations along with indoor and outdoor actual flight tests demonstrate the capabilities of detection and tracking by providing a low cost, compact size and low weight solution for the problem of estimating the location of other cooperative or non-cooperative flying vehicles within a formation architecture

An Innovative Human Machine Interface for UAS Flight Management System

The thesis is relative to the development of an innovative Human Machine Interface for UAS Flight Management System. In particular, touchscreena have been selected as data entry interface. The thesis has been done together at Alenia Aermacch

An Airspace Simulator for Separation Management Research

Air Traffic Management (ATM) systems are undergoing a period of major transformation and modernisation, requiring and enabling new separation management (SM) methods. Many novel SM functions, roles and concepts are being explored using ATM simulators. Commercial simulators are capable, high-fidelity tools, but tend to be complex and inaccessible. The Airspace Simulator is a fast-time, discrete event simulator originally designed for exploratory ATM research. This thesis describes the redevelopment of the Airspace Simulator into a simulation platform better suited for researching and evaluating SM in future airspace. The Airspace Simulator-II has the advantage of new functionality and greater fidelity, while remaining high-speed, accessible and readily adaptable. The simulator models FMS-like spherical earth navigation and autopilot flight control with an average cross track error of 0.05 nmi for waypoint-defined routes in variable wind-fields. Trajectories are computed using the BADA v3.8 tabulated database to model the performance of 318 aircraft types. The simulator was demonstrated with up to 4000 total aircraft, and trajectories for 300 simultaneous aircraft were computed over 900 times faster than real-time. Datalink and radio-telephony communications are modelled between the air traffic and ATM systems. Surveillance is provided through ADS-B-like broadcasts, and an algorithm was developed to automatically merge instructions from conflict resolution systems with existing flight plans. Alternate communication, navigation, and separation modes were designed to permit the study of mixed-mode operations. Errors due to wind, navigational wander, communication latencies, and localised information states are modelled to facilitate research into the robustness of SM systems. The simulator incorporates a traffic visualisation tool and was networked to conflict detection and resolution software through a TCP/IP connection. A scenario generator was designed to automatically prepare flight plans for a large variety of two-aircraft encounters to support stochastic SM experiments. The simulator, scenario generator, and resolver were used for the preliminary analysis of a novel concept for automated SM over radio-telephony using progressive track angle vectoring

Design and implementation of a protocol for safe cooperation of self-driving cars

Tese de mestrado, Engenharia Informática (Arquitetura, Sistemas e Redes de Computadores) Universidade de Lisboa, Faculdade de Ciências, 2019Ever since its introduction, the car has fundamentally changed our society. Its popularity grew tremendously in the early 20th century, and today it is nearly ubiquitous. However, there are several problems related to automobiles, one of the major ones being road congestion. Drivers lose millions of dollars every year in fuel costs and time spent in day to day traffic congestion. Another major problem is emissions from vehicles, forming a significant percentage of greenhouse gas emissions. Automated driving systems currently rely on their own sensors to gather information from the real-world, and make informed decisions to keep their passengers safe. But sensors may not be sufficiently accurate during all conditions and can even fail, so automated driving systems take this into consideration when controlling the car, leading to larger safety margins. Vehicle-to-Vehicle communication can enable cooperation between vehicles which, among other things, can be sending or receiving information from other nearby vehicles, increasing the confidence level in the information gathered or even gathering information otherwise not obtainable. Cooperation opens the door to complex vehicular applications such as road-trains (or platooning) and virtual traffic lights, both of which have the potential to mitigate the problems mentioned before. These applications have tight safety requirements due to the context in which they operate: vehicles, possibly with human occupants, operating on roads traversed by non-autonomous vehicles and pedestrians. In this MSc Dissertation, we describe the implementation of a vehicular cooperation algorithm backed by both vehicle-to-vehicle communication and a cloud membership service. We then evaluate the implemented algorithm in a cooperative environment to conclude about its correctness, making use of the Robot Operating System middleware to implement a simulation and visualize a maneuver executed using the algorithm

Using learning from demonstration to enable automated flight control comparable with experienced human pilots

Modern autopilots fall under the domain of Control Theory which utilizes Proportional Integral Derivative (PID) controllers that can provide relatively simple autonomous control of an aircraft such as maintaining a certain trajectory. However, PID controllers cannot cope with uncertainties due to their non-adaptive nature. In addition, modern autopilots of airliners contributed to several air catastrophes due to their robustness issues. Therefore, the aviation industry is seeking solutions that would enhance safety. A potential solution to achieve this is to develop intelligent autopilots that can learn how to pilot aircraft in a manner comparable with experienced human pilots. This work proposes the Intelligent Autopilot System (IAS) which provides a comprehensive level of autonomy and intelligent control to the aviation industry. The IAS learns piloting skills by observing experienced teachers while they provide demonstrations in simulation. A robust Learning from Demonstration approach is proposed which uses human pilots to demonstrate the task to be learned in a flight simulator while training datasets are captured. The datasets are then used by Artificial Neural Networks (ANNs) to generate control models automatically. The control models imitate the skills of the experienced pilots when performing the different piloting tasks while handling flight uncertainties such as severe weather conditions and emergency situations. Experiments show that the IAS performs learned skills and tasks with high accuracy even after being presented with limited examples which are suitable for the proposed approach that relies on many single-hidden-layer ANNs instead of one or few large deep ANNs which produce a black-box that cannot be explained to the aviation regulators. The results demonstrate that the IAS is capable of imitating low-level sub-cognitive skills such as rapid and continuous stabilization attempts in stormy weather conditions, and high-level strategic skills such as the sequence of sub-tasks necessary to takeoff, land, and handle emergencies