ICNavS: A tool for reliable dynamic route guidance

The aim of this paper is to devise a new reliable dynamic route guidance approach by integrating the A* algorithm, the concept of reliability and an existing route guidance method into a single package. A new purpose-developed software tool, the Imperial College Navigation Software (ICNavS), is presented, so as to implement and demonstrate the new approach on a real road network, using simulated data. A summary of the background of the program is given, followed by a procedure developed in order to model the features of real road networks, as well as missing data. Then, a imulation experiment on a part of West London’s road network is carried out and the results are presented

Testing a reliable in-vehicle navigation algorithm in the field

The results of a field experiment carried out to assess the accuracy and efficiency of a new in-vehicle navigation algorithm, whose aim is to incorporate and consider travel time reliability and route the guided vehicle along uncongested roads, in the absence of real-time traffic information are presented. Using historical travel time profiles deduced from floating vehicle data, the algorithm is implemented in a purpose-developed software tool and tested in the London Congestion Charging Zone. The experiment consists of driving a vehicle along routes computed by the algorithm and comparing the outcome with that of a conventional navigation system installed in a second vehicle. The results indicate that the new algorithm outperforms the conventional system in most cases, thus suggesting that it is a step forward towards a more intelligent navigation system

Norms, strategies and political change: explaining the establishment of the convention on the future of Europe

Norms affect political outcomes by shaping the strategies that political actors use to advance their interests. Norms do so by shaping the terms of the debates that underpin political decision making. Unlike existing literature that highlights the importance of persuasion, this article demonstrates that through the mechanism of rhetorical action, norms induce self-interested political actors to adapt their strategy and accept political change that they would normally oppose. The case of the advent of the Convention on the Future of Europe examined here shows that by considering the impact of norms on the behaviour of the opponents of change, ideational analyses can incorporate agency in the explanation of political change

Web 2.0 technologies for learning at Key Stages 3 and 4: summary report

The research project on Web 2.0 technologies for learning at Key Stages 3 and 4 was a major initiative funded by Becta to investigate the use and impact of such technologies in and out of school. The purpose of this research was to help shape Becta's own thinking and inform policy-makers, schools and local authorities on the potential benefits of Web 2.0 technologies and how their use can be effectively and safely realised. This document is he summary of the reports published for this project

Multiple-objective sensor management and optimisation

One of the key challenges associated with exploiting modern Autonomous Vehicle technology for military surveillance tasks is the development of Sensor Management strategies which maximise the performance of the on-board Data-Fusion systems. The focus of this thesis is the development of Sensor Management algorithms which aim to optimise target tracking processes. Three principal theoretical and analytical contributions are presented which are related to the manner in which such problems are formulated and subsequently solved.Firstly, the trade-offs between optimising target tracking and other system-level objectives relating to expected operating lifetime are explored in an autonomous ground sensor scenario. This is achieved by modelling the observer trajectory control design as a probabilistic, information-theoretic, multiple-objective optimisation problem. This novel approach explores the relationships between the changes in sensor-target geometry that are induced by tracking performance measures and those relating to power consumption. This culminates in a novel observer trajectory control algorithm based onthe minimax approach.The second contribution is an analysis of the propagation of error through a limited-lookahead sensor control feedback loop. In the last decade, it has been shown that the use of such non-myopic (multiple-step) planning strategies can lead to superior performance in many Sensor Management scenarios. However, relatively little is known about the performance of strategies which use different horizon lengths. It is shown that, in the general case, planning performance is a function of the length of the horizon over which the optimisation is performed. While increasing the horizon maximises the chances of achieving global optimality, by revealing information about the substructureof the decision space, it also increases the impact of any prediction error, approximations, or unforeseen risk present within the scenario. These competing mechanisms aredemonstrated using an example tracking problem. This provides the motivation for a novel sensor control methodology that employs an adaptive length optimisation horizon. A route to selecting the optimal horizon size is proposed, based on a new non-myopic risk equilibrium which identifies the point where the two competing mechanisms are balanced.The third area of contribution concerns the development of a number of novel optimisation algorithms aimed at solving the resulting sequential decision making problems. These problems are typically solved using stochastic search methods such as Genetic Algorithms or Simulated Annealing. The techniques presented in this thesis are extensions of the recently proposed Repeated Weighted Boosting Search algorithm. In its originalform, it is only applicable to continuous, single-objective, ptimisation problems. The extensions facilitate application to mixed search spaces and Pareto multiple-objective problems. The resulting algorithms have performance comparable with Genetic Algorithm variants, and offer a number of advantages such as ease of implementation and limited tuning requirements

Risk-averse multi-armed bandits and game theory

The multi-armed bandit (MAB) and game theory literature is mainly focused on the expected cumulative reward and the expected payoffs in a game, respectively. In contrast, the rewards and the payoffs are often random variables whose expected values only capture a vague idea of the overall distribution. The focus of this dissertation is to study the fundamental limits of the existing bandits and game theory problems in a risk-averse framework and propose new ideas that address the shortcomings. The author believes that human beings are mostly risk-averse, so studying multi-armed bandits and game theory from the point of view of risk aversion, rather than expected reward/payoff, better captures reality. In this manner, a specific class of multi-armed bandits, called explore-then-commit bandits, and stochastic games are studied in this dissertation, which are based on the notion of Risk-Averse Best Action Decision with Incomplete Information (R-ABADI, Abadi is the maiden name of the author's mother). The goal of the classical multi-armed bandits is to exploit the arm with the maximum score defined as the expected value of the arm reward. Instead, we propose a new definition of score that is derived from the joint distribution of all arm rewards and captures the reward of an arm relative to those of all other arms. We use a similar idea for games and propose a risk-averse R-ABADI equilibrium in game theory that is possibly different from the Nash equilibrium. The payoff distributions are taken into account to derive the risk-averse equilibrium, while the expected payoffs are used to find the Nash equilibrium. The fundamental properties of games, e.g. pure and mixed risk-averse R-ABADI equilibrium and strict dominance, are studied in the new framework and the results are expanded to finite-time games. Furthermore, the stochastic congestion games are studied from a risk-averse perspective and three classes of equilibria are proposed for such games. It is shown by examples that the risk-averse behavior of travelers in a stochastic congestion game can improve the price of anarchy in Pigou and Braess networks. Furthermore, the Braess paradox does not occur to the extent proposed originally when travelers are risk-averse. We also study an online affinity scheduling problem with no prior knowledge of the task arrival rates and processing rates of different task types on different servers. We propose the Blind GB-PANDAS algorithm that utilizes an exploration-exploitation scheme to load balance incoming tasks on servers in an online fashion. We prove that Blind GB-PANDAS is throughput optimal, i.e. it stabilizes the system as long as the task arrival rates are inside the capacity region. The Blind GB-PANDAS algorithm is compared to FCFS, Max-Weight, and c-mu-rule algorithms in terms of average task completion time through simulations, where the same exploration-exploitation approach as Blind GB-PANDAS is used for Max-Weight and c-$\mu$-rule. The extensive simulations show that the Blind GB-PANDAS algorithm conspicuously outperforms the three other algorithms at high loads

Efficient algorithms for risk-averse air-ground rendezvous missions

Demand for fast and inexpensive parcel deliveries in urban environments has risen considerably in recent years. A framework is envisioned to enforce efficient last-mile delivery in urban environments by leveraging a network of ride-sharing vehicles, where Unmanned Aerial Systems (UASs) drop packages on said vehicles, which then cover the majority of the distance before final aerial delivery. By combining existing networks we show that the range and efficiency of UAS-based delivery logistics are greatly increased. This approach presents many engineering challenges, including the safe rendezvous of both agents: the UAS and the human-operated ground vehicle. This dissertation presents tools that guarantee risk-optimal rendezvous between the two vehicles. We present mechanical and algorithmic tools that achieve this goal. Mechanically, we develop a novel aerial manipulator and controller that improves in-flight stability during the pickup and drop-off of packages. At a higher level and the core of this dissertation, we present planning algorithms that mitigate risks associated with human behavior at the longest time scales. First, we discuss the downfalls of traditional approaches. In aerial manipulation, we show that popular anthropomorphic designs are unsuitable for flying platforms, which we tackle with a combination of lightweight design of a delta-type parallel manipulator, and L1 adaptive control with feedforward. In planning algorithms, we present evidence of erratic driver behavior that can lead to catastrophic failures. Such a failure occurs when the UAS depletes its resource (battery, fuel) and has to crash land on an unplanned location. This is particularly dangerous in urban environments where population density is high, and the probability of harming a person or property in the event of a failure is unsafe. Studies have shown that two types of erratic behavior are common: speed variation and route choice. Speed variation refers to a common disregard for speed limits combined with different levels of comfort per driver. Route choice is conscious, unconscious, or purely random action of deviating from a prescribed route. Route choice uncertainty is high dimensional and complex both in space and time. Dealing with these types of uncertainty is important to many fields, namely traffic flow modeling. The critical difference to our interpretation is that we frame them in a motion planning framework. As such, we assume each driver has an unknown stochastic model for their behavior, a model that we aim to approximate through different methods. We aim to guarantee safety by quantifying motion planning risks associated with erratic human behavior. Only missions that plan on using all of the UAS's resources have inherent risk. We postulate that if we have a high assurance of success, any mission can be made to use more resources and be more efficient for the network by completing its objective faster. Risk management is addressed at three different scales. First, we focus on speed variation. We approach this problem with a combination of risk-averse Model Predictive Control (MPC) and Gaussian Processes. We use risk as a measure of the probability of success, centered around estimated future driver position. Several risk measures are discussed and CVaR is chosen as a robust measure for this problem. Second we address local route choice. This is route uncertainty for a single driver in some region of space. The primary challenge is the loss of gradient for the MPC controller. We extend the previous approach with a cross-entropy stochastic optimization algorithm that separates gradient-based from gradient-free optimization problems within the planner. We show that this approach is effective through a variety of numerical simulations. Lastly, we study a city-wide problem of estimating risk among several available drivers. We use real-world data combined with synthetic experiments and Deep Neural Networks (DNN) to produce an accurate estimator. The main challenges in this approach are threefold: DNN architecture, driver model, and data processing. We found that this learning problem suffers from vanishing gradients and numerous local minima, which we address with modern self-normalization techniques and mean-adjusted CVaR. We show the model's effectiveness in four scenarios of increasing complexity and propose ways of addressing its shortcomings

Decision-Aiding and Optimization for Vertical Navigation of Long-Haul Aircraft

Most decisions made in the cockpit are related to safety, and have therefore been proceduralized in order to reduce risk. There are very few which are made on the basis of a value metric such as economic cost. One which can be shown to be value based, however, is the selection of a flight profile. Fuel consumption and flight time both have a substantial effect on aircraft operating cost, but they cannot be minimized simultaneously. In addition, winds, turbulence, and performance x,ary widely with altitude and time. These factors make it important and difficult for pilots to (a) evaluate the outcomes associated with a particular trajectory before it is flown and (b) decide among possible trajectories. The two elements of this problem considered here are (1) determining, what constitutes optimality, and (2) finding optimal trajectories. Pilots and dispatchers from major U.S. airlines were surveyed to determine which attributes of the outcome of a flight they considered the most important. Avoiding turbulence-for passenger comfort topped the list of items which were not safety related. Pilots' decision making about the selection of flight profile on the basis of flight time, fuel burn, and exposure to turbulence was then observed. Of the several behavioral and prescriptive decision models invoked to explain the pilots' choices, utility maximization is shown to best reproduce the pilots' decisions. After considering more traditional methods for optimizing trajectories, a novel method is developed using a genetic algorithm (GA) operating on a discrete representation of the trajectory search space. The representation is a sequence of command altitudes, and was chosen to be compatible with the constraints imposed by Air Traffic Control, and with the training given to pilots. Since trajectory evaluation for the GA is performed holistically, a wide class of objective functions can be optimized easily. Also, using the GA it is possible to compare the costs associated with different airspace design and air traffic management policies. A decision aid is proposed which would combine the pilot's notion of optimility with the GA-based optimization, provide the pilot with a number of alternative pareto-optimal trajectories, and allow him to consider un-modelled attributes and constraints in choosing among them. A solution to the problem of displaying alternatives in a multi-attribute decision space is also presented

Reliable dynamic in-vehicle navigation

Having started off from luxury makes and models, in-vehicle navigation systems are now gradually spreading through the entire vehicle fleet, as drivers appreciate their usefulness. Increasingly sophisticated systems are being developed, having much more advanced functions than simple driving directions. This thesis presents a new approach for in-vehicle navigation, in which travel time reliability is incorporated in the route finding component of the navigation system. Based on historical traffic data and in the absence of current traffic information, positions in the road network at which it is likely to encounter delays, are predicted and avoided as much as possible by the route finding algorithm. The thesis starts by reviewing shortest path algorithms and conjectures that the most appropriate algorithm to use is A*, which forms a vital part of the approach developed. Performing multiple runs of A* forwards and backwards on the road network, efficiency of the route finding procedure is achieved. The time-dependent version of the algorithm is also derived. Then, the thesis goes on to define reliability on a single link of the road network as the maximum delay that can be encountered with 90% confidence and extends this definition to derive the reliability of entire routes. Having introduced the route finding procedure and the concept of reliability, the thesis presents the in-vehicle navigation approach, which involves computing a more reliable route from the driver's origin to his/her destination than the fastest, if this is unreliable. Additionally, the approach aims at computing multiple alternative partially disjoint but equivalently reliable routes to the driver, such that the congestion feedback effect can be avoided as much as possible, without the need of carrying out a dynamic traffic assignment, which would be impracticable in an in-vehicle system. A number of constraints are introduced so as to ensure that the resulting routes are acceptable to the driver (are not too long, etc). Hence, the main concept lies in initially computing the fastest time-dependent route, then applying penalties to the links characterised as unreliable (increasing the link weights in inverse proportion to their reliability) and re-running the route finding algorithm so as to find a more reliable route. After each run, the route obtained is checked against the constraints and if it does not satisfy them, it is discarded, the penalties are reduced and a new route is sought. In order to obtain alternative partially disjoint routes, penalties are also applied to links that are already included in a previously computed and accepted route. The new algorithm, RDIN, is thus presented and mathematically formulated. An extension to RDIN for re-routing, RDIN-R, is also developed. The software tool developed for the application of RDIN and RDIN-R, the Adaptive Reliable Imperial Advanced Navigation Engine (ARIAdNE) is described. A simulation example is given for demonstration and preliminary validation; then a number of field experiments are carried out in Central London to test the method in a real road network environment and to compare its performance with an existing conventional car navigation system. The results suggest that the method is workable and precise, while at the same time it is a promising step forward in the field of in-vehicle navigation