1,288 research outputs found

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

    Get PDF
    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Optimal speed trajectory and energy management control for connected and automated vehicles

    Get PDF
    Connected and automated vehicles (CAVs) emerge as a promising solution to improve urban mobility, safety, energy efficiency, and passenger comfort with the development of communication technologies, such as vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I). This thesis proposes several control approaches for CAVs with electric powertrains, including hybrid electric vehicles (HEVs) and battery electric vehicles (BEVs), with the main objective to improve energy efficiency by optimising vehicle speed trajectory and energy management system. By types of vehicle control, these methods can be categorised into three main scenarios, optimal energy management for a single CAV (single-vehicle), energy-optimal strategy for the vehicle following scenario (two-vehicle), and optimal autonomous intersection management for CAVs (multiple-vehicle). The first part of this thesis is devoted to the optimal energy management for a single automated series HEV with consideration of engine start-stop system (SSS) under battery charge sustaining operation. A heuristic hysteresis power threshold strategy (HPTS) is proposed to optimise the fuel economy of an HEV with SSS and extra penalty fuel for engine restarts. By a systematic tuning process, the overall control performance of HPTS can be fully optimised for different vehicle parameters and driving cycles. In the second part, two energy-optimal control strategies via a model predictive control (MPC) framework are proposed for the vehicle following problem. To forecast the behaviour of the preceding vehicle, a neural network predictor is utilised and incorporated into a nonlinear MPC method, of which the fuel and computational efficiencies are verified to be effective through comparisons of numerical examples between a practical adaptive cruise control strategy and an impractical optimal control method. A robust MPC (RMPC) via linear matrix inequality (LMI) is also utilised to deal with the uncertainties existing in V2V communication and modelling errors. By conservative relaxation and approximation, the RMPC problem is formulated as a convex semi-definite program, and the simulation results prove the robustness of the RMPC and the rapid computational efficiency resorting to the convex optimisation. The final part focuses on the centralised and decentralised control frameworks at signal-free intersections, where the energy consumption and the crossing time of a group of CAVs are minimised. Their crossing order and velocity trajectories are optimised by convex second-order cone programs in a hierarchical scheme subject to safety constraints. It is shown that the centralised strategy with consideration of turning manoeuvres is effective and outperforms a benchmark solution invoking the widely used first-in-first-out policy. On the other hand, the decentralised method is proposed to further improve computational efficiency and enhance the system robustness via a tube-based RMPC. The numerical examples of both frameworks highlight the importance of examining the trade-off between energy consumption and travel time, as small compromises in travel time could produce significant energy savings.Open Acces

    Automated identification and behaviour classification for modelling social dynamics in group-housed mice

    Get PDF
    Mice are often used in biology as exploratory models of human conditions, due to their similar genetics and physiology. Unfortunately, research on behaviour has traditionally been limited to studying individuals in isolated environments and over short periods of time. This can miss critical time-effects, and, since mice are social creatures, bias results. This work addresses this gap in research by developing tools to analyse the individual behaviour of group-housed mice in the home-cage over several days and with minimal disruption. Using data provided by the Mary Lyon Centre at MRC Harwell we designed an end-to-end system that (a) tracks and identifies mice in a cage, (b) infers their behaviour, and subsequently (c) models the group dynamics as functions of individual activities. In support of the above, we also curated and made available a large dataset of mouse localisation and behaviour classifications (IMADGE), as well as two smaller annotated datasets for training/evaluating the identification (TIDe) and behaviour inference (ABODe) systems. This research constitutes the first of its kind in terms of the scale and challenges addressed. The data source (side-view single-channel video with clutter and no identification markers for mice) presents challenging conditions for analysis, but has the potential to give richer information while using industry standard housing. A Tracking and Identification module was developed to automatically detect, track and identify the (visually similar) mice in the cluttered home-cage using only single-channel IR video and coarse position from RFID readings. Existing detectors and trackers were combined with a novel Integer Linear Programming formulation to assign anonymous tracks to mouse identities. This utilised a probabilistic weight model of affinity between detections and RFID pickups. The next task necessitated the implementation of the Activity Labelling module that classifies the behaviour of each mouse, handling occlusion to avoid giving unreliable classifications when the mice cannot be observed. Two key aspects of this were (a) careful feature-selection, and (b) judicious balancing of the errors of the system in line with the repercussions for our setup. Given these sequences of individual behaviours, we analysed the interaction dynamics between mice in the same cage by collapsing the group behaviour into a sequence of interpretable latent regimes using both static and temporal (Markov) models. Using a permutation matrix, we were able to automatically assign mice to roles in the HMM, fit a global model to a group of cages and analyse abnormalities in data from a different demographic

    20th SC@RUG 2023 proceedings 2022-2023

    Get PDF

    Evaluating Architectural Safeguards for Uncertain AI Black-Box Components

    Get PDF
    Although tremendous progress has been made in Artificial Intelligence (AI), it entails new challenges. The growing complexity of learning tasks requires more complex AI components, which increasingly exhibit unreliable behaviour. In this book, we present a model-driven approach to model architectural safeguards for AI components and analyse their effect on the overall system reliability

    Towards a holistic understanding of the role of green infrastructure in improving urban air quality

    Get PDF
    Air pollution has been identified as a major problem in modern societies, threatening urban population health. Pedestrians, in particular, are directly exposed to one of the main sources of air pollutants: road transport, which is concentrated in proximity to the road, worsening the air. Green infrastructure (GI) has been promoted as a natural method for reducing exposure to local street air pollutants and providing additional Ecosystem Services with a range of environmental, social and economic benefits for citizens. The effectiveness of GI for improving air quality depends on the spatio-temporal context and the species-specific characteristics of the GI. Urban planting could maximise this benefit by a holistic understanding of the effects of GI in cities, balancing its benefits and constraints. However, little is currently known about the application of GI design and planning with regard to air pollution mitigation. Moreover, there is little agreement on the quantifiable effectiveness of GI in improving street air quality as its effectiveness is highly context dependent. Holistic guidance is therefore needed to inform practitioners of site- and species- specifics, trade-offs, and GI maintenance considerations for successful urban planting. This research reviews the academic literature addressing GI-related characteristics in streets, creating a holistic framework to help guide decision-makers on using GI solutions to improve air quality. Additionally, this research aims to understand how and which GI, along with other local characteristics, influence pedestrian air quality and how these characteristics are considered in real-world practice within the United Kingdom. This research progresses through three stages: First, the mechanisms by which GI is considered to influence air quality were identified through literature reviews. A specific literature review was then conducted for each mechanism to extract the associated GI and spatial characteristics that affect the potential for GI to mitigate urban air pollution. In the second stage, this list of characteristics, together with other Ecosystem Services, was discussed in consultation with practitioners in the UK. A survey was conducted to explore and evaluate the recommendations and resources available for planning plantings, as well as the practitioners’ knowledge about the characteristics associated with mitigating air pollution. Supported by results from the survey and the literature reviews, the third stage evaluated (validated) an easy-to-use computational model for its potential use in improving planting decisions for air pollution mitigation. Green infrastructure influences air quality by providing surfaces for pollutant deposition and absorption, effects on airflow and dispersion, and biogenic emissions. The relationship between the specific GI and the spatio-temporal context also influences air quality. Street structure, weather variables, and the type, shape and size of GI influence the dispersion of pollutants, with micro-and macro-morphological traits additionally influencing particulate deposition and gas absorption. In addition, maintaining GI lessens air quality deterioration by controlling biogenic emissions. According to participants in the survey, aesthetics were the principal drivers of urban planting, followed by improving well-being and increasing biodiversity and air pollution mitigation as a lesser priority. Characteristics such as airflow manipulation, leaf surface traits, and biogenic emissions were the less important influences in planting decisions in the UK, despite the fact that these characteristics influence air quality. Perhaps, a lack of communication of current information and low confidence about which specific characteristics have a tangible effect on air quality reduces the incorporation of GI for air pollution mitigation purposes. Uncertainties exist about the quantification of pollutants removed by GI. Field campaigns and computational models still need improvement to address the effectiveness of GI in real-world environments adequately and also to understand whether GI can exert a significant effect on pollutant levels under real-world conditions. This research showed that a promising and easy-to-use model used to evaluate the effectiveness of trees in removing particles was not an acceptable model to study the effect of GI on streets. The validation results showed a poor agreement between wind tunnel data and the model results. More effort is needed to develop better modelling tools that can quantify the actual effect of GI on improving street air quality. This research contributes to the air pollution mitigation field, explicitly helping to inform decision-making for more health-promoting urban settings by optimising the expected benefits of GI through a holistic understanding of their impacts. Facilitating the communication of current evidence through a holistic guide that considers both the benefits and trade-offs of planting decisions for air quality improvement. Improving information on air pollution mitigation to feed the decision-making process might maximise the benefits of GI planting for air pollution mitigation in streets.Open Acces

    Chatbots for Modelling, Modelling of Chatbots

    Full text link
    Tesis Doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Ingeniería Informática. Fecha de Lectura: 28-03-202
    • …
    corecore