2,864 research outputs found
Flood dynamics derived from video remote sensing
Flooding is by far the most pervasive natural hazard, with the human impacts of floods expected to worsen in the coming decades due to climate change. Hydraulic models are a key tool for understanding flood dynamics and play a pivotal role in unravelling the processes that occur during a flood event, including inundation flow patterns and velocities. In the realm of river basin dynamics, video remote sensing is emerging as a transformative tool that can offer insights into flow dynamics and thus, together with other remotely sensed data, has the potential to be deployed to estimate discharge. Moreover, the integration of video remote sensing data with hydraulic models offers a pivotal opportunity to enhance the predictive capacity of these models.
Hydraulic models are traditionally built with accurate terrain, flow and bathymetric data and are often calibrated and validated using observed data to obtain meaningful and actionable model predictions. Data for accurately calibrating and validating hydraulic models are not always available, leaving the assessment of the predictive capabilities of some models deployed in flood risk management in question. Recent advances in remote sensing have heralded the availability of vast video datasets of high resolution. The parallel evolution of computing capabilities, coupled with advancements in artificial intelligence are enabling the processing of data at unprecedented scales and complexities, allowing us to glean meaningful insights into datasets that can be integrated with hydraulic models. The aims of the research presented in this thesis were twofold. The first aim was to evaluate and explore the potential applications of video from air- and space-borne platforms to comprehensively calibrate and validate two-dimensional hydraulic models. The second aim was to estimate river discharge using satellite video combined with high resolution topographic data. In the first of three empirical chapters, non-intrusive image velocimetry techniques were employed to estimate river surface velocities in a rural catchment. For the first time, a 2D hydraulicvmodel was fully calibrated and validated using velocities derived from Unpiloted Aerial Vehicle (UAV) image velocimetry approaches. This highlighted the value of these data in mitigating the limitations associated with traditional data sources used in parameterizing two-dimensional hydraulic models. This finding inspired the subsequent chapter where river surface velocities, derived using Large Scale Particle Image Velocimetry (LSPIV), and flood extents, derived using deep neural network-based segmentation, were extracted from satellite video and used to rigorously assess the skill of a two-dimensional hydraulic model. Harnessing the ability of deep neural networks to learn complex features and deliver accurate and contextually informed flood segmentation, the potential value of satellite video for validating two dimensional hydraulic model simulations is exhibited. In the final empirical chapter, the convergence of satellite video imagery and high-resolution topographical data bridges the gap between visual observations and quantitative measurements by enabling the direct extraction of velocities from video imagery, which is used to estimate river discharge. Overall, this thesis demonstrates the significant potential of emerging video-based remote sensing datasets and offers approaches for integrating these data into hydraulic modelling and discharge estimation practice. The incorporation of LSPIV techniques into flood modelling workflows signifies a methodological progression, especially in areas lacking robust data collection infrastructure. Satellite video remote sensing heralds a major step forward in our ability to observe river dynamics in real time, with potentially significant implications in the domain of flood modelling science
Walking through the abstract(ed) city and co-creating urban space.
This paper explores how co-designing urban walkability can be augmented by an innovative hybrid approach, whereby virtual records and visualisations of the walking experience can enhance the awareness, perceptions and immersion of the participant in both real and virtual spaces. From one side of that model, the research explores how people might be intrigued enough to discover the real context, based on their experience informed and enriched by parallel images of the city. On the other side, the study aimed to develop a critical understanding of urban walking through the lens of 3D high-definition LIDAR scanning technology, where visualisation techniques were used to support studies to explore how the rich experience of walking could be captured and represented. The paper presents a theoretical framework to propose how walking could be promoted, and positively influenced by the urban environment, by regarding the city from the abstract perspective of the virtual point cloud. The research has investigated how and whether a place – real and abstracted - could act as a trigger to produce novel ideas and unfold thoughts in a participatory way. The interlinkages between motion and (visual) perception of the environment as an aesthetic experience were critical to informing how digital technology can be utilised as a virtual space within which the richness of real interactions and experiences with urban space can be represented, refined, interacted with and used within a rich(er) process of co-design
A Survey on Imitation Learning Techniques for End-to-End Autonomous Vehicles
Funding Agency: 10.13039/100016335-Jaguar Land Rover 10.13039/501100000266-U.K. Engineering and Physical Sciences Research Council (EPSRC) (Grant Number: EP/N01300X/1) jointly funded Towards Autonomy: Smart and Connected Control (TASCC) ProgramPeer reviewedPostprin
Open or Closed? Measurement Performance of Open- and Closed-Path Methane Sensors for Mobile Emissions Screening
Ground-based vehicle systems are being increasingly used by industry, regulators, and service providers in the upstream oil and gas sector to measure methane emissions. However, the suite of methane sensors affixed to these systems is non-standardized and existing literature displays a scarcity of direct comparisons regarding their measurement performance. Gaussian dispersion models are often used to supplement measured data and derive estimates of emission intensity in screening applications based on data measured by these sensors. Existing literature indicates these models perform with considerable uncertainty. As such, equivalence of performance between existing vehicle-based emission screening systems is difficult to assess. To address this issue, field-based controlled release experiments were conducted to compare concentration data from an open- and closed-path sensor deployed in tandem onboard a vehicle. Performance of a forward Gaussian dispersion model was assessed relative to measured data from both sensors. 801 transects were driven through methane plumes dispersed downwind of a controlled emission source at various measurement distances and driving speeds, as well as a range of atmospheric conditions. Measurement performance was predicated on three primary descriptors of concentration data: the maximum concentration within each plume (maximum enhancement), plume width, and plume area (total methane sampled within the plume). Results showed that the measurement performances of both sensors were not equivalent. Relative to the open-path sensor, the closed-path sensor reported maximum enhancements that were ~40% smaller on average and plume widths that were ~42% larger on average, while measures of plume area displayed near 1:1 parity. Measurement discrepancies are largely explained by differences in sensor measurement frequency and intrinsic sampling mechanisms. Forward Gaussian dispersion model performance displayed uncertainties ranging from 12.3% to 1207.0%. The origin of this uncertainty is largely determined by generalizations of atmospheric stability and simplistic representations of downwind plume migration within the model
CAROM Air -- Vehicle Localization and Traffic Scene Reconstruction from Aerial Videos
Road traffic scene reconstruction from videos has been desirable by road
safety regulators, city planners, researchers, and autonomous driving
technology developers. However, it is expensive and unnecessary to cover every
mile of the road with cameras mounted on the road infrastructure. This paper
presents a method that can process aerial videos to vehicle trajectory data so
that a traffic scene can be automatically reconstructed and accurately
re-simulated using computers. On average, the vehicle localization error is
about 0.1 m to 0.3 m using a consumer-grade drone flying at 120 meters. This
project also compiles a dataset of 50 reconstructed road traffic scenes from
about 100 hours of aerial videos to enable various downstream traffic analysis
applications and facilitate further road traffic related research. The dataset
is available at https://github.com/duolu/CAROM.Comment: Accepted to IEEE ICRA 202
Optimal speed trajectory and energy management control for connected and automated vehicles
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
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