92 research outputs found
Data-centric Design and Training of Deep Neural Networks with Multiple Data Modalities for Vision-based Perception Systems
224 p.Los avances en visión artificial y aprendizaje automático han revolucionado la capacidad de construir sistemas que procesen e interpreten datos digitales, permitiéndoles imitar la percepción humana y abriendo el camino a un amplio rango de aplicaciones. En los últimos años, ambas disciplinas han logrado avances significativos,impulsadas por los progresos en las técnicas de aprendizaje profundo(deep learning). El aprendizaje profundo es una disciplina que utiliza redes neuronales profundas (DNNs, por sus siglas en inglés) para enseñar a las máquinas a reconocer patrones y hacer predicciones basadas en datos. Los sistemas de percepción basados en el aprendizaje profundo son cada vez más frecuentes en diversos campos, donde humanos y máquinas colaboran para combinar sus fortalezas.Estos campos incluyen la automoción, la industria o la medicina, donde mejorar la seguridad, apoyar el diagnóstico y automatizar tareas repetitivas son algunos de los objetivos perseguidos.Sin embargo, los datos son uno de los factores clave detrás del éxito de los algoritmos de aprendizaje profundo. La dependencia de datos limita fuertemente la creación y el éxito de nuevas DNN. La disponibilidad de datos de calidad para resolver un problema específico es esencial pero difícil de obtener, incluso impracticable,en la mayoría de los desarrollos. La inteligencia artificial centrada en datos enfatiza la importancia de usar datos de alta calidad que transmitan de manera efectiva lo que un modelo debe aprender. Motivada por los desafíos y la necesidad de los datos, esta tesis formula y valida cinco hipótesis sobre la adquisición y el impacto de los datos en el diseño y entrenamiento de las DNNs.Específicamente, investigamos y proponemos diferentes metodologías para obtener datos adecuados para entrenar DNNs en problemas con acceso limitado a fuentes de datos de gran escala. Exploramos dos posibles soluciones para la obtención de datos de entrenamiento, basadas en la generación de datos sintéticos. En primer lugar, investigamos la generación de datos sintéticos utilizando gráficos 3D y el impacto de diferentes opciones de diseño en la precisión de los DNN obtenidos. Además, proponemos una metodología para automatizar el proceso de generación de datos y producir datos anotados variados, mediante la replicación de un entorno 3D personalizado a partir de un archivo de configuración de entrada. En segundo lugar, proponemos una red neuronal generativa(GAN) que genera imágenes anotadas utilizando conjuntos de datos anotados limitados y datos sin anotaciones capturados en entornos no controlados
Essays on time series analysis and statistical machine learning
This thesis encompasses three research articles contributing to the fields of time series analysis and statistical machine learning. Firstly, we develop a peaks-over- threshold approach, which captures both short- and long-term correlations in the underlying time series in order to model the clustering behaviour in high-threshold exceedances. The suggested model is motivated by and applied to oceanographic data. Secondly, we propose an efficient discrepancy-based inference approach for intractable generative models based on quasi-Monte Carlo methods. We demonstrate that this method substantially reduces the computational cost of estimating the model parameters in various applications of academic and practical interest. Thirdly, we suggest training methods for deep sequential models, which improve the forecast precision when facing structural breaks in the in-sample period. These mitigation strategies are examined in an extensive simulation study and utilised to forecast energy data. As the developed theory in this thesis is very versatile, it is applicable to a broad range of data types as well as research fields, and in particular to economic time series
Vehicle trajectory prediction for safe navigation of autonomous vehicles
Trajectory prediction of the other road users in the vicinity of an autonomous vehicle is important for safe navigation in dense traffic. Once an autonomous vehicle
anticipates how the other road actors will react in the near future, path planning is
a lot more simpler and safer. Moreover, the knowledge of future movement of other
road actors allows control of sudden jerks in the planned ego vehicle’s path and thus
makes travel smoother. This trajectory prediction stage can be used at any level,
from restricted driver assistance to full vehicle autonomy. In this thesis two novel trajectory prediction models have been developed. In the
first model, the spatio-temporal features that form the basis of behaviour prediction were captured using a Convolutional Long Short Term Memory (Conv-LSTM)
neural network architecture consisting of three modules: 1) Interaction Learning to
capture the motion of and interaction with surrounding cars, 2) Temporal Learning
to identify the dependency on past movements and 3) Motion Learning to convert
the extracted features from these two modules into future positions. In addition,
a novel feedback scheme was introduced in which the current predicted positions
of each car are leveraged to update future motion, encapsulating the effect of the
surrounding cars. In the second model a conventional Long Short Term Memory
(LSTM) cell based encoder-decoder architecture was developed which uses not only
the historical observations but also the associated map features. Moreover, unlike
existing architectures, the proposed method incorporates and updates the surrounding vehicle information in both the encoder and decoder, making use of dynamically
predicted new data for accurate prediction in longer time horizons. This seamlessly
performs four tasks: first, it encodes a feature given the past observations, second,
it estimates future maneuvers given the encoded state, third, it predicts the future
motion given the estimated maneuvers and the initially encoded states, and fourth,
it estimates future trajectory given the encoded state and the predicted maneuvers
and motions. Both the developed models were evaluated extensively on two publicly available datasets which include both multi-lane highway and signalled intersections,
to benchmark the prediction accuracy with the state-of-the-art models. Later, the
conventional encoder-decoder model was also evaluated with a newly collected “Radiate” dataset which includes two intersections, the Kingussie T-junction and the
Edinburgh four-way junction, both without traffic signals. The accuracy of the predicted trajectories on the benchmark datasets are comparable with state-of-the-art
methods. Moreover, evaluation on the latter dataset (“Radiate”) made it possible
to understand better the effect of inter-vehicle interactions on future motion without
any influence from mandatory traffic signals.Engineering and Physical Sciences Research Council (EPSRC) funding
Exploring the challenges and opportunities of image processing and sensor fusion in autonomous vehicles: A comprehensive review
Autonomous vehicles are at the forefront of future transportation solutions, but their success hinges on reliable perception. This review paper surveys image processing and sensor fusion techniques vital for ensuring vehicle safety and efficiency. The paper focuses on object detection, recognition, tracking, and scene comprehension via computer vision and machine learning methodologies. In addition, the paper explores challenges within the field, such as robustness in adverse weather conditions, the demand for real-time processing, and the integration of complex sensor data. Furthermore, we examine localization techniques specific to autonomous vehicles. The results show that while substantial progress has been made in each subfield, there are persistent limitations. These include a shortage of comprehensive large-scale testing, the absence of diverse and robust datasets, and occasional inaccuracies in certain studies. These issues impede the seamless deployment of this technology in real-world scenarios. This comprehensive literature review contributes to a deeper understanding of the current state and future directions of image processing and sensor fusion in autonomous vehicles, aiding researchers and practitioners in advancing the development of reliable autonomous driving systems
Driver lane change intention inference using machine learning methods.
Lane changing manoeuvre on highway is a highly interactive task for human drivers. The intelligent vehicles and the advanced driver assistance systems (ADAS) need to have proper awareness of the traffic context as well as the driver. The ADAS also need to understand the driver potential intent correctly since it shares the control authority with the human driver. This study provides a research on the driver intention inference, particular focus on the lane change manoeuvre on highways.
This report is organised in a paper basis, where each chapter corresponding to a publication, which is submitted or to be submitted. Part Ⅰ introduce the motivation and general methodology framework for this thesis. Part Ⅱ includes the literature survey and the state-of-art of driver intention inference. Part Ⅲ contains the techniques for traffic context perception that focus on the lane detection. A literature review on lane detection techniques and its integration with parallel driving framework is proposed. Next, a novel integrated lane detection system is designed. Part Ⅳ contains two parts, which provides the driver behaviour monitoring system for normal driving and secondary tasks detection. The first part is based on the conventional feature selection methods while the second part introduces an end-to-end deep learning framework. The design and analysis of driver lane change intention inference system for the lane change manoeuvre is proposed in Part Ⅴ.
Finally, discussions and conclusions are made in Part Ⅵ.
A major contribution of this project is to propose novel algorithms which accurately model the driver intention inference process. Lane change intention will be recognised based on machine learning (ML) methods due to its good reasoning and generalizing characteristics. Sensors in the vehicle are used to capture context traffic information, vehicle dynamics, and driver behaviours information. Machine learning and image processing are the techniques to recognise human driver behaviour.PhD in Transpor
Cyber Threats and NATO 2030: Horizon Scanning and Analysis
The book includes 13 chapters that look ahead to how NATO can best address the cyber threats, as well as opportunities and challenges from emerging and disruptive technologies in the cyber domain over the next decade.
The present volume addresses these conceptual and practical requirements and contributes constructively to the NATO 2030 discussions. The book is arranged in five short parts...All the chapters in this book have undergone double-blind peer review by at least two external experts.https://scholarworks.wm.edu/asbook/1038/thumbnail.jp
Recent Applications in Graph Theory
Graph theory, being a rigorously investigated field of combinatorial mathematics, is adopted by a wide variety of disciplines addressing a plethora of real-world applications. Advances in graph algorithms and software implementations have made graph theory accessible to a larger community of interest. Ever-increasing interest in machine learning and model deployments for network data demands a coherent selection of topics rewarding a fresh, up-to-date summary of the theory and fruitful applications to probe further. This volume is a small yet unique contribution to graph theory applications and modeling with graphs. The subjects discussed include information hiding using graphs, dynamic graph-based systems to model and control cyber-physical systems, graph reconstruction, average distance neighborhood graphs, and pure and mixed-integer linear programming formulations to cluster networks
- …