A framework for user- and system-oriented optimisation of fuel efficiency and traffic flow in Adaptive Cruise Control

Fully automated vehicles could have a significant share of the road network traffic in the near future. Several commercial vehicles with full-range Adaptive Cruise Control (ACC) systems or semi-autonomous functionalities are already available on the market. Many research studies aim at leveraging the potential of automated driving in order to improve the fuel efficiency of vehicles. However, in the vast majority of those, fuel efficiency is isolated to the driving dynamics between a single follower-leader pair, hence overlooking the complex nature of traffic. Consequently fuel efficiency and the efficient use of the roadway capacity are framed as conflicting objectives, leading to fuel-economy control models that adopt highly conservative driving styles. This formulation of the problem could be seen as a user-optimal approach, where in spite of delivering savings for individual vehicles, there is the side-effect of the deterioration of traffic flow. An important point that is overlooked is that the inefficient use of roadway capacity gives rise to congested traffic and traffic breakdowns, which in return increases energy costs within the system. The optimisation methods used in these studies entail high computational costs and, therefore, impose a strict constraint on the scope of problem. In this study, the use of car-following models and the limitation of the search space of optimal strategies to the parameter space of these is proposed. The proposed framework enables performing much more comprehensive optimisations and conducting more extensive tests on the collective impacts of fuel-economy driving strategies. The results show that, as conjectured, a “short-sighted” user-optimal approach is unable to deliver overall fuel efficiency. Conversely, a system-optimal formulation for fuel efficient driving is presented, and it is shown that the objectives of fuel efficiency and traffic flow are in fact not only non-conflicting, but also that they could be viewed as one when the global benefits to the network are considered

Measurement of dermal water content using a multi-wavelength optical sensor

Skin hydration is crucial for overall skin health. Maintaining skin hydration levels preserves skin integrity and prevents tissue damage which can lead to several debilitating conditions. Moreover, continuous monitoring of skin hydration can contribute to the diagnosis or management of serious diseases. For instance, sugar imbalance in diabetes mellitus and kidney disease can lead to the loss of bodily fluids and cause dry skin. Therefore, continuous, accurate and non-intrusive monitoring of skin hydration would present a remarkable opportunity for maintaining overall health and wellbeing. There are various techniques to assess skin hydration. Electrical based Corneometers are currently the gold standard in clinical and non-clinical practice. However, these techniques have a number of limitations. In particular, they are costly, sizeable, intrusive, and operator dependent. Recent research has demonstrated that near infrared spectroscopy could be used as a non-intrusive alternative for the measurement of skin water content. The present paper reports the development and in-vitro validation of a noninvasive, portable, skin hydration sensor. The results indicate that the developed sensor can deliver reliable measurements of skin water content

Energy efficient adaptive cruise control: Towards benchmarking energy efficiency in the control of partially and fully automated vehicles

Fully automated vehicles are expected to have a significant share of the road network traffic in the near future. Several commercial vehicles with full-range adaptive cruise control systems or semi-autonomous functionalities are already available in the market. This provides a unique opportunity to improve the acceleration behaviour of vehicles, and thereby, improve network’s efficiency in terms of important performance indicators such as fuel consumption and traffic throughput. However, automated driving systems usually adopt a highly conservative driving strategy to ensure safety and fuel efficiency for individual vehicles. The collective impacts of such strategies on the network level can lead to the deterioration of traffic flow and to an increase in fuel/energy consumption. Much of the existing research in this area either target driving conditions where there are no additional complexities caused by interaction between vehicles, or make simplistic assumptions about the dynamics of driving behaviour and its relationship with fuel consumption in order to formulate a feasibly solvable optimisation problem. The reduction of the question of fuel efficiency to optimisation scenarios where only a pair of vehicles are considered and little attention is paid to the surrounding traffic, leads to a user-optimal driving strategy at best, however addressing environmental concerns and a more efficient use of fossil fuels in road transport networks necessitates a system-optimal approach. A system-optimal approach means the scope of the problem must be broadened so that a) the complex relationship between individual driving styles and the dynamical features of traffic flow are incorporated within the optimisation framework and b) the long-term impacts of driving strategies on network’s performance are modelled within optimisation scenarios. The challenge here is to model driving behaviour and traffic flow with sufficient accuracy and devise an optimisation framework that is computationally efficient enough to cope with the complexity of the problem. In this study, the use of car-following models and limiting the search space for optimal strategies to the parameter space of car-following models is proposed. This framework enables performing much more comprehensive optimisations and conducting more extensive tests on the collective impacts of fuel-economy driving strategies. The results obtained in this study show that formulating the optimisation in a short-sighted way where merely individual vehicles are considered and no attention is paid to the collective impacts of a fuel-economy driving strategy, can lead to significant increase in fuel consumption for the whole network while delivering marginal benefits for the target vehicle. This study establishes a complex relationship between traffic flow and fuel consumption on the link level, where the former cannot be achieved without addressing the latter correctly in the optimisation process. In addition to the main research question discussed above, the present thesis proposes a new method for the analysis of car-following models. The conventional method in the analysis of car-following models relies on the cumulative error between real data and modelled data in order to benchmark car-following models. Although the cumulative error is indeed an informative measure of performance, it leaves many questions regarding the capacity of models for replicating driving behaviour unanswered. Here the use of dynamic system identification is investigated as a way to provide a more in-depth analysis of the strengths and weaknesses of car-following models in reproducing realistic driving behaviour. Subsequently, the proposed method is applied to compare a number of car-following models. Although the application of this method for the comparison of car-following models presents some challenges that require further research, the results are very promising

Comparison of wavelength selection methods for in-vitro estimation of lactate: a new unconstrained, genetic algorithm-based wavelength selection

Biochemical and medical literature establish lactate as a fundamental biomarker that can shed light on the energy consumption dynamics of the body at cellular and physiological levels. It is therefore, not surprising that it has been linked to many critical conditions ranging from the morbidity and mortality of critically ill patients to the diagnosis and prognosis of acute ischemic stroke, septic shock, lung injuries, insulin resistance in diabetic patients, and cancer. Currently, the gold standard for the measurement of lactate requires blood sampling. The invasive and costly nature of this procedure severely limits its application outside intensive care units. Optical sensors can provide a non-invasive, inexpensive, easy-to-use, continuous alternative to blood sampling. Previous efforts to achieve this have shown significant potential, but have been inconclusive. A measure that has been previously overlooked in this context, is the use of variable selection methods to identify regions of the optical spectrum that are most sensitive to and representative of the concentration of lactate. In this study, several wavelength selection methods are investigated and a new genetic algorithm-based wavelength selection method is proposed. This study shows that the development of more accurate and parsimonious models for optical estimation of lactate is possible. Unlike many existing methods, the proposed method does not impose additional locality constraints on the spectral features and therefore helps provide a much more granular interpretation of wavelength importance

In vitro quantification of lactate in Phosphate Buffer Saline (PBS) samples.

Continuous measurement of lactate levels in the blood is a prerequisite in intensive care patients who are susceptible to sepsis due to their suppressed immune system and increased metabolic demand. Currently, there exists no noninvasive tool for continuous measurement of lactate in clinical practice. The current mode of measurement is based on arterial blood gas analyzers which require sampling of arterial blood. In this work, we propose the use of Near Infra-Red (NIR) spectroscopy together with multivariate models as a means to non-invasively predict the concentration of lactate in the blood. As the first step towards this objective, we examined the possibility of accurately predicting concentrations of sodium lactate (NaLac) from the NIR spectra of 37 isotonic phosphate buffer saline (PBS) samples containing NaLac ranging from 0 to 20 mmol/L. NIR spectra of PBS samples were collected using the Lambda 1050 dual beam spectrometer over a spectral range of 800 - 2600 nm with a quartz cell of 1 mm optical path. Estimates and calibration of the lactate concentration with the NIR spectra were made using Partial Least-Squares (PLS) regression analysis and leave-one-out cross-validation on filtered spectra. The regression analysis showed a correlation coefficient of 0.977 and a standard error of 0.89 mmol/L between the predicted and prepared samples. The results suggest that NIR spectroscopy together with multivariate models can be a valuable tool for non-invasive assessment of blood lactate concentrations

The efficacy of support vector machines in modelling deviations from the Beer-Lambert law for optical measurement of lactate

Lactate is an important biomarker with a significant diagnostic and prognostic ability in relation to life-threatening conditions and diseases such as sepsis, diabetes, cancer, pulmonary and kidney diseases, to name a few. The gold standard method for the measurement of lactate relies on blood sampling, which due to its invasive nature, limits the ability of clinicians in frequent monitoring of patients' lactate levels. Evidence suggests that the optical measurement of lactate holds promise as an alternative to blood sampling. However, achieving this aim requires better understanding of the optical behavior of lactate. The present study investigates the potential deviations of absorbance from the Beer-Lambert law in high concentrations of lactate. To this end, a number of nonlinear models namely support vector machines with quadratic, cubic and quartic kernels and radial basis function kernel are compared with the linear principal component regression and linear support vector machine. Interestingly, it is shown that even in extremely high concentrations of lactate (600 mmol/L) in a phosphate buffer solution, the linear models surpass the performance of the other models

Comparison of a Genetic Algorithm Variable Selection and Interval Partial Least Squares for quantitative analysis of lactate in PBS

Blood lactate is an important biomarker that has been linked to morbidity and mortality of critically ill patients, acute ischemic stroke, septic shock, lung injuries, insulin resistance in diabetic patients, and cancer. Currently, the clinical measurement of blood lactate is done by collecting intermittent blood samples. Therefore, noninvasive, optical measurement of this significant biomarker would lead to a big leap in healthcare. This study, presents a quantitative analysis of the optical properties of lactate. The benefits of wavelength selection for the development of accurate, robust, and interpretable predictive models have been highlighted in the literature. Additionally, there is an obvious, time- and cost-saving benefit to focusing on narrower segments of the electromagnetic spectrum in practical applications. To this end, a dataset consisting of 47 spectra of Na-lactate and Phosphate Buffer Solution (PBS) was produced using a Fourier transform infrared spectrometer, and subsequently, a comparative study of the application of a genetic algorithm-based wavelength selection and two interval selection methods was carried out. The high accuracy of predictions using the developed models underlines the potential for optical measurement of lactate. Moreover, an interesting finding is the emergence of local features in the proposed genetic algorithm, while, unlike the investigated interval selection methods, no explicit constraints on the locality of features was imposed. Finally, the proposed genetic algorithm suggests the formation of α-hydroxy-esters methyl lactate in the solutions while the other investigated methods fail to indicate this

An interpretable machine learning framework for measuring urban perceptions from panoramic street view images

The proliferation of street view images (SVIs) and the constant advancements in deep learning techniques have enabled urban analysts to extract and evaluate urban perceptions from large-scale urban streetscapes. However, many existing analytical frameworks have been found to lack interpretability due to their end-to-end structure and "black-box" nature, thereby limiting their value as a planning support tool. In this context, we propose a five-step machine learning framework for extracting neighborhood-level urban perceptions from panoramic SVIs, specifically emphasizing feature and result interpretability. By utilizing the MIT Place Pulse data, the developed framework can systematically extract six dimensions of urban perceptions from the given panoramas, including perceptions of wealth, boredom, depression, beauty, safety, and liveliness. The practical utility of this framework is demonstrated through its deployment in Inner London, where it was used to visualize urban perceptions at the Output Area (OA) level and to verify against real-world crime rate

Investigations into the Effects of pH on Quantitative Measurements of Lactate in Biological Media Using ATR-FTIR Spectroscopy

Quantification of lactate/lactic acid in critical care environments is essential as lactate serves as an important biochemical marker for the adequacy of the haemodynamic circulation in shock and of cell respiration at the onset of sepsis/septic shock. Hence, in this study, ATR-FTIR was explored as a potential tool for lactate measurement, as the current techniques depend on sample preparation and fails to provide rapid response. Moreover, the effects of pH on PBS samples (7.4, 7, 6.5 and 6) and change in solution conditions (PBS to whole blood) on spectral features were also investigated. A total 189 spectra from five sets of lactate containing media were obtained. Results suggests that lactate could be measured with more than 90% accuracy in the wavenumber range of 1500-600 cm-1. The findings of this study further suggest that there exist no effects of change in pH or media, when estimating lactate concentration changes in this range of the Mid-IR spectral region

Near Infrared and Aquaphotomic analysis of water absorption in lactate containing media

Increased concentrations of lactate levels in blood are often seen in patients with life-threatening cellular hypoperfusion or infections. State-of-the-art techniques used in clinical practice for measuring serum lactate concentrations rely on intermittent blood sampling and do not permit continuous monitoring of this all important parameter in critical care environments.In recent years, Near Infrared (NIR) Spectroscopy has been established as a possible alternative to existing methods that can mitigate these constraints and be used for non-invasive continuous monitoring of lactate. Nevertheless, the dominant absorption of -OH overtone bands of water in the NIR presents a challenge and complicates the accurate detection of other absorbers such as lactate. For this reason, comprehensive analysis of the -OH overtone bands with systematic lactate concentration changes is essential. This paper reports on the analysis of NIR spectra of two aqueous systems of varying concentrations of lactate in saline and whole blood using the principles of Aquaphotomics.The results show distinctive conformational and structural differences in lactate-water binding, which arise due to the molecular interactions of bonds present in respective solvents