Presently, autonomous vehicles are on the rise and are expected to be on the roads in the coming years. In this sense, it becomes necessary to have adequate knowledge about its states to design controllers capable of providing adequate performance in all driving scenarios. Sideslip and roll angles are critical parameters in vehicular lateral stability. The later has a high impact on vehicles with an elevated center of gravity, such as trucks, buses, and industrial vehicles, among others, as they are prone to rollover. Due to the high cost of the current sensors used to measure these angles directly, much of the research is focused on estimating them. One of the drawbacks is that vehicles are strong non-linear systems that require specific methods able to tackle this feature. The evolution in Artificial Intelligence models, such as the complex Artificial Neural Network architectures that compose the Deep Learning paradigm, has shown to provide excellent performance for complex and non-linear control problems. In this paper, the authors propose an inexpensive but powerful model based on Deep Learning to estimate the roll and sideslip angles simultaneously in mass production vehicles. The model uses input signals which can be obtained directly from onboard vehicle sensors such as the longitudinal and lateral accelerations, steering angle and roll and yaw rates. The model was trained using hundreds of thousands of data provided by Trucksim^® and validated using data captured from real driving maneuvers using a calibrated ground truth device such as VBOX3i dual-antenna GPS from Racelogic^®. The use of both Trucksim^® software and the VBOX measuring equipment is recognized and widely used in the automotive sector, providing robust data for the research shown in this article.

In this article, a Linear Quadratic Regulator (LQR) lateral stability and rollover controller has been developed including as the main novelty taking into account the road bank angle and using exclusively active suspension for both lateral stability and rollover control. The main problem regarding the road bank is that it cannot be measured by means of on-board sensors. The solution proposed in this article is performing an estimation of this variable using a Kalman filter. In this way, it is possible to distinguish between the road disturbance component and the vehicle’s roll angle. The controller’s effectiveness has been tested by means of simulations carried out in TruckSim, using an experimentally-validated vehicle model. Lateral load transfer, roll angle, yaw rate and sideslip angle have been analyzed in order to quantify the improvements achieved on the behavior of the vehicle. For that purpose, these variables have been compared with the results obtained from both a vehicle that uses passive suspension and a vehicle using a fuzzy logic controller.

We synthesized an anthracene derivative with solvatochromic properties to be used as a molecular probe for membrane dynamics and supramolecular organization.

In recent years, there have been many advances in vehicle technologies based on the efficient use of real-time data provided by embedded sensors. Some of these technologies can help you avoid or reduce the severity of a crash such as the Roll Stability Control (RSC) systems for commercial vehicles. In RSC, several critical variables to consider such as sideslip or roll angle can only be directly measured using expensive equipment. These kind of devices would increase the price of commercial vehicles. Nevertheless, sideslip or roll angle or values can be estimated using MEMS sensors in combination with data fusion algorithms. The objectives stated for this research work consist of integrating roll angle estimators based on Linear and Unscented Kalman filters to evaluate the precision of the results obtained and determining the fulfillment of the hard real-time processing constraints to embed this kind of estimators in IoT architectures based on low-cost equipment able to be deployed in commercial vehicles. An experimental testbed composed of a van with two sets of low-cost kits was set up, the first one including a Raspberry Pi 3 Model B, and the other having an Intel Edison System on Chip. This experimental environment was tested under different conditions for comparison. The results obtained from low-cost experimental kits, based on IoT architectures and including estimators based on Kalman filters, provide accurate roll angle estimation. Also, these results show that the processing time to get the data and execute the estimations based on Kalman Filters fulfill hard real time constraints.

The study of surfactant and bio membranes interaction is particularly complex due to the diversity in lipid composition and the presence of proteins in natural membranes. Even more difficult is the study of this interaction in vivo since cellular damage may complicate the interpretation of the results, therefore for most of the studies in this field either artificial or model systems are used. One of the model system most used to study biomembranes are erythrocytes due to their relatively simple structure (they lack nuclei and organelles having only the plasma membrane), their convenient experimental manipulation and availability. In this context, we used rabbit erythrocytes as a model membrane and Laurdan (6-lauroyl-2-dimethylaminonaphthalene) as the fluorescent probe to study changes promoted in the membrane by the interaction with the sucrose monoester of myristic acid, β-d-fructofuranosyl-6-O-myristoyl-α-d-glucopyranoside (MMS). Surfactant and erythrocytes interaction was studied by measuring hemoglobin release and the changes in water content in the membrane sensed by Laurdan. Using two-photon excitation, three types of measurements were performed: Generalized Polarization (analyzed as average GP values), Fluorescence Lifetime Imaging, FLIM (analyzed using phasor plots) and Spectral imaging (analyzed using spectral phasor). Our data indicate that at sublytical concentration of surfactant (20μM MMS), there is a decrease of about 35% in erythrocytes size, without changes in Laurdan lifetime or emission spectra. We also demonstrate that as hemolysis progress, Laurdan lifetime increased due to the decrease in hemoglobin (strong quencher of Laurdan emission) content inside the erythrocytes. Under these conditions, Laurdan spectral phasor analyses can extract the information on the water content in the membrane in the presence of hemoglobin. Our results indicate an increase in membrane fluidity in presence of MMS.