Search

Expanding the performance and autonomous-decision capability of driver-assistance systems is critical in today's automotive engineering industry to help drivers and reduce accident incidence. It is essential to provide vehicles with the necessary perception systems, but without creating a prohibitively expensive product. In this area, the continuous and precise estimation of a road surface on which a vehicle moves is vital for many systems. This paper proposes a low-cost approach to solve this issue. The developed algorithm resorts to analysis of vibrations generated by the tyre-rolling movement to classify road surfaces, which allows for optimizing vehicular-safety-system performance. The signal is analyzed by means of machine-learning techniques, and the classification and estimation of the surface are carried out with the use of a self-organizing-map (SOM) algorithm. Real recordings of the vibration produced by tyre rolling on six different types of surface were used to generate the model. The efficiency of the proposed model (88.54%) and its speed of execution were compared with those of other classifiers in order to evaluate its performance. [ABSTRACT FROM AUTHOR]

Tires play a critical role in vehicle safety. Proper modeling of tire–road interaction is essential for optimal performance of active safety systems. This work studies the influence of temperature, longitudinal vehicle speed, steering frequency, vertical load, and inflation pressure on lateral tire dynamics. To this end, a tire test bench that allows the accurate control of these parameters and the measurement of the variables of interest was used. The obtained results made it possible to propose a simple model that allowed the determination of relaxation length as a function of tire vertical load and vehicle linear speed, and the determination of a representative tread temperature. Additionally, a model has been proposed to determine the lateral friction coefficient from the aforementioned temperature. Finally, results also showed that some variables had little influence on the parameters that characterize lateral dynamics. [ABSTRACT FROM AUTHOR]

In the last years, a large number of new biocompatible materials for 3D printers have emerged. Due to their recent appearance and rapid growth, there is little information about their mechanical properties. The design and manufacturing of oral appliances made with 3D printing technologies require knowledge of the mechanical properties of the biocompatible material used to achieve optimal performance for each application. This paper focuses on analysing the mechanical behaviour of a wide range of biocompatible materials using different additive manufacturing technologies. To this end, tensile and bending tests on different types of recent biocompatible materials used with 3D printers were conducted to evaluate the influence of the material, 3D printing technology, and printing orientation on the fragile/ductile behaviour of the manufactured devices. A test bench was used to perform tensile tests according to ASTM D638 and bending tests according to ISO 178. The specimens were manufactured with nine different materials and five manufacturing technologies. Furthermore, specimens were created with different printing technologies, biocompatible materials, and printing orientations. The maximum allowable stress, rupture stress, flexural modulus, and deformation in each of the tested specimens were recorded. Results suggest that specimens manufactured with Stereolithography (SLA) and milling (polymethyl methacrylate PMMA) achieved high maximum allowable and rupture stress values. It was also observed that Polyjet printing and Selective Laser Sintering technologies led to load–displacement curves with low maximum stress and high deformation values. Specimens manufactured with Digital Light Processing technology showed intermediate and homogeneous performance. Finally, it was observed that the printing direction significantly influences the mechanical properties of the manufactured specimens in some cases. [ABSTRACT FROM AUTHOR]

This paper is devoted to developing a biological-based algorithm to simulate the control of a human arm by means of a Spiking Neural Network (SNN) with a pre-set structure similar to those found in reflex arcs. The replication of the behavior of biological systems is a primary step to understand how the brain and the neural networks that comprise it work. The study of these systems began with the analysis of animals with low neural complexity as well as with small control neural networks such as those present in the reflex acts of the human body. However, the study of the brain, due to its intricate structure, still presents many unknowns and challenges to be solved. Meanwhile, one way to understand how biological systems work is to emulate their behavior through computer simulations. Artificial neural networks (ANNs) offered the opportunity to replicate neural structures to understand and reproduce their behavior and performance. Many types of ANNs are based on the use of activation functions. However, these ANNs are simplified models that do not replicate the behavior of complex biological neural systems accurately. For this reason, spike-based models have been developed to reproduce real biological systems more faithfully. This work proposes simulating the motor behavior of the central nervous system to control the position of an arm. To this end, a spiking neural network has been developed to emulate motion control using a fixed structure that reproduces reflex arcs. A channel-based synapse model and a control scheme based on the equilibrium point hypothesis have been proposed to improve biological similarity of the controller. Furthermore, the developed controller has learning capabilities thanks to the reproduction of the synapse plasticity process that takes place in real biological systems through a supervised Spike-Timing Dependent Plasticity (STDP) learning strategy. The performance of the proposed approach has been demonstrated by simulating the control of the movement of an arm using Hill's Muscle Model. Results suggest that the proposed control algorithm resembles the response of biological systems adequately in terms of speed and temporal response. [ABSTRACT FROM AUTHOR]

The performance of vehicle safety systems depends very much on the accuracy of the signals coming from vehicle sensors. Among them, the wheel speed is of vital importance. This paper describes a new method to obtain the wheel speed by using Sin-Cos encoders. The methodology is based on the use of the Savitzky–Golay filters to optimally determine the coefficients of the polynomials that best fit the measured signals and their time derivatives. The whole process requires a low computational cost, which makes it suitable for real-time applications. This way it is possible to provide the safety system with an accurate measurement of both the angular speed and acceleration of the wheels. The proposed method has been compared to other conventional approaches. The results obtained in simulations and real tests show the superior performance of the proposed method, particularly for medium and low wheel angular speeds. [ABSTRACT FROM AUTHOR]

The development of new control algorithms in vehicles requires high economic resources, mainly due to the use of generic real-time instrumentation and control systems. In this work, we proposed a low-cost electronic control unit (ECU) that could be used for both development and implementation. The proposed electronic system used a hybrid system on chip (SoC) between a field-programmable gate array (FPGA) and an Advanced RISC (reduced instruction set computer) Machine (ARM) processor that allowed the execution of parallel tasks, fulfilling the real-time requirements that vehicle controls demand. Another feature of the proposed electronic system was the recording of measured data, allowing the performance of the implemented algorithm to be evaluated. All this was achieved by using modular programming that, without the need for a real-time operating system, executed the different tasks to be performed, exploiting the parallelism offered by the FPGA as well as the dual core of the ARM processor. This methodology facilitates the transition between the designing, testing, and implementation stages in the vehicle. In addition, our system is programmed with a single binary file that integrates the code of all processors as well as the hardware description of the FPGA, which speeds up the updating process. In order to validate and demonstrate the performance of the proposed electronic system as a tool for the development and implementation of control algorithms in vehicles, a series of tests was carried out on a test bench. Different traction control system (TCS) algorithms were implemented and the results were compared. [ABSTRACT FROM AUTHOR]