Driver Control Research Articles

Construction of a prototype of the basic module of a scalable robotic system for simulation of composite surfaces for dynamic tests

The authors of the article developed a mathematical model of Stewart platform management. The angles of rotation of the servo shafts were obtained when setting various laws of motion of the mobile platform, in matrix mode. The hardware part of the prototype of the basic module of a scalable robotics system for modeling composite surfaces for dynamic tests was developed. The results of calculations of the maximum permissible load on the shafts of servos are presented. The CubeIDE settings for programming the STM32 microcontroller are given. The results of software development for the control of this prototype were obtained, namely, a library for controlling 6 servo drives under driver control was written. The problems that may arise during the above-described developments by future developers are considered, solutions to these problems are obtained. Detailed design features of this prototype are described, the control code of the servo drive is given; steps for further development of the final robotic system are considered

Hardware Implementation of Fully Controlled Bridge Rectifier with Rapid Control Prototyping Approach

In industrial applications, rapid prototyping of digital controls is important in terms of time, cost, and getting easier design steps. Especially the complexity of different power electronic converter circuits and their necessity of providing various operating conditions make digital control inevitable. However, developing a digital control system has many unknown details. In-the-loop simulation techniques evolved to simplify this stage during the last few decades. In this paper, a fully controlled bridge rectifier is designed and implemented by using a rapid control prototyping approach; its steps are accelerated with processor-in-the-loop and hardware-in-the-loop tests. Launchpad F28379D from Texas Instruments is used as an interface between the designed rectifier hardware circuit and MATLAB/Simulink Embedded Coder platform. Additionally, a driver control board is developed to provide switching signals and analog to digital measurements. The performance of the system is experimentally tested on a 500W rectifier prototype with a closed loop PI controller for voltage regulation at different parametric variations.

A perspective review on the role of engine sound in speed perception and control: state of the art and methodological suggestions.

In this review we focus on the role of in-car sound, specifically the artificial engine sounds, on drivers' speed perception and control, a topic that has received little attention so far. Previous studies indicate that removing or reducing engine sound leads drivers to underestimate speed and, consequently, to drive faster. Furthermore, evidence suggests that specific sound frequencies could play a role in this process, highlighting the importance of in-car sound features. First, we show that the amount of research in the field is scarce and rather outdated, and that this is largely due to the fact that industrial research is subject to very few publications. Then, we examine benefits and limitations of different research paradigms used and we propose a protocol to investigate systematically the phenomenon. In particular, we argue for the benefits of a wider use of psychophysical methods in speed perception, a field that has been typically explored by means of driving simulation. Finally, we highlight some methodological and statistical limitations that might impact the interpretation of the evidence considered. Our methodological considerations could be particularly useful for researchers aiming to investigate the impact of sound on speed perception and control, as well as for those involved in the design of in-car sounds. These are particularly relevant for the design of electric vehicles, which represent a challenge but also the ideal testing ground to advance the knowledge in the field.

Imaging Neural Activity in Intact, Semirestrained Drosophila Larvae.

The Drosophila larval nerve cord, which is the equivalent of the vertebrate spinal cord, houses the circuits required to process somatosensory stimuli (e.g., tactile, temperature, vibration, and self-movement) and generate the patterned muscle contractions underlying movement and behavior. Within this complex structure reside many cell types and cellular processes, making it difficult to experimentally access, when compared to peripheral parts of the nervous system (i.e., primary sensory neuron dendrites, motor neuron axons and synapses, and muscles). Additionally, the neurons in the larval nerve cord have small cell bodies, precluding traditional electrophysiological approaches. As such, the function of neurons in the nerve cord is less well studied than other parts of the nervous system, severely limiting our understanding of how larvae process sensory information and generate movement. Ca2+-sensitive fluorescent proteins enable the study of neuronal activity in live, genetically tractable animals, even those with small neuronal cell bodies. In addition, live imaging of neurons within the nerve cord in whole, intact animals is possible because larvae are translucent, and the use of intact animals allows for the peripheral sensory neuron circuits to remain intact. Ca2+-sensitive fluorescent proteins increase their fluorescence when voltage-gated Ca2+ channels are opened in depolarized neurons. Here, we describe an assay where a Ca2+-sensitive fluorescent protein (GCaMP6m) is expressed under the control of a GAL4 driver in a subset of neurons that reside in a circuit for vibration sensation. External vibration (sound) stimulates sensory neurons that activate the cells expressing the Ca2+-sensitive fluorescent protein. Visualization of the calcium-induced fluorescent signal with microscopy allows for quantification of neuronal activity.

Feedforward Control for Track Seeking Control in Hard Disk Drive with Sampled-Data Polynomial Based on Causal First-Order Hold

Feedforward Control for Track Seeking Control in Hard Disk Drive with Sampled-Data Polynomial Based on Causal First-Order Hold

Comfort in Automated Driving: A Literature Survey and a High-Level Integrative Framework

The advancement of automated vehicle technology and the resulting shift from active driver control to a more passive role introduce previously unexplored factors that influence drivers' comfort. Examples include the vehicle's level of automation and the driver's preferred driving style, trust in the vehicle, and situation awareness. To structure these as a resource that can support future research, we conducted a comprehensive literature review, identifying 51 works that directly or peripherally address comfort in an automated driving context. Most of these works focus on the physical component of comfort, rooted in vehicle dynamics, while only a few consider a broader concept of comfort necessary to encompass a more expansive set of factors. Based on this review, we propose an integrative framework of 27 comfort influencing factors and their interrelationships. We categorize factors into six groups, encompassing the driving environment, vehicle physical features and automation system, and the user's activity, individual characteristics, and understanding of the automated system. These six groups are organized into the three larger categories of environment, vehicle, and user-related considerations. Patterns that emerge from the framework include that: a) some factors primarily influence physical well-being (such as motion forces), b) some contribute to discomfort (automation failures) while others contribute to comfort (secondary activities), c) some are stable and known before the trip (individual characteristics) while others change over time (environment), and d) comfort or discomfort can lead users to change either the relevant factors (level of automation) or their own behavior (secondary activities).

Influence of pneumatic tire enveloping behavior characteristics on the performance of a half car suspension system using multi-objective optimization algorithms

The interaction between a vehicle's tire and the road surface is pivotal for a driver's control over the vehicle's movements. It serves as the fundamental link between the vehicle and the road. The modeling of tires holds significant importance in contemporary vehicle design, playing a critical role in assessing aspects such as vehicle handling, ride comfort, and road load analysis. This study focuses on investigating the impact of the enveloping behavior characteristics of a pneumatic tire on the performance of a suspension system. The analysis of the vehicle's ride comfort utilizes a half-car model. Unlike a previous model with a single point of contact with the road, the presented suspension system, coupled with a four-degree-of-freedom rigid ring tire model, offers a more precise estimation of both ride comfort and road holding. The primary emphasis of this research lies in the modeling and evaluation of the proposed suspension system's performance. A comprehensive computer model of the entire system is developed using MATLAB software. This work enhances the existing framework by incorporating both a Multi-Objective Genetic Algorithm (MOGA) and a Multi-Objective Particle Swarm Optimization (MOPSO) to optimize the damping and stiffness coefficients of the passive suspension. This approach allows for a detailed comparison of the optimization capabilities and effectiveness of both algorithms in refining vehicle ride comfort. The results from MATLAB simulations highlight performance improvements, and the comparative analysis of MOGA and MOPSO provides insights into the selection of optimization techniques for suspension system design.

Authority Allocation Approach for the Human-Machine Co-Driving System in Crosswinds

To improve driving safety and the stability of human-machine co-driving vehicles in crosswinds, a human-machine co-driving system to address the impact of crosswinds is developed. To reveal the relationship between crosswinds and tire cornering stiffness, the effect of tire load on cornering stiffness is analyzed based on the Magic Formula tire model. On this basis, the correction function of tire cornering stiffness is obtained. A human-machine co-driving system framework is introduced, which includes the driver model, controller, and authority allocation strategy. The controller is used to guarantee the stability of the system, which can remain robust to the uncertainty of tire cornering stiffness and vehicle velocity. The authority allocation strategy can adjust the authority level of the driver and controller according to the crosswind speed, working states of the vehicle, and driving feeling of drivers. The effectiveness of the proposed system is verified by simulation. The results show that the proposed system can improve the vehicle’s lane-keeping and obstacle-avoidance abilities in crosswinds. Further, the system can be compatible with drivers with different driving characteristics and achieve greater versatility. In addition, the impact of crosswinds on the trajectory tracking performance and driving stability of the vehicle, at different speeds and different tire cornering stiffnesses, can be weakened and the reliability of the system improved significantly.

Automatic Accident Avoidance System

Abstract: As much as road travel has improved lives for the better part it also has proposed many challenges. Along with taking lives of those if countless people it also has been an economic burden on the country, in India the cost of road travel accidents in 2019 was estimated to be 12 billion USD. Along with loss of lives, serious injuries, damage to public infrastructure and with huge financial burden on the tax payers it becomes a major in road block in the urbanization of developing countries, a lot of the steps have been taken by the government regarding this issue including awareness among the general public about using the safety features present in their respective automobile, avoiding driving under the influence of substances, regulating the manufacturing standards of automobiles so they can withstand the damage and keep the passengers safe. But these steps are primary based on the presumption on the reliable decision making of the human driver. A lot more could be achieved by devising methods for accident prevention that don’t rely on human intervention. In this project we have devised a system that works automatically using an ATmega328mp microcontroller paired with an ultrasonic distance sensor that can detect the obstacles facing the automobile and can make a decision to warn the driver and if ignored can override the control of the human driver.

Developing a Sustainable Quality Improvement Program in an Academic Center: The Experience of an Adolescent Unit.

Successful quality improvement (QI) efforts use a comprehensive, institutional QI framework and professional development, but literature describing implementing QI frameworks in Adolescent Medicine practices is sparse. We aimed to implement and increase the number of formally structured QI projects (primary aim) and the number of projects achieving a centerline (CL) shift (secondary aim) in our hospital's Adolescent Medicine Clinic. We used formal QI methodology to improve health outcomes by increasing the number of faculty with formalized QI education, creating interdisciplinary QI teams, and improving staff motivation. QI education was mandatory for Adolescent Medicine fellows and pediatric residents and encouraged for faculty and staff. The Divisional QI leadership team attended monthly meetings to review key driver diagrams, run and control charts, and receive intervention updates. All providers and staff received monthly updates, and the Hospital Quality and Safety Committee received biannual updates. We used run charts to share progress with primary and secondary aims. Since Q3 2014, the Adolescent Medicine team consistently achieved the primary aim of having 5 active projects in process, with 9 projects from Q1 2018-Q4 2020. For the secondary aim, a target of 50% of active QI projects attaining a sustained centerline shift was achieved in Q2 2018 and maintained in 16 of 20 quarters since. Clinicians can use QI methodology to improve health outcomes while facilitating professional development. For this initiative to succeed, institutional leadership must provide an infrastructure prioritizing meaningful QI involvement.

Safety evaluation and prediction of takeover performance in automated driving considering drivers’ cognitive load: A driving simulator study

Automated vehicles alleviate the need for driver attention and control. However, a takeover request (TOR) to the driver remains essential for emergencies and safety–critical scenarios beyond automation's capability. Thus, accessing drivers’ safety performance in various TOR scenarios is crucial for conditionally automated driving (SAE L3). However, TOR safety performance is seldom examined concerning drivers' cognitive load, despite its presumed relevance to TOR scenarios and human–machine interaction. Moreover, the adequacy of the time window preceding a TOR, critical for TOR safety prediction, remains inadequately explored. This study aims to assess safety performance across diverse TOR scenarios in Level 3 conditional automation, incorporating drivers’ cognitive load, and predict TOR safety by considering the time window's impact. A driving simulator experiment gathered eye movement and driving behavior data from 37 recruited participants. Participants were instructed to take control of the vehicle from automated driving within a time budget (TB) of 3 s or 7 s in obstacle avoidance (OA) or lane keeping (LK) scenarios while engaging in non-driving-related tasks (NDRTs). Participants’ subjective cognitive load in the different TOR scenarios was scaled using NASA-TLX. Furthermore, safe TOR performance was predicted utilizing Convolutional Neural Networks (CNNs) across different time window sizes preceding a TOR. The results indicate that: 1) cognitive loads in takeover scenarios ranked from highest to lowest as TB = 3s_OA, TB = 3s_LK, TB = 7s_OA, TB = 7s_LK; and the cognitive loads of the NDRTs ranked from highest to lowest as mistake finding, texting, chatting, monitoring; 2) the takeover safety performance in the four scenarios from lowest to highest was TB = 3s_OA, TB = 3s_LK, TB = 7s_OA, TB = 7s_LK; likewise, the takeover safety performance during the four NDRTs ranked from lowest to highest as mistake finding, monitoring, texting, chatting; 3) the time window size before the TORs significantly affected the prediction performance of the model. A 30-second window was recommended as optimal for predicting takeover safety using the CNN model, achieving an average F1 score of 0.8120 and 81.98 % accuracy. This study's findings enhance our comprehension of driving behavior characteristics during TOR and offer valuable insights for detecting driver states in conditionally automated driving contexts.

Monolithically Integrated GaN Power Stage for More Sustainable 48 V DC–DC Converters

In this article, a fully monolithically integrated GaN power stage with a half-bridge, driver, level shifter, dead time and voltage mode control for 48 V DC–DC converters is proposed and analyzed. The design of the GaN IC is presented in detail, and measurements of the single function blocks and the DC–DC converter up to 48 V are shown. Finally, considerations are given on a life cycle assessment with regard to the GaN power integration. This GaN power IC or stage demonstrates a higher level of integration, resulting in a reduced bill of materials and therefore lower climate impact.

Demographic and social differences in autonomous vehicle technology acceptance in Hungary

Progress in automation has resulted in a growing number of autonomous vehicles (AVs). However, demographic and social differences behind the acceptance of AV technology are an emerging topic in the East-Central European region. These countries (e.g., Poland, Slovakia, Romania) move on a similar technological development path, and the social and economic conditions are alike; thus, Hungary represents this region well. This study contributes to fill this niche. We used quantitative research methods (factor analysis, Kruskal-Wallis H test, Pearson-correlation) to analyze with a snowball (non-probability) sampling. The Hungarian respondents (N=949) selected in the sample were interviewed through a face-to-face and online quantitative questionnaire. The results show that gender and age influence mostly the acceptance; residence and occupation have only partial influence. The propensity to take risks is significantly differentiated in almost all demographic segments. The results facilitate differentiation of users based on their demographic characteristics in AV adoption. Furthermore, the integration of risk propensity into the analysis helps to identify which potential user groups are more likely to overcome any fears of novelty or which clusters are more likely to adopt the current framework of safe transport without driver control. The outcomes are of interest to engineers, manufacturers and policy-makers who can adapt their products, services and taking measures to meet the mobility needs of potential users and introduce effective incentives to increase public acceptance of AVs.

Study of the influence of lateral forces and velocity on the lateral dynamics of automobile

Stability of a car's trajectory of motion is the ability of a car to retain its required state of motion, under different conditions of motion. Depending on different states of motion, cars can lose direction of motion instability when there is a lateral force, due to vehicle speed, due to turning or braking the road with different grip coefficients on the sides of the wheel. In such complex moving conditions, cars need to keep a stable trajectory of movement under the driver's control. The paper examines the stability of the car's trajectory in linear motion conditions with the influence of lateral forces and the velocity of the vehicle, these two factors act to change the state of movement of the vehicle, which significantly affects the safety of the vehicle's movement. The authors looked at the effect of vehicle velocity and lateral forces on vehicle transverse dynamics: lateral forces caused by horizontal winds, inclination of the road surface, speed of movement of the vehicle acting on the entire body create centrifugal inertial forces; this force acts to cause the tires to deform. At this point the vehicle is shifted from its original trajectory. In the research content, the authors built a lateral Dynamics Model, in which the influencing factors are set as inputs to the model including vehicle velocity and transverse wind forces, output is the rotation angle of the body, transverse acceleration and horizontal displacement of the vehicle. The simulation results showed that the effect of vehicle speed was relatively large: lateral displacement increased by 228 % at a speed of 120 km/h. Therefore, when driving on the highway, there should always be driver control to ensure that the car does not deviate from the lane

Water Trash Collector

Water Trash Collector

Game Theory-Based Interactive Control for Human–Machine Cooperative Driving

To address the safety and efficient driving issues of human–machine shared control vehicles (HSCVs) in future complex traffic environments, this paper proposes a game theory-based interactive control method between HSCVs and surrounding autonomous vehicles (SVs) and involves considering different driving behaviors. In HSCV, a comprehensive driver model integrating steering control and speed control is designed based on the brain emotional learning circuit model (BELCM), and the control authority between the driver and the automation system is dynamically allocated through the evaluation of the driving safety field. Factors such as driving safety and travel efficiency that reflect personalized driving style are considered for modeling the uncertain behavior of SVs. In the interaction between HSCVs and SVs, a method based on game theory and distributed model predictive control (DMPC) that considers the uncertainty of SVs’ driving behavior is established and is finally integrated into a multi-objective constraint problem. The driver control input in an HSCV will also be introduced into the solution process. To demonstrate the feasibility of the proposed method, two test scenarios considering the driving characteristics of different SVs are established. The test results show that automation control systems can promptly stop the human driver’s dangerous operations in an HSCV and safely interact with multiple AVs with different driving characteristics.

Concepts for realizing High-Voltage Power Modules by Embedding of SiC Semiconductors

Today’s power electronics modules typically consist of a ceramics substrate (DBC – Direct Bond Copper), carrying IGBTs, diodes or MOSFETs. These semiconductors are soldered or sintered to the ceramics and their top sides are interconnected by thick Al wires. An integration of further components or functions on the DBC substrate is difficult or even not possible. Therefore, driver circuits and controllers have to be mounted to a separate substrate, typically an organic Printed Circuit board (PCB). The PCB has to be connected to the DBC by wires or pins. The mechanical integration of the whole system requires a bulky housing. Embedding of power semiconductors like SiC MOSFET allows a significant size reduction, improved electrical performance and a high degree of reliability of such modules. In the last years, the capability of PCB embedding technology for the realization of low and high voltage power modules was demonstrated. Fraunhofer IZM together with partners from the industry demonstrated the feasibility for different applications, from single die SiC packages to high voltage automotive traction inverters. In this paper, a general overview about innovative power module technologies will be given and the embedding of power semiconductors will be introduced in detail. It will address all most relevant point, like the demands on the semiconductor, the thermal and electrical considerations as well as the demands on the used materials. To address the integration of the required driver circuits and controllers, the idea of modularization such electronics systems will also be presented. Here already packaged components will be used and embedded into PCB layers too. As a result, a modular approach to form a complete system will be developed. Different functional layers, e.g. power switches, logic modules, will be formed and finally stacked und connected to form the system. The concept and first realized demonstrators will be discussed.

Utilization of PWM Signals in Automatic Parking Gate Speed Control Using PID System Modeling

Utilization of signals (Pulse Width Modulation) on the l298n motor driver and PID controller (Proportional Integral Derivative) is often used in industries, especially those that require robots as their automation medium, so that a process in robot automation does not only design system modeling, but is also required to implement a control system. angular position on a DC motor using the PID model. The PID control system in this study utilizes speed control with a PWM signal in the l298n motor driver and is applied as a DC motor angle position control module which is used for the parking doorstop system. The use of the trial error method is a suitable choice to be applied in finding values for the PID position control. This system keeps the speed and position in accordance with the set point when given a load as well as the ability of the system to catch up with the speed and position in order to reach the set point when the motor starts running are very important factors as a measure of speed and position control performance. because of its effectiveness, in the implementation of the parking gate latch produces a standard configuration of the PID controller with parameters Kp, Ki and Kd that can be determined so that the plant characteristics match the expected design criteria. In the trial error method, the most effective value was obtained with Kp = 10, Ki, 1.9 and Kd = 0.8 which resulted in steady state and no overshoot.

Human–Machine Cooperative Driving and Real-Time Algorithm Implementation Based on Double-Loop Control System

The double loop structure has been implemented to verify the feasibility of human-machine cooperative control in real environment in this paper, which can fully reflect the performance of the controller and realization of the sharing steering control between human driver and automatic control system. The outer loop adopts fuzzy proportional-integral-derivative(PID) controller to deal with different working conditions, on which the complexity of the algorithm is reduced and the real-time performance of the controller is improved. The inner loop adopts the active disturbance rejection controller (ADRC), which can effectively suppress the interference such as drivers’ faulty operations etc. and improve the tracking accuracy and system performance. The stability for extended state observe(ESO) and ADRC is described in theory to ensure the feasibility of the controller in the real world environment. The experiment results show that the human-machine cooperative control system based on the double-loop controller can effectively reduce the lateral error of the vehicle and the steering torque of the driver in the process. And the control performance of the vehicle is improved.

Application of Variational Graph Autoencoder in Traction Control of Energy-Saving Driving for High-Speed Train

In a high-speed rail system, the driver repeatedly adjusts the train’s speed and traction while driving, causing a high level of energy consumption. This also leads to the instability of the train’s operation, affecting passengers’ experiences and the operational efficiency of the system. To solve this problem, we propose a variational graph auto-encoder (VGAE) model using a neural network to learn the posterior distribution. This model can effectively capture the correlation between the components of a high-speed rail system and simulate drivers’ operating state accurately. The specific traction control is divided into two parts. The first part employs an algorithm based on the K-Nearest Neighbors (KNN) algorithm and undersampling to address the negative impact of imbalanced quantities in the training dataset. The second part utilizes a variational graph autoencoder to derive the initial traction control of drivers, thereby predicting the energy performance of the drivers’ operation. An 83,786 m long high-speed train driving section is used as an example for verification. By using a confusion matrix for our comparative analysis, it was concluded that the energy consumption is approximately 18.78% less than that of manual traction control. This shows the potential and effect of the variational graph autoencoder model for optimizing energy consumption in high-speed rail systems.