Driver Operation Research Articles

Wide voltage gain isolated LCC resonant converters for LED driver applications

AbstractThis article proposes an isolated high‐efficiency resonant LED driver with wide‐input voltage range. The proposed converter is developed by integrating LCC resonant converter and buck‐boost converter. In this topology, the LCC network provides many features including the integration of buck‐boost filter inductor and the transformer magnetizing inductance. In addition, the LCC network provides zero voltage switching (ZVS) operation of the main switches and zero current switching (ZCS) operation of the rectifier diodes over a wide variation of duty cycle operation. Also, load‐independent output current characteristic with improved efficiency is obtained at the resonant frequency operation which is well‐suited for LED applications. Other topology variations of the proposed converter generating step‐up and step‐down voltage gains are introduced for applications with different input voltage ranges. To validate the theoretical analysis and LED driver operation, a 50 W laboratory prototype of the proposed LED driver is implemented for the wide input voltage of 18–40 V to deliver 500 mA output current to a string of 1–30 LEDs, which corresponds to an output voltage range of 3.3–100 V.

Smart tachographs application in monitoring and controlling the Operation of Vehicles and Drivers in „ITS“ Environment

ITS systems enable information transparency, manageability and improved response of the traffic system, so they have the epithet of intelligent. The application of ITS solutions on tachographs, as devices that record all the activities of truck and bus drivers, is defined in the ISO 14813-1:2015 Standard in the context of remote data transmission from tachographs. In view of the growing problems regarding violations of regulations on working hours and mandatory vacations for mobile workers, frequent manipulations and misuse of tachographs, as well as regarding the state of traffic safety and increasing environmental pollution, the EU has set stricter conditions in the field of driving these commercial vehicles in its regulations, in the manner of implementing the ITS system of smart tachographs, which brings significant benefits, especially in terms of the work of control authorities as entities that influence better compliance with regulations and the enforcement of legal regulations.

Robust Platoon Control of Mixed Autonomous and Human-Driven Vehicles for Obstacle Collision Avoidance: A Cooperative Sensing-Based Adaptive Model Predictive Control Approach

Obstacle detection and platoon control for mixed traffic flows, comprising human-driven vehicles (HDVs) and connected and autonomous vehicles (CAVs), face challenges from uncertain disturbances, such as sensor faults, inaccurate driver operations, and mismatched model errors. Furthermore, misleading sensing information or malicious attacks in vehicular wireless networks can jeopardize CAVs’ perception and platoon safety. In this paper, we develop a two-dimensional robust control method for a mixed platoon, including a single leading CAV and multiple following HDVs that incorporate robust information sensing and platoon control. To effectively detect and locate unknown obstacles ahead of the leading CAV, we propose a cooperative vehicle–infrastructure sensing scheme and integrate it with an adaptive model predictive control scheme for the leading CAV. This sensing scheme fuses information from multiple nodes while suppressing malicious data from attackers to enhance robustness and attack resilience in a distributed and adaptive manner. Additionally, we propose a distributed car-following control scheme with robustness to guarantee the following HDVs, considering uncertain disturbances. We also provide theoretical proof of the string stability under this control framework. Finally, extensive simulations are conducted to validate our approach. The simulation results demonstrate that our method can effectively filter out misleading sensing information from malicious attackers, significantly reduce the mean-square deviation in obstacle sensing, and approach the theoretical error lower bound. Moreover, the proposed control method successfully achieves obstacle avoidance for the mixed platoon while ensuring stability and robustness in the face of external attacks and uncertain disturbances.

Information density threshold of urban road traffic signs based on visual comfort

The content and density of traffic signs directly affect the operation of urban road traffic and drivers. To overcome the limitations of quantitative research on the density threshold of traffic signs on urban roads, a real vehicle experiment was conducted to record the psychological characteristics of drivers. Four psychological parameters of drivers—pupil area, fixation intensity, heart rate change rate, and heart rate variability—were explored. Subsequently, principal component analysis was used to present a new index, S, divided into 5 grade scales, to represent the driving visual comfort level. The information entropy theory was applied to quantify the amount of information on road traffic signs that are included in driving tests, and a regression relationship between the traffic sign information and comfort index S was established. The visual psychological load thresholds for different comfort levels were −2.289≤S < −1.526 for very comfortable, 1.526≤S < −0.763 for relatively comfortable, −0.763≤S ≤ 0.763 for comfortable, 0.763<S ≤ 1.526 for uncomfortable, and 1.526<S ≤ 2.289 for very uncomfortable. To maintain the visual comfort of drivers, the information density of traffic signs should be less than 0.373 bits/m and the maximum information density of traffic signs should not exceed 0.507 bits/m. This conclusion provides a reference for the rational layout of traffic signs on urban roads.

Repetitive high-power discharge operation of a micro-laser triggered gas switch.

A low-energy, diode-pumped, solid-state passively Q-switched laser operating at a wavelength of 1535nm has been demonstrated to trigger gas switches. This technology has the potential to bring significant improvements to the operation and reliability of compact pulsed power drivers. Compact pulsed power drivers are often required to operate for hundreds to thousands of shots without maintenance; thus, the reliable operation of the laser triggered switch is significant for the adoption of this technology. A primary limiter of spark gap lifetime is electrode erosion. In the case of a laser triggered switch where the laser is focused on one electrode, an additional source of erosion is the direct ablation of the electrode material by the laser. The micro-laser used in the previous experiments is very low energy, but over thousands of shots, it does damage directly to the target electrode. This damage could potentially interact with the erosion due to the current discharge and impact the operation of the switch. The focus of this paper is to describe experiments undertaken to assess this damage and quantify its effect on switch operation over many discharges at current and energy levels relevant to compact pulsed power systems.

Research on the fatigue level of underground mine truck drivers with integration of cardiovascular and brain features

The fatigue of miners is one of the primary factors influencing unsafe behaviors in mining. To accurately identify the fatigue status of miners, prevent coal mine accidents, and simulate the loading and unloading operations of underground mine truck drivers.Collecting electroencephalogram (EEG) and electrocardiogram (ECG) feature indicators of mine truck drivers during different work periods, using SPSS software to conduct correlation analysis on the collected fatigue feature indicators, and studying the changes in fatigue feature indicators before and after the operation of mine truck drivers. The results indicate that when the underground mine truck drivers' brainwave band entropy (β/ θ)exceeds the fatigue threshold of 60 or the HR_MEAN heart rate index is below 0.05uv, the drivers are in a state of severe fatigue. The fatigue levels of underground mine truck drivers vary during different work periods, with drivers at 13:00 experiencing the most severe fatigue. The study found that reducing the midday working hours for underground mine truck drivers can effectively decrease the occurrence of fatigue.

A Load-Adaptive Driving Method for a Quasi-Continuous-Wave Laser Diode.

A quasi-continuous-wave (QCW) laser diode (LD) driver is commonly used to drive diode bars and stacks designed specifically for QCW operations in solid-state lasers. Such drivers are optimized to deliver peak current and voltage pulses to LDs while maintaining low average power levels. As a result, they are widely used in laser processing devices and laser instruments. Traditional high-energy QCW LD drivers primarily use capacitors as energy storage components and pulsed constant-current sources with op-amps and power metal-oxide-semiconductor field-effect transistors (MOSFETs) as their core circuits for generating repeated constant-current pulses. The drawback of this type of driver is that the driver's output voltage needs to be manually adjusted according to the operating voltage of the load before use to maximize driver efficiency while providing a sufficient current. Another drawback is its inability to automatically adjust the output voltage to maintain high efficiency when the load changes during the driver operation. Drastic changes in the load can cause the driver to fail to function properly in extreme cases. Based on the above traditional circuit structure, this study designed a stability compensation circuit and realized a QCW LD driver for driving a GS20 diode stack with a maximum repetition rate of 100 Hz, a constant current of approximately 300 A, a load voltage of approximately 10 V, and a pulse width of approximately 300 μs. In particular, a high-efficiency, load-adaptive driving method was used with the MOSFETs in the critical saturation region (i.e., between the linear and saturated regions), controlling its power loss effectively while achieving maximum output current of the driver. The experiments demonstrated that the driver efficiency could be maintained at more than 80% when the load current varied from 50 to 300 A.

IoT Enable Grass Cutter Robot - Car

Abstract: The IoT Enabled Grass Cutter Robo-Car project represents a transformative leap in the realm of lawn maintenance, merging cutting-edge robotics with the expansive capabilities of the Internet of Things (IoT). At its core, this project introduces a state-of-the-art Robo-Car, meticulously engineered to be remotely controlled through an Android application. The crux of the communication infrastructure is a web server, with the ESP8266 serving as the linchpin by receiving signals from the server. These signals orchestrate the nuanced operations of a L298N motor driver, steering the precise movement of 100 RPM motors, and a relay module that commands the activation of the grass cutter motor. With a vision to redefine the landscape of lawn care, this project stands as an exemplar of efficiency, convenience, and remote accessibility in the realm of smart agriculture.

AUTOMATED NAVIGATION FOR UNMANNED GROUND VEHICLES IN LOGISTICS

The use of ground-based courier robots is widespread in the systems that integrate the complex automation of transportation management. Most often, the robot's main problems are finding and avoiding obstacles on the way during cargo transportation. Many computing resources are used to model the route taking into account the features of the terrain during logistic navigation. In research, the choice of gear motors, motor drivers, microcontrollers, and microcomputers was justified to ensure movement. To ensure effective autonomous navigation, the robot must contain ToF, LiDAR, ultrasonic sensors, GPS modules, cameras, an accelerometer, and a magnetometer. The selected combination of the investigated hardware components will allow for accurately determining the location of the vehicle. The article analyses the components of the robot. The principle of operation of drivers and engines is defined. Two models of drivers and two models of engines were considered. A schematic of the device with all connected sensors, a controller, and a microcomputer was developed. The principle of interaction of all system components to ensure autonomous navigation was developed. For the long-term operation of the system with autonomous navigation, the power supply system and the capacity of the necessary battery were calculated. A neural network was created and used for object detection. For neural network calculations, a server-side was created that receives frames from the device's camera and returns data on detected objects to the autonomous vehicle.

Shared control for human–machine co-driving vehicles based on constraint-following approach

To ensure the human–machine co-driving vehicle path-tracking accuracy, as well as to diminish the effects of vehicle underactuated characteristics and undesirable driver operation, a parallel shared control framework is proposed in this paper. By structuring a rational driving weights allocation module, the framework enables cooperative driving between the driver and automated system. First, for the front steering vehicle, a robust path-tracking controller is proposed using the constraint-following approach. The system uncertainty is decomposed into matched and non-matched uncertainties. As the non-matched portions are orthogonal to the constraint-following geometric space, they will not affect control performance. Therefore, the constraint-following control (CFC) is designed to handle the path-tracking equality constraint, initial state deviations, and matched uncertainties. Second, a fuzzy rule-based driving weights allocation module is proposed. According to the lateral deviation and the relative accuracy of the steering angle, the allocation module adjusts the driving weight of the driver and automated system to improve the vehicle condition. Finally, the simulation results show that the framework can effectively reduce the emergence caused by the driver or automated system, and significantly improve the path-tracking performance. Additionally, as the controller exhibits sufficient robustness, it can handle multiple sources of uncertainty in vehicle systems.

WISDOM: WiFi Improved Safe Driver Operation Monitoring With CSI

WISDOM: WiFi Improved Safe Driver Operation Monitoring With CSI

Analisis Sistem Informasi Driver Maxim Indonesia Menggunakan Pendekatan Evaluasi Usability Heuristik dan Human Resource Information System

Driver information system analysis includes evaluation and in-depth understanding of various aspects of the system used by drivers in a service or platform. Driver information system analysis helps ensure that the technology infrastructure that supports driver operations in application-based transportation services is efficient, safe, and in line with business and driver needs. This research aims to analyze the usability of the driver information system with a case study of the Maxim driver information system. The method used to analyze Maxim drivers uses the Heuristic Evaluation (HE) and Human Resource Information System (HRIS) approaches. The research results found that the Maxim driver information system is easy to use and the information contained in it is quite clear.

Deep learning assisted three triboelectric driving operation sensors for driver training and behavior monitoring

Improving the driving skills of drivers, particularly during the training stage, is crucial in reducing the likelihood of road traffic accidents. In this work, a driver training assistance system (DTAS) is developed for driver training and behavior monitoring. The DTAS integrates three triboelectric driving operation sensors, including gear shift sensor, steering angle sensor, and pedal sensors. Through the ingenious structural design of contact-separation and freestanding-triboelectric-layer mode, these triboelectric sensors have the characteristics of simple structure, easy manufacture and installation, and self-powered, which avoids the complex wiring problem in the limited space of the vehicle. The basic electrical performance test of triboelectric sensors and driving simulation experiment show that the developed DTAS can monitor the driver behavior and provide the feedback on each driver’s operation process in real-time. Combined with deep learning (DL) technology, the DTAS can identify whether the driving operation of drivers in specific training scenarios is correct or not, with an accuracy rate of 97.5%. This work is aimed at assisting the novice or learner drivers in driving training, which helps to improve their driving skills and form good driving habits. The proposed scheme can provide new ideas for the innovative exploration of driving training modes without coaching.

Study on Intelligent Braking Energy Recovery System

Nowadays, traditional internal combustion engine vehicles use fossil fuels, emitting exhaust gases and pollutants, making a negative impact on air quality and the environment. The intensification of global climate change also makes it particularly important to reduce greenhouse gas emissions. In order to meet the coexistence of the environment and human development, electric vehicles(EV), as a zero-emission transportation mode, can reduce air pollution and carbon emissions, and improve urban air quality. The number of EVs in the world has increased significantly, and the technology of EVs is constantly innovating. In this article, the brake system is extracted separately from the four parts of the traditional EV and treated as the fifth part to work together with the other four parts.This article analyzes the optimization of motor machinery, power recovery and release, and the driver operation and operation safety. In addition, this article also discusses how to effectively alleviate the problems of insufficient power and short battery life of EVs. This article may offer a reference for the development of EVs.

Effects of Aging on Taxi Service Performance: A Comparative Study Based on Different Age Groups

Rapid population aging has significantly impacted labor supply and posed substantial challenges for the taxi industry, which have not yet been fully comprehended. Here, for the first time, we employ a large-scale dataset of taxi driver operations from China, establishing a comprehensive indicator system of taxi service performance, encompassing economic, environmental, and safety aspects. Through the application of multivariate regression models and other statistical analysis techniques, we have thoroughly investigated the mechanisms through which aging influences taxi service performance. Our research reveals that older drivers, despite exhibiting higher operational efficiency and greater inclination towards stability, underperform in time efficiency metrics, such as income per hour worked, owing to a more conservative working style. Furthermore, aging manifests negative effects on safety and environmental performance. Adjusting the driving strategies of older taxi drivers, such as regulating daily working hours and refining passenger-searching area preferences, can help mitigate these adverse impacts.

Detection of driver drowsiness level using a hybrid learning model based on ECG signals.

Fatigue has a considerable impact on the driver's vehicle and even the driver's own operating ability. An intelligent algorithm is proposed for the problem that it is difficult to classify the degree of drowsiness generated by the driver during the driving process. By studying the driver's electrocardiogram (ECG) during driving, two models were established to jointly classify the ECG signals as awake, stress, and fatigue or drowsiness states for drowsiness levels. Firstly, the deep learning method was used to establish the model_1 to predict the drowsiness of the original ECG, and model_2 was developed using the combination of principal component analysis (PCA) and weighted K-nearest neighbor (WKNN) algorithm to classify the heart rate variability characteristics. Then, the drowsiness prediction results of the two models were weighted according to certain rules, and the hybrid learning model combining dilated convolution and bidirectional long short-term memory network with PCA and WKNN algorithm was established, and the mixed model was denoted as DiCNN-BiLSTM and PCA-WKNN (DBPW). Finally, the validity of the DBPW model was verified by simulation of the public database. The experimental results show that the average accuracy, sensitivity and F1 score of the test model in the dataset containing multiple drivers are 98.79, 98.81, and 98.79 % respectively, and the recognition accuracy for drowsiness or drowsiness state is 99.33 %. Using the proposed algorithm, it is possible to identify driver anomalies and provide new ideas for the development of intelligent vehicles.

Soft‐switched full‐bridge light‐emitting diode driver with reduced rectifier components

SummaryIn this paper, a new light‐emitting diode (LED) driver based on full‐bridge series resonant converter (SRC) is presented. In the proposed converter LED, load is placed in between DC source and the input side of the full‐bridge inverter which reduces the rectifier components of the SRC. The proposed converter reduces the number of diodes used for rectification and ensures zero voltage switching of the power switches which improves the efficiency of the converter. LED lamp current is regulated with phase‐shift pulse width modulation (PWM) of full‐bridge. Lamp illumination is controlled with PWM dimming. The operation and mathematical analysis of LED driver is presented. To validate the proposed concept, a 40‐W laboratory prototype is developed. Experimental results are presented. Peak efficiency of 93% is obtained.

Comparative Analysis of High-Frequency Plasma Drivers with Various Protective Screens for Atomic Injectors with Multi-Second Pulse Duration

Atomic beam injection is one of the main methods of plasma heating in thermonuclear facilities. An injector of high-energy hydrogen atoms for plasma heating based on the acceleration and neutralization of negative hydrogen ions is being developed at the Budker Institute of Nuclear Physics, Siberian Branch of the Russian Academy of Sciences. The injector uses a surface plasma source, in which a plasma flow is created using a radio-frequency driver: an induction radio-frequency (RF) discharge, ignited inside a cylindrical ceramic chamber when an RF voltage is applied to an external antenna. As a part of this work, a new version of the RF driver is being developed. A protective screen is used to prevent overheating and erosion of the ceramic wall of the driver. The operation of RF driver with different protective screens is studied. These screens reduce the efficiency of RF power transmission into the discharge, but make it possible the operation of the ion source in multi-second or steady-state pulses.

A Pure Electric Driverless Crawler Construction Machinery Walking Method Based on the Fusion SLAM and Improved Pure Pursuit Algorithms.

Driverless technology refers to the technology that vehicles use to drive independently with the help of driverless system under the condition of unmanned intervention. The working environment of construction machinery is bad, and the working conditions are complex. The use of driverless technology can greatly reduce the risk of driver operation, reduce labor costs and improve economic benefits.Aiming at the problem of the GPS positioning signal in the working environment of construction machinery being weak and not able to achieve accurate positioning, this paper uses the fusion SLAM algorithm based on improved NDT to realize the real-time positioning of the whole vehicle through reconstruction of the scene. Considering that the motion characteristics of crawler construction machinery are different from those of ordinary passenger cars, this paper improves the existing pure pursuit algorithm. Simulations and real vehicle tests show that the algorithm combined with the fusion SLAM algorithm can realize the motion control of driverless crawler construction machinery well, complete the tracking of the set trajectory perfectly and have high robustness. Considering that there is no mature walking method of driverless crawler construction machinery for reference, the research of this paper will lay a foundation for the development of driverless crawler construction machinery.

Research on Regenerative Braking Control Strategy for Single-Pedal Pure Electric Commercial Vehicles

In recent years, with the increasing severity of energy and environmental issues, countries have vigorously developed the new energy automotive industry. To reduce the difficulty of driver operation and increase endurance mileage, this article proposes a regenerative braking control strategy for a single-pedal pure electric commercial vehicle. Firstly, the single-pedal control system’s hierarchical approach was designed to contain the driver’s intention analysis and torque calculation layers. After identifying the driver’s intention, a logic threshold method was used to determine the braking pattern. Then, a fuzzy theory was used, with road gradient, braking strength, and speed as input parameters, and the ratio coefficient of braking force as the output parameter. A hybrid regenerative braking strategy was formulated based on the ideal distribution curve. Finally, the proposed strategy was verified through simulation and a constant-speed car-following experiment. The constant-speed car-following experiment results show that the maximum optimization rate of energy consumption provided by the proposed single-pedal regenerative braking control strategy is 5.81%, and the average optimization rate is 4.33%. This strategy can effectively reduce energy consumption and improve the economic performance of single-pedal pure electric commercial vehicles.