Vehicle Speed Control Research Articles

Self Balancing Robot

Abstract: Self-regulation robot is among the demainding topic and the working principle behind it is widely used in the humanoids, transportation and robotic hands in industries for improvemising the capaibility and features. In this project, we are working on the layout, control mechanisum, and draft of self-propelled robot. The robot is equipped with stepper motor, PID controller, accelerometer and gyroscope that are used to measure the robot motions and along the x and y axis. In addition, we have focused on the mathematical modeling and simulation of robot. This robot is designed to use analog data to control vehicle speed efficiently. This data is used to feed variation in the pulse width of the motor to reset the robot angle to zero

Investigating factors of pedestrian safety using ordinal logistic regression in Addis Ababa City roads

Pedestrians are the most victimized group in traffic accidents, and this has raised concerns about pedestrian safety in urban Ethiopia. The aim of this study is to determine the factors that affect pedestrian safety in Addis Ababa city Roads. This study employed a concurrent mixed-methods research method and an explanatory research design. Perception-based pedestrian safety data was collected from respondents’ key informants and personal observation. Exploratory factor analysis was employed to identify significant variables. Besides, the fitness of each variable was evaluated under the assumption of ordinal logistic regression, and the effect size of significant variables was determined using the odds ratio. The result indicates pedestrians are 0.065, 0.181, 0.296, 0.324, 0.289, 0.306, 0.038, and 0.193 times less likely to be safe due to narrow sidewalk width, poor sidewalk and footpath pavement comfort, inclusivity problems of sidewalks and footpaths, illegal vehicle parking, street vending, fast vehicle speed, and absence of traffic signs and lights, respectively. Consequently, the use of ordinal logistic regression offers a practical and effective means of determining pedestrian safety factors. Better pedestrian facilities should be constructed by considering pedestrian flow and diversity. Besides, the concerned body should provide spaces for street vendors and car parking and enforce traffic rules in the appropriate system to control vehicle speed.

Analysis of the Application of Deep Learning Technology in Autonomous Driving Systems

The purpose of this paper is to analyse and discuss the application forms of existing autonomous driving technology with a focus on deep learning, while also describing the opportunities and challenges currently faced by this technology. By combining a literature review, this paper sorts out the components of autonomous driving technology and lists several typical and effective application examples, providing an overview of their development ideas and principles. Examples include traffic sign recognition based on visual technology, pedestrian trajectory prediction, vehicle speed control based on algorithms, and lane-level path planning. Research shows that deep learning technology can significantly improve the accuracy and stability of autonomous driving technology in complex environments. However, this technology is still in the development and exploration stage, with issues such as safety risks and data privacy. In summary, deep learning is indispensable in autonomous driving technology, but its characteristics also determine that mature applications require the support of comprehensive regulations and data systems. Future research should focus on the development of emerging technologies while also concentrating on this aspect to promote the construction of intelligent transportation systems.

Smart Zone-Based Vehicle Speed Retarding System Utilizing Radio Frequency (RF) in Electronic Vehicles

The proposed system aims in designing an efficient automatic wireless vehicle speed control in low-speed zones using RF communication. The advent of new high-speed technology and the growing computer capacity provided realistic opportunity for new robot controls and realization of new methods of control theory. This technical improvement together with the need for high performance robots created faster, more accurate and more intelligent robots using new robots control devices, new drivers and advanced control algorithms. The proposed method uses RF transmitter and receiver modules for establishing wireless communication, DC motors for the vehicle movement controller. The status of the project will display on LCD display. The controlling device of the whole system is a Microcontroller to which RF receiver module, DC motors are interfaced through a motor driver. Whenever the vehicle enters into a low-speed zone, the data from RF transmitter of low-speed zone will be received by the vehicle system. The microcontroller in the vehicle system automatically slows down the speed to the speed limit of that zone. The microcontroller checks the data with the program embedded in it and performs appropriate actions on the electric motors. The microcontroller is programmed using Embedded C language.

Dynamic Network-Level Traffic Speed and Signal Control in Connected Vehicle Environment.

The advent of connected vehicles holds significant promise for enhancing existing traffic signal and vehicle speed control methods. Despite this potential, there has been a lack of concerted efforts to address issues related to vehicle fuel consumption and emissions during travel across multiple intersections controlled by traffic signals. To bridge this gap, this research introduces a novel technique aimed at optimizing both traffic signals and vehicle speeds within transportation networks. This approach is designed to contribute to the improvement of transportation networks by simultaneously addressing issues related to fuel consumption and pollutant emissions. Simulation results vividly illustrate the pronounced the effectiveness of the proposed traffic signal and vehicle speed control methods of alleviating vehicle delay, reducing stops, lowering fuel consumption, and minimizing CO2 emissions. Notably, these benefits are particularly prominent in scenarios characterized by moderate traffic density, emphasizing the versatility and positive impact of the method across varied traffic conditions.

Use of Wireless Sensor Networks for Area-Based Speed Control and Traffic Monitoring

This paper reviews the potential of low-power wireless networks to improve road safety. The authors characterized this type of network and its application in road transport. They also presented the available technologies, highlighting one that was considered the most promising for transport applications. The study includes an innovative and proprietary concept of area-based vehicle speed monitoring using this technology and describes its potential for enhancing road safety. Assumptions and a model for the deployment of network equipment within the planned implementation area were developed. Using radio coverage planning software, the authors conducted a series of simulations to assess the radio coverage of the proposed solution. The results were used to evaluate the feasibility of deployment and to select system operating parameters. It was also noted that the proposed solution could be applied to traffic monitoring. The main objective of this paper is to present a new solution for improving road safety and to assess its feasibility for practical implementation. To achieve this, the authors conducted and presented the results of a series of simulations using radio coverage planning software. The key contribution of this research is the authors′ proposal to implement simultaneous vehicle speed control across the entire monitored area, rather than limiting it to specific, designated points. The simulation results, primarily related to the deployment and selection of operating parameters for wireless sensor network devices, as well as the type and height of antenna placement, suggest that the practical implementation of the proposed solution is feasible. This approach has the potential to significantly improve road safety and alter drivers′ perceptions of speed control. Additionally, the positive outcomes of the research could serve as a foundation for changing the selection of speed control sites, focusing on areas with the highest road safety risk at any given time.

A new system for underwater vehicle balancing control based on weightless neural network and fuzzy logic methods

<p>The utilization of humans to be in the water for short time, resulting in limited area underwater that can be explored, so the information obtained is very limited, plus the influence of irregular water movements, changes in waves, and changes in water pressure, indirectly also constitutes obstacle to this problem. One of the best solutions is to develop underwater vessel that can travel either autonomously or by giving control of movement and navigation systems. New system for underwater vehicle balance control through weightless neural network (WNN) and fuzzy logic methods was proposed in this study. The aim was to simplify complicated data source on stability system using WNN algorithm and determine depth level of autonomous underwater vehicle (AUV) through fuzzy logic method. Moreover, speed control of underwater vehicle was determined using fuzzy rule-based design and inference. The tests were conducted by showing convergence performance of system in the form of AUV simulator. The results showed that proposed system could produce real-time motion balance performance, faster execution time, and good level of accuracy. This study was expected to produce real-time motion balance system with better performance, faster execution time, and good level of accuracy which could be subsequently used to design simple, cheap, and efficient hardware prototype.</p>

Enhanced PD Controller for Speed Control of Electric Vehicle Based on Gorilla Troops Algorithm

Enhanced PD Controller for Speed Control of Electric Vehicle Based on Gorilla Troops Algorithm

Increasing the energy efficiency of an electric vehicle powered by hydrogen fuel cells

The article considers fuel cell electric vehicle energy efficiency improvement by means of traction battery remanent state of charge modern control algorithms application which based on fuzzy logic. Fuel cell vehicle traction system used as experimental setup for the study includes brushless dc motor, lithium-ion batteries pack and electronic differential approach implementation. The results of simulation modelling in MATLAB-Simulink software comparing conventional PID-controller based and fuzzy logic based controller for vehicle speed control systems are presented. Fuzzy logic controller provides an electric vehicle dynamic behavior tuning for different driving modes automatically transferring the control system to economy mode when it possible. However, the control system is able to provide fast dynamic behavior when it is required. The proposed control algorithm is tested by means of developed experimental setup. Experimental results validate conducted simulation modelling results and the proposed control algorithm. Application of the proposed fuzzy logic control algorithm to control the electric vehicle dynamic behavior allows energy efficiency improvement and driving range increase.

Switched capacitor voltage boost converter for BLDC motor speed control of electric vehicles

BLDC motors are extensively used in various industries, including CNC machine tools, industrial robots, and electric vehicles. Because of their compact size, high efficiency, high torque-to-power ratios, and low maintenance requirements due to their brushless operation, BLDC motors are the backbone of many industrial automation systems. However, they pose significant challenges when it comes to speed control. In this study, the speed of BLDC motors is controlled by PI-based speed controllers. In the described method, the BLDC motor is commutated using Hall Effect sensors. A proposed approach uses PI to regulate the speed of BLDC motors in an open-loop PWM method. The speed-controlled BLDC motor is analysed using MATLAB/simulink. The hardware of the proposed system is also implemented.

Enhancement of energy utilization efficiency and speed control of autonomous electric vehicles (AEVs): A hybrid approach

Enhancement of energy utilization efficiency and speed control of autonomous electric vehicles (AEVs): A hybrid approach

IOT Based Automatic Electric Vehicle Accident Prevention and Speed Control

IOT Based Automatic Electric Vehicle Accident Prevention and Speed Control

Ramp Start and Speed Control of Self-Driving Commercial Vehicles under Ramp and Vehicle Load Uncertainty

In order to improve the performance of self-driving commercial vehicles for half-hill starting, a ramp control strategy based on the back-slip speed corresponding to the parking moment is proposed. Firstly, the longitudinal dynamics model of the vehicle is established, the force of the vehicle on the ramp is analyzed, and the rear slip speed of the vehicle is matched with the parking moment, and finally the target speed is tracked based on the sliding-mode controller, and in order to validate the validity of the method, two comparative algorithms of the pure PI controller and the proportional gain controller based on the back-sliding speed corresponding to the resting moment are designed for comparative experiments, and the data results show that the control strategy based on the resting moment corresponding to the backsliding speed of the sliding mode ramp start control strategy can stably complete the ramp start under different weights and different slopes, and greatly reduce the backsliding distance of the vehicle.

Enhanced Autonomous Speed Control System for Integrated Cars

The Automated Vehicle Speed Control System (AVSCS) is an embedded framework that utilizes specialized hardware and software components to automatically regulate a vehicle's speed. This system is designed to be implemented in a variety of vehicles, including cars, trucks, and autonomous vehicles, to enhance safe and efficient driving. The AVSCS comprises several essential elements: sensors, a processor or microcontroller, a control calculation algorithm, and a user interface. The sensors gather real-time data about the vehicle's current speed, weather conditions, and surrounding road environment, including wheel speed, GPS, radar, LIDAR, and cameras. The microcontroller or processor processes the sensor data and employs control algorithms, such as PID (Proportional-Integral-Derivative) controllers or advanced Model Predictive Control (MPC) techniques, to calculate the optimal vehicle speed. This information is then used to adjust the throttle or braking mechanisms accordingly. Additionally, the user interface allows drivers to customize the system or set their desired speed preferences, providing a seamless and personalized driving experience. The integration of the AVSCS into various vehicle types can significantly increase security, streamline traffic,, and reduce energy consumption, making it a crucial component of the connected and autonomous vehicle ecosystem. Keywords: vehicle speed regulator, GPS, radar, LIDAR, and cameras

Research on the optimal speed of vehicles passing speed bumps on the highway based on an immune algorithm

Abstract. With the advancement of vehicle technology, there is a growing demand for vehicle comfort in addition to the focus on safety and functionality. On certain accident-prone sections of highways, such as entrance and exit ramps, tunnels, and downhill stretches, continuous speed bumps are typically installed to remind vehicles to reduce their speed. However, while enhancing safety, these measures also introduce a degree of discomfort for passengers and drivers alike. Vehicle speed and the type of road speed bump are key factors influencing vehicle comfort. In order to improve the ride comfort, this paper investigates the problem of adaptive speed control for vehicles passing over different types of continuous speed bumps and proposes a method for solving the optimal speed. In this research, a 4-degree-of-freedom vehicle suspension model and a road excitation model are employed to simulate vehicle vibrations. Simulation optimisation is performed using MATLAB in conjunction with an immune algorithm to obtain the optimal vehicle speeds for traversing three types of continuous speed bumps – sinusoidal, rectangular, and trapezoidal – while adhering to specified constraints. The simulation results demonstrate that this optimisation algorithm effectively enhances the ride comfort of vehicles when navigating speed bumps. The algorithm, when applied, reduces vehicle vertical displacement, acceleration, suspension deflection, and tyre load to varying degrees when crossing speed bumps. It also reduces tyre ground clearance to some extent, achieving a balance between comfort and safety. Furthermore, the study identifies the range of comfortable vehicle speeds for traversing these three types of speed bumps, providing valuable insights for selecting the appropriate speed bump design on roads with varying speed limits.

Embedded Vehicle Speed Control and Over-Speed Violation Using IoT

Abstract: In our daily life we come across so many road accidents resulting in affecting humans and animals due to different reasons like over-speeding and neglecting alerts. This study aims to overcome that problem by using sensors, GSM Modem, GPS Module, gear motor and Arduino UNO. Vehicles motion is detected by IR sensors and their speed is calculated, if the speed of the vehicle is more than the speed limit an alert will be sent. If the speed of the vehicle is still the same alert is sent to nearby officials and the speed of the vehicle is automatically reduced.

Sliding Mode Control of an Electric Vehicle Driven by a New Powertrain Technology Based on a Dual-Star Induction Machine

This article examines a new powertrain system for electric vehicles based on the dual-star induction machine, presented as a promising option due to its significant advantages in terms of performance, energy efficiency, and reliability. This system could play a key role in the evolution of electro-mobility technology. The dual-star induction machine reduces electromagnetic torque fluctuations, limits current harmonics, improves power factor, and enables half-speed operation. Our study focuses on the control strategy and operation of the traction chain for electric vehicles propelled by the dual-star induction machine (DSIM) using Matlab software with version 2017. We integrate the battery as the main energy source, along with three-level static converters for energy conversion in the vehicle’s four operating quadrants. We have opted for sliding mode control, which has proven to be feasible and robust against external disturbances. Although we have modeled driver behavior, we consider it as an aspect of control, to which we add the driving profile to guide our evaluation of the control to be used for vehicle operation. The results of our study demonstrate the reliability and robustness of DSIM for electric vehicle motorization and speed control. Promoting this technology is essential to improve the overall performance and efficiency of electric vehicles, especially in traction and braking modes for energy recovery. This underscores the importance of DSIM in the sustainable development of the electric transportation system.

Optimized Speed Control for Electric Vehicles on Dynamic Wireless Charging Lanes: An Eco-Driving Approach

Optimized Speed Control for Electric Vehicles on Dynamic Wireless Charging Lanes: An Eco-Driving Approach

Research on vehicle speed control method based on spatiotemporal data and vehicle-road-cloud cooperation

Research on vehicle speed control method based on spatiotemporal data and vehicle-road-cloud cooperation

Part Two: Neural Network Controller for Hydrogen-CNG Powered Vehicle

Background: The control system of the vehicle regulates parameters like fuel flow control, vehicle speed control, tracking, etc. Objective: The main objective of the paper is to monitor and determine an efficient, and automated control system for an H-CNG-powered vehicle. Using neural networks and machine learning, we would develop an algorithm for the controller to regulate the speed of the car with the help of variables involved during the runtime of the vehicle. Methods: Initially, Generating a dataset with the help of formulation and computation for training. Further, analysing different supervised machine learning algorithms and training the Artificial Neural Network (ANN) using the generated dataset to predict and track the gains of the H-CNG vehicle accurately. Results: Analysis of the gains of the H-CNG vehicle are presented to understand the precision of the trained Neural Network. Conclusion: The final verdict of the paper is that the Neural Network is successful in tracking the gains of the H-CNG vehicle with the help of the dataset presented for training using the Random Forest Regression technique for machine learning.