Vehicle Dynamics Research Articles

Path tracking with personalized handling stability constraints matched to driving styles

In the path-tracking control process, drivers have different expectations of the lateral response of vehicles. To meet drivers’ expectations of handling stability, this paper proposes a model predictive control (MPC)-based path-tracking controller with personalized handling stability constraints matched to driving style. Firstly, the categories of driving styles are obtained by applying the K-Means algorithm to the collected driving data. Then the sideslip angle-longitudinal velocity map is constructed based on the nonlinear dynamics model of the vehicle to generate the front-wheel steering angle constraints that are matched with the maximum sideslip of the vehicle expected by drivers of different styles. After that, an MPC-based path-tracking controller has been built, and the front-wheel steering angle is incorporated into the MPC controller. Furthermore, compared to the strategy of directly using sideslip angle constraints for handling stability control, this approach avoids introducing additional constraint variables, leading to improved real-time performance. Simulation experiments are conducted to compare the dynamic vehicle responses between the handling stability control that takes into account the driving style and the control system that does not consider the driving style. In addition, the subjective evaluation is conducted to verify the proposed path-tracking control method that considers the stability constraints of driving style. The evaluation results show that the proposed method can obtain better subjective evaluation results than a path-tracking controller that does not consider the driving style. Finally, through the hardware-in-the-loop experiments, the real-time computational capability of the proposed controller is verified.

An Alternate Search Method and An Extended Trace Line Method for Wheel‐Rail Contact Patch Centers Detection

AbstractWheel‐rail contact is a link between the vehicle system and the rail system, and the contact processing approach affects accuracy of vehicle dynamic simulation. Contact detection is usually the start of wheel‐rail contact calculation, and detecting precision of contact patch center affect accuracy of contact force calculating results, therefore it is significant to improve detecting precision of contact patch center. An alternate search method (ASM) and an extended trace line method are proposed to improve precision and efficiency of contact patch centers detection. The proposed alternate search method applies Golden Ratio Search Method and variable intervals between grids to detect contact patch centers. To make the detected contact patch centers match rail tangential directions better, an extended trace line method is constructed based on geometrical constraints between contact points and rail tangential directions. Dynamic simulations of a bogie vehicle formed by two wheelsets and a bogie frame negotiating a rail curve are conducted to evaluate effects of the proposed methods. Calculating times of the proposed methods for finishing the simulations are compared to explain computational efficiencies of the different methods. The proposed approaches are real‐time capable and can be used for vehicle dynamics simulations.

Multi-Output Physically Analyzed Neural Network for the Prediction of Tire–Road Interaction Forces

<div>This article introduces an innovative method for predicting tire–road interaction forces by exclusively utilizing longitudinal and lateral acceleration measurements. Given that sensors directly measuring these forces are either expensive or challenging to implement in a vehicle, this approach fills a crucial gap by leveraging readily available sensor data. Through the application of a multi-output neural network architecture, the study focuses on simultaneously predicting the longitudinal, lateral, and vertical interaction forces exerted by the rear wheels, specifically those involved in traction. Experimental validation demonstrates the efficacy of the methodology in accurately forecasting tire–road interaction forces. Additionally, a thorough analysis of the input–output relationships elucidates the intricate dynamics characterizing tire–road interactions. This research underscores the potential of neural network models to enhance predictive capabilities in vehicle dynamics, offering insights that are valuable for various applications in automotive engineering and control systems.</div>

FGO-MFI: factor graph optimization-based multi-sensor fusion and integration for reliable localization

Reliable and precise information pertaining to the position, velocity, and attitude is essential for automated driving. This paper proposes FGO-MFI, a cost-effective and robust multi-sensor fusion and integration localization framework that utilizes factor graph optimization. Firstly, a tightly coupled Global Navigation Satellite Systems (GNSS)/on-board sensor fusion localization framework is established to estimate vehicle states, including position, velocity, and attitude. To address the large drift rate of the Inertial Measurement Unit (IMU), this study introduces a novel IMU/Dynamics pre-integration method based on the vehicle dynamics model. We establish a two-degree-of-freedom vehicle dynamics model utilizing measurements from the wheel speed sensor and steering wheel angle sensor. The IMU/Dynamics factor is devised through a close integration of the model output and IMU pre-integration, enabling the construction of precise odometry with low-cost on-board sensors. Then, to address the issue of the non-Gaussian distribution of GNSS pseudorange error, this paper employs a Gaussian Mixture Model (GMM) to characterize the pseudorange noise, which is then applied to further sensor fusion. Given the time-varying nature of pseudorange noise, the expectation maximization algorithm is utilized to estimate GMM parameters online, leveraging the pseudorange residuals within a sliding window. Comprehensive experiments, inclusive of challenging scenarios such as urban canyons, tunnels, and wooded areas, have been carried out. They affirm the superior performance of the proposed method. Experiments have shown that our method demonstrates reliable localization across different statuses of GNSS signal, exhibiting a 39.4% improvement in the root mean square error of position error when compared to the state-of-the-art. Additionally, this FGO-MFI is a general sensor data fusion framework and is able to incorporate diverse sensor measurements, for example, from cameras and LiDARs to provide more reliable and accurate localization information.

Constructing critical control parameters to keep vehicle away from divergent loss of stability

This paper describes a proposed methodology for the vehicle dynamics modelling and analysis of the stationary states for a two-axle road vehicle subject to a constant external lateral force and a resulting moment acting about a vertical axis passing through the mass centre of the vehicle. The classic nonlinear bicycle model is used and analyses are performed to investigate the stabilisation of straight-line motion through the automated correction of steering inputs to the front wheels. The approach taken here is based on the Pevsner-Pacejka graph-analytical method analysis of a stability set of stationary states in combination with an applicable theory of bifurcation. The disturbing lateral force is typically due to the pressure resulting from a strong side wind gust. The resolution of this force will act through the centre of pressure, taking the vehicle in side profile. The centre of pressure is unlikely to coincide with the centre of mass. This results in the additional disturbing yaw moment acting about a vertical axis through the centre of mass of the vehicle. Depending on the vehicle configuration this moment may introduce a dangerous oversteer response that most drivers would find difficult to manage.

Hand Gesture Controlled Smart Car Using Image Recognition

A hand gesture-controlled smart automobile is the newest initiative in the field of human-computer interaction, and it represents the evolution toward more natural and in- tuitive user interfaces. This paper describes how OpenCV and Google’s MediaPipe are intricately coordinated to produce a control strategy that is both agile and responsive. The deployed technology translates dynamic vehicle motion commands from complex human hand gestures using advanced image recognition algorithms. This is the pinnacle of interactive technology meeting real-world locomotion needs: state-of-the-art computer vision and machine learning combined. The suggested system not only demonstrates the viability of non-contact user input for complex tasks like driving, but it also sets the way for future research in the field of autonomous vehicle guidance and control systems. This study highlights how gesture-based interfaces have the power to completely change the way people interact with cars, paving the way for more flexible and human-centered navigation systems.

Robust cooperative control strategy for a platoon of connected and autonomous vehicles against sensor errors and control errors simultaneously in a real-world driving environment

In a real-world driving environment, a platoon of connected and autonomous vehicles (CAVs) is subject to many internal and external disturbances, resulting in uncertain vehicle dynamics. In general, the disturbances can be categorized into two types: disturbances due to vehicle sensor errors (e.g., GPS error) and disturbances due to vehicle control errors (e.g., actuator delay). In the literature, many control strategies have been proposed to improve the robustness of the CAV platoon against uncertain vehicle dynamics induced by these disturbances. However, most of these strategies only consider one type of disturbance and cannot tackle both types of disturbances simultaneously. Furthermore, they are designed to maximize the benefits of each vehicle in the platoon independently, which can deteriorate the performance of the platoon. To address these problems, this study proposes a robust cooperative control (RCC) strategy to maneuver the vehicles in the platoon cooperatively to counteract the impacts of both types of disturbances. The RCC strategy is developed based on a minimax problem, where the maximization subproblem seeks to find the worst inputs for the uncertainty terms in the vehicle dynamics equation to minimize the platoon performance, while the minimization subproblem seeks to find the optimal control decisions for all subsequent vehicles to maximize the platoon performance in the worst case. To solve the minimax problem, this study proposes a globally convergent solution algorithm. It can solve the minimax problem very efficiently to enable real time deployment of the RCC strategy. Numerical application indicates that compared to the existing methods, the RCC strategy can dramatically improve the robustness of the CAV platoon against the uncertain vehicle dynamics induced by both vehicle state detection errors and vehicle control errors. Therefore, it can maneuver the CAV platoon safely and efficiently in a real-world driving environment.

Merging agent-based simulation and vehicle dynamics: a hybrid approach for value exploration in the mining industry

AbstractInnovation plays a vital role in ensuring sustainable mining operations. Electrification and autonomy are two significant trends, but their implementation brings complexity at vehicle and site levels. Therefore, it is crucial to understand how these technologies impact the overall site value creation. This paper suggests a hybrid approach that combines Agent-Based Simulation and vehicle dynamics modeling to explore site configurations. By regarding a mining site as a System-of-Systems, designers can concurrently test different designs to find the optimal combination for a specific scenario.

Analysis of Energy Consumption for electric motorcycles depending on vehicle configuration and driving cycles

This article presents an analysis of energy consumption for battery electric motorcycles (BEM) based on vehicle configuration and driving cycles. The study investigates the impact of two architectures: rear-wheel regenerative (RWR), and two-wheel regenerative (2WR) on consumption. For the analysis, the study considered the effect of various parameters on the longitudinal vehicle dynamics such as speed profiles (WMTC, real-world profile), longitudinal road slope (from -5° to 5° with 0.5° step increment), and 5 different brake distributions. The research employs simulation techniques using the Matlab/Simulink environment to develop a simplified longitudinal vehicle dynamics model (1 DOF) and for the implementation of a serial brake-blending control strategy. The results of the analysis provide energy potential regeneration and insight into the sensitivity of energy consumption to road slope variations, powertrain working points, and potential energy harvesting. The findings contribute to a valuation of the factors that influence energy consumption in electric motorcycles and have implications for the development of vehicle architectures through accurate range assessment on real-world riding conditions and provide valuable information for powertrain components right-sizing.

Research on long term tracking of centralized power EMU wheel wear and vibration performance

The centralized power EMU is a new type of passenger transportation equipment for ordinary-speed railways in China, which can greatly improve the efficiency of ordinary-speed railway passenger transportation and improve passenger travel conditions. This paper conducts long-term dynamic tracking experiments on this new type of railway passenger car, studying the evolution laws of wheel wear, axle box vibration, bogie frame vibration, and car body vibration during long-term service, and analyzing typical vehicle dynamics phenomena that occur in long-term tracking experiments. Various studies have shown that the vehicle can maintain good lateral stationarity throughout the entire wheel reprofile cycle, and the growth rate of equivalent conicity gradually slows down as the mileage increases; However, when the mileage after wheel reprofiling is large, harmonic vibration may occur in individual sections of the frame, leading to abnormal vibration of the control car body at 10 Hz. The vertical vibration of the axle box will significantly increase at the rail joint, and its 70 Hz vibration is transmitted to the frame through the experiment suspension, stimulating the bending mode of the crossbeam of the frame, resulting in a more obvious 70 Hz vibration in the vertical direction of the frame. The RMS of the axle box, frame, and body sleeper does not show significant changes with the rise in mileage following wheel reprofiling.

Learning Vehicle Dynamics From Cropped Image Patches for Robot Navigation in Unpaved Outdoor Terrains

Learning Vehicle Dynamics From Cropped Image Patches for Robot Navigation in Unpaved Outdoor Terrains

Estimation of the pavement skid resistance by automated vehicle based on one-dimensional convolutional neural network

ABSTRACT The pavement skid resistance is critical to the driving safety of a road. Automated vehicles, which have high requirements for environmental perception, are not yet commonly equipped with an estimation module that could monitor the real-time skid resistance, leading thus to instability in the steering and braking control. In this paper, we propose a data-driven approach incorporating vehicle dynamics implementing real-time pavement skid resistance estimation for autonomous driving. Feature data is collected through the automated vehicle dynamics calibration process, and the selection of features is essentially based on the use of the vehicle dynamics model. To extract the features of one-dimensional timing signals, a temporal one-dimensional convolutional neural network is proposed to improve the stability and robustness of the estimation and minimise the effect of small perturbations and noises. In addition, given the insufficient data that may be faced in practical situations, a transfer learning approach is further proposed to improve the model generalisation. Training data is collected in an autonomous driving simulation platform and the effectiveness of the method is then verified based on this platform. Satisfactory performance is demonstrated for different driving scenarios and under varied road conditions.

Risks of Goods Transport Focused on the Assessment of Semi-Trailer Dynamics on Highways for Cargo Securing

The issue of the transport of goods is well-known, yet, in practice, there are often cases of damaged shipments due to improper storage and inappropriately chosen transport technology. Many cases are due to ignorance of the basic characteristics of the cargo and, consequently, its transport characteristics. Vehicle dynamics is crucial to the design of proper cargo securing; therefore, this article provides the values of longitudinal and lateral acceleration of a 16.5 m semi-trailer vehicle combination for test routes of length of 10,827 km on highways and other roads in Slovakia, Austria, and Germany from the monitoring of goods. The horizontal acceleration of 0.2 g is considered as the minimum stability of the load unit that should withstand transport. A load unit with a stability from 0.2 g to 0.3 g could be considered as the weakest load unit. The test results show that even the weakest load units such as these can be damaged in transports, as semi-trailer vehicle combinations still reach longitudinal ax1000 and lateral ay1000 accelerations between 0.2 g and 0.3 g relatively frequently. Acceleration events higher than 0.3 g occur very rarely, at 1.4 event/1000 km for roads, but only 0.1 event/1000 km for highways from our test transports. We have demonstrated through our research that it is necessary for the load units to have a minimum stability of 0.2 g. We can conclude that load units with a stability of less than 0.2 g are completely unacceptable for transport without additional securing because we obtained 70.3 acceleration events per 1000 km in the interval from 0.1 g to 0.2 g on highways but 1148.1 events per 1000 km on other roads. There is a big difference between the number of acceleration events per 1000 km on roads and highways for all acceleration intervals, which means that there is a substantially lower probability of damaging the weak load units on highways than on other roads.

Design and Manufacturing of EV Go-Kart

Abstract: Go-karts are small-size and small-weight vehicles that were developed for racing. These are made with materials that are strong and durable. It consists of many parts which include a chassis, engine, steering and braking system, and electronic controls. The chassis is the main part that is responsible for the stability of the vehicle. Chassis is made with the material having greater endurance and rigidity. It was developed in the 1950s in the USA and now getting popular. These are now used in amusement parks as a recreational activity. Many researchers have done work on go-karts and improve their design. This project was intended to design and fabricate a reliable and durable go-kart. Its primary objective to build a go-kart using local resources and applying different techniques to limit the cost of vehicles. These objectives were achieved by going through a detailed literature review and studying different techniques which can be implemented. A reliable design was chosen which can be implemented and can be completed in our period. Critically evaluate the design of the vehicle and then different parts were designed. This paper presents an approach to a go-kart chassis design, vehicle dynamics calculation, Li-ion battery capacity analysis, and electric motor choice for optimized vehicle performance. The chassis analysis shown in this paper was performed using a CAD/FEA software package, SolidWorks Student Edition. Three highlights can be found in this paper: An original design was implemented; the basic analysis was composed of chassis optimization using beam elements and modelling such an optimized chassis “locally” with solid elements for sub-modelling purposes. The most stressed tube joint was sub-modelled to calculate the risk of tube wall stability. Vehicle dynamics were calculated for the case of braking on a curved path and the case of a collision with the front tire due to road imperfection. The authors intend to install a data acquisition system in the future to analyse the stress of local chassis tubes.

Mixed Learning- and Model-Based Mass Estimation of Heavy Vehicles

This research utilized long short-term memory (LSTM) to oversee an RLS-based mass estimator based on longitudinal vehicle dynamics for heavy-duty vehicles (HDVs) instead of using the predefined rules. A multilayer LSTM network that analyzed parameters such as vehicle speed, longitudinal acceleration, engine torque, engine speed, and estimated mass from the RLS mass estimator was employed as the supervision method. The supervisory LSTM network was trained offline to recognize when the vehicle was operated so that the RLS estimator gave an estimate with the desired accuracy and the network was used as a reliability flag. High-fidelity simulation software was employed to collect data used to train and test the network. A threshold on the error percentage of the RLS mass estimator was used by the network to check the reliability of the algorithm. The preliminary findings indicate that the reliability of the RLS mass estimator could be predicted by using the LSTM network.

A Study on Power Transmission Control for Applying MR Fluid Multi-Plate Clutch to Automobile Power Distribution Device

The aim of this study is to design and manufacture a multi-plate clutch system that uses magnetorheological (MR) fluid control to allow for a variable power transmission ratio in power distribution systems. MR fluid is a smart material that enables presenting a solution to the shocks and power loss that occur due to mechanical problems in power distribution systems. As such, the longitudinal and lateral dynamic properties of 4WD (four-wheel drive) vehicles were examined and analyzed to develop an algorithm to control the front/rear power distribution according to the road surface state and driving conditions. To verify the algorithm, the CarSim vehicle dynamics simulation program was adopted to perform experiments to understand the vehicle’s dynamic performance improvements and turning stability via a HILS (Hardware in the Loop) system. In this study, an MR fluid, multi-plate clutch was used that combines a dry clutch and a wet clutch using the characteristics of the MR fluid. Such a clutch was designed to enable continuous and smooth torque transmission by utilizing the strengths of each of the dry and wet clutches. The CarSim vehicle dynamics program was used to conduct the experiments, which were conducted by linking to the manufactured MR fluid clutch experimental device. The experiments investigated the dynamic performance based on the power distribution ratio by performing longitudinal flat, inclined driving and lateral DLC (double lane change) driving. In summary, this study found that it is possible to perform power transmission by applying a current to an MR fluid and forming a magnetic field to change the flow properties of the fluid to control the torque transmission ratio that occurs in an MR fluid clutch.

Development of a New Vertical Dynamic Model of a Rail Vehicle for the Analysis of Ride Comfort

The rail vehicle industry wants to produce vehicles with higher speeds, to maintain and increase its market share. However, when the speed of the vehicle increases, it may have an undesirable effect on ride comfort, in terms of ride dynamics. Recent developments towards lighter and faster vehicles make the problem of ride comfort at higher speeds increasingly important. Focusing on the behavior of flexible rather than rigid body behavior should not be neglected when designing long and light car bodies. There are several approaches to incorporate body flexibility in multibody simulations and they have some superiorities and weaknesses. In this study, an efficient and accurate vertical dynamic model for the ride comfort analysis is developed and implemented in a commercial object-oriented modeling (OOM) software Dymola (2015 FD01) which uses the open-source code Modelica. This model includes car body flexibility with the assembling of a rigid body approach. The developed model is compared to a three-dimensional vehicle model in the commercial Vampire software (Pro V5.50) at different velocities. For the vertical ride comfort analysis, the ISO 2631-1 standard was used for both the developed model and the three-dimensional model. The results are presented as acceleration history and awrms—weighted r.m.s (root mean square) of accelerations—as required by the standard. The developed model has shown its feasibility in terms of its efficiency and accuracy for the vertical ride comfort analysis. The accuracy of the model is evidenced by the fact that the car body vibration level at high speeds shows minor differences compared to the results of the Vampire, which is a validated commercial software in the area of rail vehicle dynamics. The approach involving the assembly of rigid bodies is applied for the first time for high-speed trains in dynamical modelling, with flexible car bodies for ride comfort analysis. Furthermore, it can be used for parametrical studies focusing on ride comfort, thereby offering a quite beneficial framework for addressing the challenges of ride comfort analysis in high-speed rail vehicles. Improvements for and analyses of other aspects are also possible, since the optimization and other useful libraries are readily available in Dymola/Modelica.

Pitch motion suppression of electric vehicle active suspensions based on multibody dynamics

Active suspension control strategies are proposed to suppress pitch motion and address the issue of motion sickness among electric vehicle passengers. In this work, we investigate the performance of the linear quadratic regulator (LQR), variable universe fuzzy control, adaptive-network-based fuzzy inference system (ANFIS), and fuzzy PID control under continuous acceleration and deceleration and bumpy road conditions. First, a 14-degrees-of-freedom (DOF) dynamics model of an electric vehicle is developed based on a semirecursive multibody dynamics method, which was originally proposed by Javier García de Jalón. The vehicle dynamics simulation is performed to accurately model the vehicle states and responses. Second, the LQR, variable universe fuzzy control, ANFIS, and Fuzzy PID control algorithms are tailored via vehicle states, including pitch, rate, and acceleration. Their performances are investigated and compared in detail. Finally, the robustness of the proposed control strategies is further discussed based on various speed bump parameters and initial velocities. The results indicate that the proposed control strategies are capable of suppressing the pitch motion of electric vehicles, enhancing the riding comfort under diverse operating conditions, thereby reducing the likelihood of motion sickness among passengers of electric vehicles.

Advances in Vehicle Dynamics and Road Safety: Technologies, Simulations, and Applications

Alongside various road safety aspects, vehicle dynamics play a crucial role in enhancing the quality of life in modern society within a holistic traffic safety framework [...]

Design of a Multidimensional Intelligent Safety Driving Monitoring System Based on Machine Vision

This study introduces a multidimensional driving safety monitoring system that integrates millimeter-wave radar and an Arduino Uno-based alcohol sensor. The system aims to enhance driving safety and prevent accidents by real-time monitoring and analysis of the driving environment, driver's condition, and vehicle dynamics using machine vision technology. Utilizing millimeter-wave radar, the system precisely detects obstacles and pedestrians around the vehicle, facilitating efficient distance and speed measurements to support automatic emergency braking and collision warnings. Additionally, the Arduino Uno-based alcohol sensor monitors the driver's breath for alcohol concentration, ensuring the driver is in a suitable state for driving. Combined with machine vision technology, the system further analyzes video stream data from inside and outside the cockpit, monitoring driver behavior and external traffic conditions to enhance the identification of potential risks. The success of this research illustrates the potential of combining machine vision with advanced sensor technologies, offering new insights for the development of comprehensive driving assistance systems.