Driver's Wheel Research Articles

Driver Drowsiness Monitoring System using Machine Learning

Abstract: In today's world, sleepiness is one of the main causes of road accidents, many of which have tragic outcomes. Statistics show that the majority of traffic collisions, which frequently result in fatalities and serious injuries, are caused by sleepy driving. As a result, various studies have been done to develop software that can recognize driver tiredness and alert them before making a major error. Using methods from the automobile industry, several of the more popular ways to design their own systems. However, other factors, such as vehicle type, road design, and the capacity to operate the driver's wheel, significantly impacted these traditional criteria. In order to monitor a driver's drowsiness, certain techniques use psychological methods, which frequently produce the most accurate and dependable results. These methods are expensive, though, because electrodes must be applied to the head and torso. This paper describes a machine learning approach to sleepiness detection. The areas of the driver's eyes are located using face detection, and these regions serve as templates for eye tracking in subsequent frames. Finally, drowsiness detection is performed on the tracked eye images to produce alarm warnings. The three steps of this method are Face detection, Eye detection, and Drowsiness detection. Image processing is used to identify the driver's face, and after that, the image of the driver's eyes is extracted to look for indicators of sleepiness. The HAAR face detection algorithm uses image frames that have been captured as input before producing the detected face. To generate results, the model is provided with a sizable database of closed and open eyes. Every time the driver is observed to be sleepy, Buzz alerts the driver. The suggested method for real-time driver drowsiness detection is a practical and affordable approach.

Measuring Method of Slip Ratio for Tractor Driving Wheels Based on Machine Vision

Tractors are prone to large slips when they are in field operation. The degree of slip plays a vital role in traction efficiency and fuel efficiency. This paper presents a method for measuring the slip ratio of tractors in field operation based on machine vision. The accurate measurement of slip ratio needs to obtain actual velocity and theoretical velocity separately. For obtaining the actual velocity, a monocular camera mounted on the tractor vertically faces down at the ground to collect images. Then, the feature points of inter-frame ground images are matched by the ORB (Oriented FAST and Rotated BRIEF) algorithm for calculating the translational displacement. Next, a homography matrix based on camera calibration is proposed to complete the transformation of a point from the pixel coordinate system to the world coordinate system. Aiming to acquire the theoretical velocity, a method that takes the variations in tire radius into account is proposed, and the tire radii of the driving wheels are indirectly determined by the tire inflation pressure in real-time. The proposed measurement method was verified with an experimental tractor. The results show that the mean absolute errors of the tractor driving wheels’ slip ratio measured by the machine vision method are less than 0.75%, and the maximum of the absolute errors is not more than 2.22%, which shows good performance.

Frequency Spectra Analysis of Drawbar Pulls Generated by Special Driving Wheels Improving Tractive Performance.

Driving wheel operation is characterized by force interactions with the ground, manifested in the form of vibrations. Signals generated by driving wheels can be analyzed in the frequency spectrum of tractor drawbar pull. The paper presents the analysis of a drawbar pull signal generated by a tractor equipped with two types of special driving wheels and standard tires. Beside the evaluation of special driving wheels’ properties according to drawbar power, the frequency spectra of measured signals were analyzed using a fast Fourier transformation. The model spectrum intervals for the standard tires, spike tires, and blade wheels were calculated according to the number of rubber lugs, blades, or spikes and compared with the experimental results. The results showed that the specific frequencies typical for blades and spikes were identified in model spectrum intervals. In the case of standard tires, the spectrum components typical for rubber lugs of the tire tread pattern were not identified. The highest amplitude of the typical frequency component was detected in the case of blades wheels, which showed the highest difference in drawbar power in comparison with the standard tires. Smaller dimensions of spikes resulted in lower amplitude and lower difference in drawbar power in comparison with the standard tires.

A method for fatigue detection based on Driver's steering wheel grip

We propose a simple method to measure a driver's fatigue state by detecting the driver's grip force on the steering wheel while driving. We tested the grip force of 36 drivers on the steering wheel in conscious states (Alert) and fatigue states under actual road driving conditions. Using the Stanford sleepiness scale (SSS), we divided drivers into Alert Group A, fatigue Group A, and fatigue Group B. During 20-min real-road driving trials, we measured the steering wheel grip force, electroencephalogram index (R = (α + θ)/β), and blink frequency of each driver synchronously. We found that ΔF, the difference between the maximum/minimum grip force and the standard deviation of the grip force, σF, for each driver, strongly correlated with the driver's fatigue state. In the fatigue state, both ΔF and σF increased significantly. We examined these force indices using analysis of variance (ANOVA) and validated them against the R-value, blink frequency, and the driver's self-reported fatigue state. Using the grip force in fatigue detection, our method can achieve an overall recognition rate of 86.6% and an individual recognition rate of 88.3%. These results indicate that this method can effectively detect a driver's fatigue state during actual road driving. This new method has several advantages, such as a high signal-to-noise ratio, simple data collection, and no influence on daily driving. Thus, our proposed method may provide a theoretical foundation for the development of fatigue-detecting steering wheels

Roll Stability and Path Tracking Control Strategy Considering Driver in the Loop

A study pointed out that the delay time of the driver's nervous system has a significant effect on the roll stability of the vehicle. However, the existing researches on vehicle rollover prevention control rarely consider the influence of driver factors on vehicle roll stability. Aiming at this problem, a vehicle roll stability and path tracking control strategy considering driver in the loop is proposed to assist different types of drivers. It includes the supervisory decision layer and execution layer. The supervisory decision layer selects the corresponding control mode according to the driver's steering wheel angle change rate, path tracking deviation and vehicle roll stability information. The execution layer includes three modes: human-machine shared steering, active braking and integrated chassis control. The human-machine shared steering and active braking modes assist the driver to improve the roll stability and path tracking accuracy. The integrated chassis control mode is used for the automatic driving of vehicles under emergency conditions. Simulation results show that the proposed control strategy can effectively improve the vehicle roll stability and path tracking accuracy, and reduce the driver's operating burden.

On the Motion of a Three-Wheel Robot with Slip of Driver Wheels

On the Motion of a Three-Wheel Robot with Slip of Driver Wheels

A Swinging and Self-Actuating Friction Drive Device Used in Large-Scale Rotary Devices

Background: Large-scale rotary devices often employ friction drives in order to be manufactured easily and usually select a large driving radius to reduce the friction forces needed. In traditional devices, the driver and driven friction wheels are required to be of higher roundness to avoid slipping between them. This makes the driven friction wheel difficult to be machined. In addition, generating adequate contact load between contacting surfaces is essential for friction drives, and the best way to achieve it is self-actuating loading. Objective: The purpose of this paper is to propose and analyze a patent about a new friction drive device, in which the driver friction wheel can roll along with the driven friction wheel throughout, and the contact load between the driver and driven friction wheels can be generated on the torque demand. Methods: By using two swinging gearboxes, the two driver friction wheels are both swinging, and thus both can roll along with the driven friction wheel floatingly. Therefore, the driven wheel can have some deviation in roundness. Besides, this design offers a condition to construct a torque-actuated loading mechanism. Driven by the input torque, each driver wheel turns and exerts a friction force on the driven wheel, and then the driven wheel applies a reactive force to each driver wheel. This reactive force tends to pull the driver wheel to the driven wheel, producing a wedging action. Thus, an appreciable amount of contact load between the driver and driven wheels will be generated. Results: The results show the contact load between the driver and driven friction wheels is directly proportional to the resistant torque acting on the friction drive device. The results also show that a “frictional locking” condition for the device to avoid slipping between the driver and driven friction wheels is needed and it depends on the geometric parameters of the device. Conclusion: A swinging and self-actuating friction drive device is developed by using two swinging gearboxes. Design principles were described and a design example for this friction drive device was demonstrated. This kind of friction drive device not only offers an alternative way to drive large-scale rotary devices, but also develops a new method to realize self-actuating loading for friction drives.

Structural and Kinematic Analysis of Gear and Lever Differential Mechanisms by Symmetric Movement of Rotation Centers for Driving and Slave Gear Wheels

Structural and Kinematic Analysis of Gear and Lever Differential Mechanisms by Symmetric Movement of Rotation Centers for Driving and Slave Gear Wheels

The analysis of drivers' reaction time using cell phone in the case of vehicle stabilization task.

The article analyzes the driver's psychophysiological qualities such as complex reaction and individual ability to control the vehicle that has suddenly lost its stability. The comparative analysis of the duration of reaction time was performed to assess the negative influence of speaking on a phone and driving with one hand. The experimental research was carried out on special training grounds with the road surface having low adhesion coefficient, where sudden lateral vehicle destabilization was caused by the moving plate mounted on the road surface. The vehicle onboard equipment was used for identifying the difference between the destabilization moment and the responsive driver's steering wheel movement which in this research was assumed as the reaction time. Statistical methods of research applied for the analysis of results showed high probability that the driver's actions would be significantly late in controlling a vehicle. When stabilizing a vehicle movement, the complex reaction time of a vehicle driver speaking on a mobile phone is increased by 18.1% as compared with the conventional driving by a driver not speaking on a phone. The risk of using the phone depends on the driver characteristics, traits and attitudes that affect the level of their experienced dangers, and the intensity of using mobile phones and driving. Speaking on a phone while driving increases the driver's reaction time and mental workload, and changes his or her visual overview ability as well as understanding of the situation. Int J Occup Med Environ Health 2018;31(5):633-648.

A feedforward and feedback integrated lateral and longitudinal driver model for personalized advanced driver assistance systems

Advanced driver assistance systems (ADAS) are a subject of increasing interest as they are being implemented on production vehicles and also continue to be developed and researched. These systems need to work cooperatively with human drivers to increase vehicle driving safety and performance. Such cooperation requires the ADAS to work with the specific driver with some knowledge of the human driver's driving behavior. To aid such cooperation between human drivers and ADAS, driver models are necessary to replicate and predict human driving behaviors and distinguish among different drivers. This paper presents a combined lateral and longitudinal driver model developed based on human subject driving simulator experiments that is able to identify different driver behaviors through driver model parameter identification. The lateral driver model consists of a compensatory transfer function and an anticipatory component and is integrated with the design of the individual driver's desired path. The longitudinal driver model works with the lateral driver model by using the same desired path parameters to model the driver's velocity control based on the relative velocity and relative distance to the preceding vehicle. A feedforward component is added to the feedback longitudinal driver model by considering the driver's ability to regulate his/her velocity based on the curvature of his/her desired path. This interconnection between the longitudinal and lateral driver models allows for fewer driver model parameters and an increased modeling accuracy. It has been shown that the proposed driver model can replicate individual driver's steering wheel angle and velocity for a variety of highway maneuvers.

A Personalizable Driver Steering Model Capable of Predicting Driver Behaviors in Vehicle Collision Avoidance Maneuvers

In recent years, significant emphases and efforts have been placed on developing and implementing advanced driver assistance systems (ADAS). These systems need to work with human drivers to increase vehicle occupant safety, control, and performance in both ordinary and emergency driving situations. To aid such cooperation between human drivers and ADAS, driver models are necessary to replicate and predict human driving behaviors and distinguish among different drivers. This paper presents a combined driver model that is able to not only identify different individual driver behaviors, but also predict a driver's behavior in rare vehicle maneuvers such as collision avoidance (CA) based on his/her daily driving data. The driver model consists of a compensatory transfer function and an anticipatory component and is integrated with the design of the individual driver's desired path. It has been shown that the proposed driver model can replicate each driver's steering wheel angle signal for a variety of highway and in-city maneuvers. The utility of the proposed driver model is its ability to predict a driver's steering wheel angle signal for a CA maneuver from only daily nonemergency driving data. The driver model is then validated by comparing two different drivers’ model parameter sets to the group average to show that each driver has a unique set of parameters. Finally, the driver model is validated by showing that its daily driving parameters differ from its predicted CA parameters.

What She Fears More Than Death.

What She Fears More Than Death.

Evaluation of Rutting Performance of Asphalt Mixture with Driving Wheel Pavement Analyzer

Rutting is common pavement distress, which leads to lower riding comfort for road users and high maintenance costs. One of the commonly used tests is the simulation test with wheel tracking devices. Here, a new rutting test system has been developed based on the “Driving Wheel Pavement Analyzer” (DWPA) to evaluate the rutting performance of asphalt mixtures. This study conducted three types of rutting tests to validate feasibility, reliability, and accuracy of DWPA test. The results indicated that the DWPA test provided more information on ruts and enabled us to distinguish the performance of materials. The CDWPA index is better suited to reflect the rutting resistance of the material, which is highly correlated to the APA rutting index and the rutting test index of China according to the grey relational analysis results.

Fabrication of Hybrid Electro Mechanical Tricycle with Regenerative Braking System

Hybrid EMT is a Human controlled Vehicle that uses both human vitality and electric vitality to drive the vehicle. The key destinations behind this examination is to produce a fitting strategy for transportation which would utilize human imperativeness in a viable way to deal with be used for driving the vehicle, for example, it runs quicker than the present day human controlled vehicles. Consequently the human quality like quality and deficiency (shortcoming) Hybrid EMT shifts from most other choice imperativeness vehicles, is the rider gives pedal power, which power is extended estimated 1.8 times terms in Gear proportion by the EMT drive course of action. In the event that a man is feeling exertive by pedal exertion so electric drive is accessible at the driver wheel, DC motor is connected at the back side, which run a vehicle by method for battery with economic speed rate.

Effects of Tyre Pressure on Vehicle Suspension Performance

Incorrect inflation pressures in tyres affects the vehicle handling, passenger comfort and braking conditions in addition to causing a reduction in fuel efficiency and tyre life. To address this problem, mathematical models have been produced and an experimental validation has been carried out. The models were developed with 7-DOF, for a full car system, using MATLAB programs. In the simulation study, the suspension faults have been considered by running the models with a range of inflation pressures at four conditions i.e. at standard pressure (2.3bar) and 1.5bar on the passenger wheel, driver wheel and front wheels. In each case, an analysis was carried out on the performances of the suspension in terms of ride comfort, road handling and stability of the vehicle followed by the presentation of the results obtained. In addition, the influence of parameter variations on transfer functions as a fault detection of suspension has been introduced. This approach has been used when detecting faults of vehicle tyres being under-inflated 35% and also to detect other suspension faults in the future.

Effects of Tyre Pressure on Vehicle Suspension Performance

Incorrect inflation pressures in tyres affects the vehicle handling, passenger comfort and braking conditions in addition to causing a reduction in fuel efficiency and tyre life. To address this problem, mathematical models have been produced and an experimental validation has been carried out. The models were developed with 7-DOF, for a full car system, using MATLAB programs. In the simulation study, the suspension faults have been considered by running the models with a range of inflation pressures at four conditions i.e. at standard pressure (2.3bar) and 1.5bar on the passenger wheel, driver wheel and front wheels. In each case, an analysis was carried out on the performances of the suspension in terms of ride comfort, road handling and stability of the vehicle followed by the presentation of the results obtained. In addition, the influence of parameter variations on transfer functions as a fault detection of suspension has been introduced. This approach has been used when detecting faults of vehicle tyres being under-inflated 35% and also to detect other suspension faults in the future.

Effects of Tyre Pressure on Vehicle Suspension Performance

Incorrect inflation pressures in tyres affects the vehicle handling, passenger comfort and braking conditions in addition to causing a reduction in fuel efficiency and tyre life. To address this problem, mathematical models have been produced and an experimental validation has been carried out. The models were developed with 7-DOF, for a full car system, using MATLAB programs. In the simulation study, the suspension faults have been considered by running the models with a range of inflation pressures at four conditions i.e. at standard pressure (2.3bar) and 1.5bar on the passenger wheel, driver wheel and front wheels. In each case, an analysis was carried out on the performances of the suspension in terms of ride comfort, road handling and stability of the vehicle followed by the presentation of the results obtained. In addition, the influence of parameter variations on transfer functions as a fault detection of suspension has been introduced. This approach has been used when detecting faults of vehicle tyres being under-inflated 35% and also to detect other suspension faults in the future.

Event-related alpha perturbations related to the scaling of steering wheel corrections

Previously we derived a new measure relating the driver's steering wheel responses to the vehicle's heading error velocity. This measure, the relative steering wheel compensation (RSWC), changes at times coincident with an alerting stimulus, possibly representing shifts in control strategy as measured by a change in the gain between visual input and motor output. In the present study, we sought to further validate this novel measure by determining the relationship between the RSWC and electroencephalogram (EEG) activity in brain regions associated with sensorimotor transformation processes. These areas have been shown to exhibit event-related spectral perturbation (ERSP) in the alpha frequency band that occurs with the onset of corrective steering wheel maneuvers in response to vehicle perturbations. We hypothesized that these regions would show differential alpha activity depending on whether the RSWC was high or low, reflecting changes in gain between visual input and motor output. Interestingly, we find that low RSWC is associated with significantly less peak desynchronization than larger RSWC. In addition we demonstrate that these differences are not attributable to the amount the steering wheel is turned nor the heading error velocity independently. Collectively these results suggest that neural activity in these sensorimotor regions scales with alertness and may represent differential utilization of multisensory information to control the steering wheel.

Anti-lock Driving Wheel Control for Electric Trains

Anti-lock Driving Wheel Control for Electric Trains

Interaction Mechanics Model for Rigid Driving Wheels of Planetary Rovers Moving on Sandy Terrain with Consideration of Multiple Physical Effects

Predicting wheel‐terrain interaction with semiempirical models is of substantial importance for developing planetary wheeled mobile robots (rovers). Primarily geared toward the design of manned terrestrial vehicles, conventional terramechanics models do not provide the sufficient fidelity required for application on autonomous planetary rovers. To develop a high‐fidelity interaction mechanics model, in this study the physical effects of wheel lug, slip sinkage, wheel dimension, and load are analyzed based on experimental results, including wheel sinkage, drawbar pull, normal force, and moment, which are measured on a single‐wheel test bed. The mechanism of lug‐terrain interaction is investigated systematically to clarify the principle of increasing shear stress, conditions of forming successive shearing among adjacent lugs, and the influence on shear displacement of soil. A mathematical model for predicting the concentrated forces and torque of rigid wheels with lugs for planetary rovers moving on sandy terrain is derived by integrating the improved models of normal and shearing stress distributions. In addition to the wheel parameters, terrain parameters, and motion state variables, wheel‐terrain interaction parameters, such as the linear varying sinkage exponent, the soil displacement radius, and load effect parameters, were proposed and explicitly included in the model. In the single‐wheel experiments, the slip ratio was increased approximately from 0.05 to 0.6, and the relative errors of the predicted results using the proposed model are less than 10% for all the wheels when compared with the experimental data. The proposed model has been used in the simulation of a four‐wheeled rover, and its effectiveness is evaluated by comparing the simulation results with experimental results.