Catalogue Search | MBRL
Are you sure you want to remove the book from the shelf?
{{itemTitle}}
2,340
result(s) for
"Steering wheels"
Sort by:
Dynamic Measurement Method for Steering Wheel Angle of Autonomous Agricultural Vehicles
Steering wheel angle is an important and essential parameter of the navigation control of autonomous wheeled vehicles. At present, the combination of rotary angle sensors and four-link mechanisms is the main sensing approach for steering wheel angle with high measurement accuracy, which is widely adopted in autonomous agriculture vehicles. However, in a complex and challenging farmland environment, there are a series of prominent problems such as complicated installation and debugging, spattered mud blocking the parallel four-bar mechanism, breakage of the sensor wire during operation, and separate calibrations for different vehicles. To avoid the above problems, a novel dynamic measurement method for steering wheel angle is presented based on vehicle attitude information and a non-contact attitude sensor. First, the working principle of the proposed measurement method and the effect of zero position error on measurement accuracy and path tracking are analyzed. Then, an optimization algorithm for zero position error of steering wheel angle is proposed. The experimental platform is assembled based on a 2ZG-6DM rice transplanter by software design and hardware modification. Finally, comparative tests are conducted to demonstrate the effectiveness and priority of the proposed dynamic sensing method. Experimental results show that the average absolute error of the straight path is 0.057° and the corresponding standard deviation of the error is 0.483°. The average absolute error of the turning path is 0.686° and the standard deviation of the error is 0.931°. This implies the proposed dynamic sensing method can accurately realize the collection of the steering wheel angle. Compared to the traditional measurement method, the proposed dynamic sensing method greatly improves the measurement reliability of the steering wheel angle and avoids complicated installation and debugging of different vehicles. The separate calibrations for different vehicles are not needed since the proposed measurement method is not dependent on the kinematic models of the vehicles. Given that the attitude sensor can be installed at a higher position on the wheel, sensor damage from mud blocking and the sensor wire breaking is also avoided.
Journal Article
Driver fatigue and drowsiness monitoring system with embedded electrocardiogram sensor on steering wheel
Real time driver health condition monitoring system with drowsiness alertness was proposed. A new embedded electrocardiogram (ECG) sensor with electrically conductive fabric electrodes on the steering wheel of a car was designed to monitor the driver's health condition. The ECG signals were measured at a sampling rate of 100 Hz from the driver's palms as they stay on a pair of conductive fabric electrodes located on the steering wheel. Practical tests were conducted using an embedded ECG sensor with a wireless sensor node, and their performance was assessed under non-stop 2 h driving test. The ECG signals were measured and transmitted wirelessly to a base station connected to a server PC in personal area network environment. The driver's health condition such as the normal, fatigued and drowsy states was analysed by evaluating the heart rate variability in the time and frequency domains.
Journal Article
A Basic Study for Active Steering Wheel System for Steering Burden Evaluation by Driving Position Focus on Driver’s Arm Size
As automated driving has not yet been established, on narrow roads where there is no separation between pedestrians and vehicles, it is essential to switch to manual driving. However, when the driver turns the steering wheel from one hand to another on narrow roads, it causes steering burdens and operational errors if the steering feel or burden is not proper. Thus, this study aims to construct an active steering wheel system that provides an appropriate steering feel or burden by controlling the steering reaction torque, driving position and steering gear ratio for each driver. In this paper, we focused on and examined the driving position among these. A two-dimensional steering model that considers the size of the arms for each driver was established to evaluate steering burden. In addition, a basic study was conducted on the appropriate driving position. Then, based on the joint movements and angles calculation, the appropriate driving position that considers the size of the arms was studied by evaluating the joint power. As a result, it was found that if the steering wheel position is too close to the driver, the amount of joint movement increases, and if it is too far away, the joint movement decreases. Therefore, it was found that the appropriate steering wheel position for each driver’s arm length can be considered by using the joint power.
Journal Article
Applying machine learning and GA for process parameter optimization in car steering wheel manufacturing
The wrapping layer’s foaming process of the car steering wheel industry usually relies on the manual parameter setting by experienced engineers, but the reliability and validity are usually hard to control due to many foaming factors that affect the hardness of steering wheel wrapping layer (e.g., vulcanization time, mold temperature, material liquid temperature, material discharge pressure, humidity). This paper first proposes an intelligent process parameter recommendation system architecture, then develops a neural network-based hardness prediction (NNHP) model to predict the car steering wheel’s wrapping hardness. Consequently, we combine NNHP and genetic algorithm (GA) to develop a foaming process parameter recommendation model (NNHP-GA) to effectively recommend the most suitable combinations of process parameters for achieving the desired wrapping hardness in an IoT-based intelligent manufacturing environment. An empirical study is applied in Taiwan’s world-leading car steering wheel company. By analyzing the 16 important process sensor data installed in each casting machine, the five experimental results show that the NNHP-GA recommendation system can successfully predict the hardness of the steering wheel’s wrapping layer and recommend appropriate process parameters (e.g., sensors S1-vulcanization time, S2-mold temperature, S5-A.P. pressure, S6-B.I. pressure, and “pressurization time”) under a specific hardness target. The manufacturing company may employ the NNHP-GA model to build a process parameter recommendation system and increase the quality of process parameter setting.
Journal Article
Driving Assistance for Collision Avoidance by Simultaneous Intervention in Steering and Throttle Operations
This paper presents a novel shared control algorithm based on quasi- linear parameter varying model predictive control (qLPV-MPC) for the purpose of preventing vehicle collisions during lane-changing maneuvers and maintaining the vehicle’s position within the lane at all times. The algorithm optimizes the control solution by transforming it into a quadratic programming problem, thus enhancing computational efficiency. By sharing control over both vehicle acceleration and steering wheel torque, the algorithm provides coupled lateral and longitudinal dynamic control, allowing simultaneous acceleration/deceleration adjustments and steering torque application for effective collision avoidance. Notably, the algorithm operates independently of a reference path, allowing the driver to retain primary control over vehicle movement, while the controller intervenes only in high-risk situations. Simulation scenarios, including rear-end, side-impact, and multi-vehicle collision cases, are employed to validate the algorithm’s effectiveness in various collision avoidance contexts.
Journal Article
Development and Testing of a Methodology for the Assessment of Acceptability of LKA Systems
In recent years, driving simulators have been widely used by automotive manufacturers and researchers in human-in-the-loop experiments, because they can reduce time and prototyping costs, and provide unlimited parametrization, more safety, and higher repeatability. Simulators play an important role in studies about driver behavior in operating conditions or with unstable vehicles. The aim of the research is to study the effects that the force feedback (f.f.b.), provided to steering wheel by a lane-keeping-assist (LKA) system, has on a driver’s response in simulators. The steering’s force feedback system is tested by reproducing the conditions of criticality of the LKA system in order to minimize the distance required to recover the driving stability as a function of set f.f.b. intensity and speed. The results, obtained in three specific criticality conditions, show that the behaviour of the LKA system, reproduced in the simulator, is not immediately understood by the driver and, sometimes, it is in opposition with the interventions performed by the driver to ensure driving safety. The results also compare the performance of the subjects, either overall and classified into subgroups, with reference to the perception of the LKA system, evaluated by means of a questionnaire. The proposed experimental methodology is to be regarded as a contribution for the integration of acceptance tests in the evaluation of automation systems.
Journal Article
Trajectory Tracking and Driving Torque Distribution Strategy for Four-Steering-Wheel Heavy-Duty Automated Guided Vehicles
A four-steering-wheel heavy-duty Automated Guided Vehicle (AGV) is prone to lateral instability and wheel slippage during acceleration, climbing, and small-radius turns. To address this issue, a trajectory tracking strategy considering lateral stability and an optimal driving torque distribution strategy considering load transfer and tire adhesion coefficient are proposed. Firstly, a three-degree-of-freedom AGV trajectory tracking model is established, tracking error and sideslip angle are incorporated into the cost function, and an improved model predictive trajectory tracking controller is proposed. Secondly, the longitudinal and yaw dynamic model of AGV is established, and vertical load transfer is analyzed. With the goal of minimizing tire adhesion utilization rate, quadratic programming is used for the optimal distribution of driving torque. Finally, through co-simulation using ADAMS and MATLAB on a narrow “climbing straight+ S-curve” road, the maximum tracking error is 0.0443 m. Compared to the unimproved model predictive control and average driving torque distribution strategy, the sideslip angle is reduced by 58.18%, the maximum tire adhesion utilization rate is reduced by 6.62%, and climbing gradeability on wet roads is enhanced.
Journal Article
Driver–Steering Wheel Interaction during Cornering
This research aims at understanding how the driver interacts with the steering
wheel, in order to detect driving strategies. Such driving strategies will allow
in the future to derive accurate holistic driver models for enhancing both
safety and comfort of vehicles.
The use of an original instrumented steering wheel (ISW) allows to measure at
each hand, three forces, three moments, and the grip force. Experiments have
been performed with 10 nonprofessional drivers in a high-end dynamic driving
simulator. Three aspects of driving strategy were analyzed, namely the
amplitudes of the forces and moments applied to the steering wheel, the
correlations among the different signals of forces and moments, and the order of
activation of the forces and moments.
The results obtained on a road test have been compared with the ones coming from
a driving simulator, with satisfactory results.
Two different strategies for actuating the steering wheel have been identified.
In the first strategy, the torque is provided mostly by just one single arm and
hand. In the second strategy, the torque is created by both of the two arms and
hands, which apply forces and moments in opposite directions. Future holistic
driver models able to describe the forces acting at whole body may benefit from
the outcomes of this research.
Journal Article
Detailed Effects of Road Conditions and Lateral Maneuvers on Dynamic Stability of Four‐Wheel‐Steering Vehicles
This study explores the detailed effects of speed, road adhesion, road slope, and dynamic maneuvers on the lateral stability of vehicles equipped with four‐wheel steering (4WS) systems, also referred to as all‐wheel steering (AWS) system. The research uses CarSim simulation software to comprehensively evaluate vehicle performance under various speeds (30–90 km/h), road slope (10°–30°) and adhesion coefficients (0.1–0.85) during lane changes and turning maneuvers. The results obtained reveal that 4WS significantly enhances stability and handling at moderate speeds and favorable adhesion conditions. However, high‐speed operations, particularly on low adhesion surfaces or with a steep road slope, increase instability and safety risks. Practical implications also emphasize the importance of maintaining safe speeds, particularly under adverse road conditions, and decreasing speed during sharp turns to counteract centrifugal forces. Finally, these findings highlight the substantial role of cautious driving and adherence to speed limits in improving overall safety for 4WS vehicles. This study thoroughly analyzes and evaluates the detailed effects of speed and road conditions on the directional stability of four‐wheel steering (4WS) vehicles under various driving scenarios in CarSim simulations. The findings highlight the 4WS advantages under moderate conditions and stress cautious driving on slippery or inclined roads for safety.
Journal Article