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Electric vehicle (EV) steering control becomes much more important in many senses such as driving comfort and safety. Unlike the traditional internal combustion engine powered vehicles that possess only one control input related to the steering wheel, the so‐called direct yaw moment control is yet another control input in EVs thanks to the dispersedly located in‐wheel motors. In view of controller design crucially depending on a proper mathematical model, this paper first focuses on the modeling of EV steering system with uncertainty in the system considered for the latter robust controller design. Then a Takagi‐Sugeno (T‐S) fuzzy model is obtained based on the EV's model, which is capable of varying vehicle velocity in a range. Finally, a robust controller with inputs constraint is designed based on the model, and the effectiveness is verified by computer simulations. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

This case study investigated the visual-motor coordination of an expert and a novice driver during high-speed cornering on an ice-racing track. The frozen track imposes unique task constraints on drivers due to the loss of traction. We Examined the coordination structure employed by the drivers to stabilize task goals in such an uncertain environment. The eye tracker system collected data on eye movements, head rotation, and steering wheel rotation. Coherent patterns of eye-head-steering were evaluated through Mutual Information with a time delay. The results showed that both drivers were forced to deal with unstable, shaky steering under the task constraints of losing traction. The novice driver employed a control strategy that suppressed both head and steering wheel movements. The expert driver exhibited rhythmic head rotation, which may have a functional role in dealing with disturbances by generating stable movement patterns. This study highlights human flexibility and adaptability in response to challenging and uncertain environments. The perception-action system of the expert driver actively generated movements, forming a functional and complementary coordination. [ABSTRACT FROM AUTHOR]

This paper proposes a robust lateral control scheme for the path tracking of autonomous vehicles. Considering the discrepancies between the model parameters and the actual values of the vehicle and the fluctuation of parameters during driving, the norm-bounded uncertainty is utilized to deal with the uncertainty of model parameters. Because some state variables in the model are difficult to measure, an H ∞ observer is designed to estimate state variables and provide accurate state information to improve the robustness of path tracking. An H ∞ state feedback controller is proposed to suppress system nonlinearity and uncertainty and produce the desired steering wheel angle to solve the path tracking problem. A feedforward control is designed to deal with road curvature and further reduce tracking errors. In summary, a path tracking method with H ∞ performance is established based on the linear matrix inequality (LMI) technique, and the gains in observer and controller can be obtained directly. The hardware-in-the-loop (HIL) test is built to validate the real-time processing performance of the proposed method to ensure excellent practical application potential, and the effectiveness of the proposed control method is validated through the utilization of urban road and highway scenes. The experimental results indicate that the suggested control approach can track the desired trajectory more precisely compared with the model predictive control (MPC) method and make tracking errors within a small range in both urban and highway scenarios. [ABSTRACT FROM AUTHOR]

This work presents a theoretical analysis of the helical phase steering via a four-wave mixing (FWM) process in a closely cycled double-ladder atomic system. Our focus is to understand the contribution of the two-photon resonant process governed by a probe field and a driving laser in the control of the helical phase. The theoretical analysis allows us to understand the helical phase can be effectively steered when the two-photon resonant condition Δ = − δ is satisfied. Moreover, we investigate the superposition modes of the FWM field and a same-frequency Gaussian beam, which testify the two-photon resonant condition Δ = − δ plays an important role in the steering of the helical phase. [ABSTRACT FROM AUTHOR]

The novel laser ranging interferometer (LRI) on GRACE Follow-On (GRACE-FO) provides range and range rate measurements for more than 4 years now. Since the launch of the GRACE-FO mission there were few investigations about this measurement system on the level of gravity field recovery and analysis of post-fit residuals. We applied techniques such as along-orbit-analysis or time-argument of latitude diagrams (TAL) to analyse the post-fit range rate residuals as well as the post-fit range acceleration residuals to identify unknown characteristics and systematic effects. The effects are the range rate effect, the panel effect, the Carrier-To-Noise Ratio (CNR) effect and the polar effect. The range rate effect occurs, when the range rate observation ρ ̇ of the LRI is around 0 m/s. In the TAL diagram the effect appears as a mesh pattern. The panel effect shows patterns of increased residuals when a specific satellite panel starts or stops being illuminated by the Sun. Increased residuals appear when the laser beam is aligned with the Sun. A CNR drop as well as a fluctuation of the yaw and pitch pointing angles obtained from the LRI steering mirrors is observable. Additionally, when the satellites flying into or out of the Earth shadow this effect coincides with the shadow transition effect. Another effect is the CNR effect which appears as an elliptic shape in the TAL diagram. This pattern occurs when the CNR values drop below the LRI requirements of 70 dB-Hz. All these effects have not been detected in the residuals of the GRACE-FO K-band ranging system (KBR) and therefore further investigation and studies will improve the understanding and applications of LRI technology. [ABSTRACT FROM AUTHOR]

The steer-by-wire (SbW) system is a promising system in the realm of automotive engineering. It substitutes the mechanical connection between the steering wheel and the front road wheels with an electronic signal-based functional connection. The SbW system offers several advantages over conventional steering systems, including weight reduction, reduced vibration, and enhanced steering functionality configuration. However, the absence of a mechanical linkage in the SbW system gives rise to certain challenges. The SbW system requires endowing adequate steering feel such as damping and reaction force using feedback motor, and the road wheel needs robust control of pinion motor for normal load variation by passengers and self-aligning torque as external disturbance. The SbW system is composed of the steering feedback module (SFM) and the road wheel module (RWM). This paper proposes a control approach to generate steering feel for SFM, in which steering feel is generated using an admittance model based on velocity control. A disturbance observer is applied to ensure robustness of velocity control. The steering wheel torque versus steering wheel angle (T–A) curve is used to analyze steering feel characteristic and evaluate steering feel. The proposed steering system is validated through experiments that confirm its ability to provide satisfactory steering feel for vehicles. This work may offer a novel solution for the design of advanced steering systems in the field for the future mobility such as an autonomous driving. [ABSTRACT FROM AUTHOR]

Highly automated vehicles are subject to high expectations, encompassing safety improvements, efficiency in traffic flow and overall higher comfort. However, it is still unclear to what extent automation will be able to meet these expectations. Failures in sensors or erroneous data processing might bring an automated drive to a sudden stop. In such situations, the user of the vehicle could cooperate with the automated vehicle, to bridge system limits and directly continue the drive. How this cooperation process should be implemented and how much the vehicle user wants to be involved in this process is examined. In this paper, we investigate four different cooperation strategies, ranging from low involvement of the user in the driving task (pressing a button to continue the drive) to high involvement (performing the entire driving task with steering wheel and pedals). Participants experienced these strategies in a driving simulator, in which they encountered five inner city traffic scenarios where the automation reached its limits. The cooperation strategies were evaluated along the dimensions comfort, discomfort, mental demand, usability, perceived safety, trust in automation, personal benefit, and personal preference. As a main result, strategies with less involvement show increases in comfort and personal benefit as well as decreases in discomfort and mental demand. Further, perceived safety was rated highly for all strategies. In traffic scenarios perceived as more unsafe, strategies with higher involvement are preferred. This paper offers insights into user involvement in cooperative human machine interfaces, potentially enhancing the application of automated driving in urban traffic. [ABSTRACT FROM AUTHOR]

In this study, the optimization problem for controlling the pivot steering function of tracked vehicles is addressed. Firstly, kinematic modeling of the pivot steering process of tracked vehicles is conducted. Secondly, the control system of tracked vehicles is decoupled, and PID control algorithms for vehicle speed and yaw rate are separately designed. Furthermore, the parameters of the PID controllers are optimized using the Grey Wolf Optimizer algorithm. Finally, by constructing a joint simulation model using Matlab/Simulink + RecurDyn (V9R4), the simulation results indicate that the above control algorithm can effectively improve the tracking speed of tracked vehicles on vehicle speed and yaw rate under the pivot steering condition, quickly respond to the driver's driving intention, and ensure the stability of the pivot steering process, providing an effective basis for further research on the pivot steering function of tracked vehicles. [ABSTRACT FROM AUTHOR]

To further enhance the precision and the adaptability of path tracking control, and considering that most of the research is focused on front-wheel steering, an adaptive parametric model predictive control (MPC) was proposed for rear-wheel-steering agricultural machinery. Firstly, the kinematic and dynamic models of rear-wheel-steering agricultural machinery were established. Secondly, the influence laws of curvature and velocity on the prediction horizon Np, control horizon Nc, and preview value Npre were obtained by simulating and analyzing the factors influencing the MPC tracking effect. The results revealed that raising Npre can improve curve tracking performance. Np was correlated negatively with the curvature change, whereas Nc and Npre were positively connected. Np, Nc, and Npre were correlated positively with the velocity change. Then, the parameters for self-adaptation of Np, Nc, and Npre were accomplished via fuzzy control (FC), and particle swarm optimization (PSO) was utilized to optimize the three parameters to determine the optimal parameter combination. Finally, simulation and comparative analysis were conducted to assess the tracking effects of the manual tuning MPC, the FC_MPC, and the PSO_MPC under U-shaped and complex curve paths. The results indicated that there was no significant difference and all three methods achieved better tracking effects under no disturbance, with the mean absolute value of lateral error ≤0.18 cm, standard deviation ≤0.37 cm, maximum deviation of U-shaped path <2.38 cm, and maximum deviation of complex curve path <3.15 cm. The mean absolute value of heading error was ≤0.0096 rad, the standard deviation was ≤0.0091 rad, and the maximum deviation was <0.0325 rad, indicating that manual tuning can find optimal parameters, but with high uncertainty and low efficiency. However, FC_MPC and PSO_MPC have better adaptability and tracking performance compared to the manual tuning MPC with fixed horizons under variable-speed disturbance and are more able to meet the actual needs of agricultural machinery operations. [ABSTRACT FROM AUTHOR]

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Recognizing hand-object interactions presents a significant challenge in computer vision. It arises due to the varying nature of hand-object interactions. Moreover, estimating the 3D position of a hand from a single frame can be problematic, especially when the hand obstructs the view of the object from the observer’s perspective. In this article, we present a novel approach to recognizing objects and facilitating virtual interactions, using a steering wheel as an illustrative example. We propose a real-time solution for identifying hand-object interactions in eXtended reality (XR) environments. Our approach relies on data captured by a single RGB camera during a manipulation scenario involving a steering wheel. Our model pipeline consists of three key components: (a) a hand landmark detector based on the MediaPipe cross-platform hand tracking solution; (b) a three-spoke steering wheel model tracker implemented using the faster region-based convolutional neural network (Faster R-CNN) architecture; and (c) a gesture recognition module designed to analyze interactions between the hand and the steering wheel. This approach not only offers a realistic experience of interacting with steering-based mechanisms but also contributes to reducing emissions in the real-world environment. Our experimental results demonstrate the natural interaction between physical objects in virtual environments, showcasing precision and stability in our system. [ABSTRACT FROM AUTHOR]

Summary: In the face of external disturbances, the autonomous vehicle with a high center of gravity is exposed to the risk of roll instability during trajectory tracking. To this end, this paper aims to propose a vector‐extended‐state‐observer (VESO)‐based adaptive fixed‐time terminal sliding mode prescribed performance control strategy, which coordinates the front wheel steering angle and external roll moment to ensure the roll stability of the high center of gravity autonomous vehicle while improving the tracking dynamic and steady state performances. The VESO for estimating the external disturbance is designed to enhance the robustness outside the sliding mode surface, based on which the adaptive updating laws compensate the estimation errors to provide the control system's dynamic performance. A fixed‐time sliding mode control with prescribed performance is proposed, which combines a fixed‐time prescribed performance control method to further improve the transient performance and steady‐state accuracy. The double lane‐changing reference trajectory tracking conditions under different vehicle driving speeds and initial errors settings are verified by simulation in MATLAB/Simulink environment, and the results show that the proposed control strategy can ensure the rolling stability of the vehicle, can track the reference trajectory accurately and quickly, and has strong robustness. [ABSTRACT FROM AUTHOR]

This research presents an experimental electric vehicle developed at the Tecnológico Nacional de México Celaya campus. It was decided to use a golf cart-type gasoline vehicle as a starting point. Initially, the body was removed, and the vehicle was electrified, meaning its engine was replaced with an electric one. Subsequently, sensors used to measure the vehicle states were placed, calibrated, and instrumented. Additionally, a mathematical model was developed along with a strategy for the parametric identification of this model. A communication scheme was implemented consisting of four slave devices responsible for controlling the accelerator, brake, steering wheel, and measuring the sensors related to odometry. The master device is responsible for communicating with the slaves, displaying information on a screen, creating a log, and implementing trajectory tracking techniques based on classical, geometric, and predictive control. Finally, the performance of the control algorithms implemented on the experimental prototype was compared in terms of tracking error and control input across three different types of trajectories: lane change, right-angle curve, and U-turn. [ABSTRACT FROM AUTHOR]

Autonomous reaction network exploration algorithms offer a systematic approach to explore mechanisms of complex chemical processes. However, the resulting reaction networks are so vast that an exploration of all potentially accessible intermediates is computationally too demanding. This renders brute-force explorations unfeasible, while explorations with completely pre-defined intermediates or hard-wired chemical constraints, such as element-specific coordination numbers, are not flexible enough for complex chemical systems. Here, we introduce a STEERING WHEEL to guide an otherwise unbiased automated exploration. The STEERING WHEEL algorithm is intuitive, generally applicable, and enables one to focus on specific regions of an emerging network. It also allows for guiding automated data generation in the context of mechanism exploration, catalyst design, and other chemical optimization challenges. The algorithm is demonstrated for reaction mechanism elucidation of transition metal catalysts. We highlight how to explore catalytic cycles in a systematic and reproducible way. The exploration objectives are fully adjustable, allowing one to harness the STEERING WHEEL for both structure-specific (accurate) calculations as well as for broad high-throughput screening of possible reaction intermediates. Automated reaction exploration is the key to systematic elucidation of chemical mechanisms. Here, the authors introduce a generally applicable algorithm to steer an automated exploration towards region of interest in chemical reaction space. [ABSTRACT FROM AUTHOR]

Introduction: Cars are increasingly computerized, and vehicle settings such as steering gain (SG) can now be altered during driving. However, it is unknown whether transitions in SG should be adaptable (i.e., triggered by driver input) or adaptive (i.e., triggered automatically). We examined this question for road segments expected to require different SG. Objective: This paper aimed to investigate whether SG mode changes should be made by the driver or automatically. Methods: Twenty-four participants drove under four conditions in a simulator: fixed low gain (FL), fixed high gain (FH), a machine-initiated steering system, which switched between the two SG levels at predetermined locations (MI), and a driver-initiated steering system, in which the SG level could be changed by pressing a button on the steering wheel (DI). Results: Participants showed poorer lane-keeping and reported higher effort for FH compared to FL on straights, while the opposite held true on curved roads. On curved roads, the MI condition yielded better lane-keeping and lower subjective effort than the DI condition. However, a substantial portion of the drivers gave low preference rankings to the MI system. Conclusion: Drivers prefer and benefit from a steering system with a variable rather than fixed gain. Furthermore, although automatic SG transitions reduce effort, some drivers reject this concept. Application: As the state of technology advances, MI transitions are becoming increasingly feasible, but whether drivers would want to delegate their decision-making authority to a machine remains a moot point. [ABSTRACT FROM AUTHOR]

Wake interference between turbines in wind farms can lead to significant losses in the overall power output from farms. Wake steering is a strategy in which yaw is introduced in the upstream turbines with respect to the incoming flow field to reduce wake interference with downstream turbines. To characterize the effectiveness of wake steering for turbines located on a hilly terrain, an open source simulator for wind farm applications has been used to perform large eddy simulations (LESs) of a 5 megawatt (MW) wind turbine located at the base of a sinusoidal hill. The height and length of the hill, as well as the turbine yaw angle, are systematically varied over a series of 10 simulations in which inflow corresponds to the neutral atmospheric boundary layer. Results from the LES statistics show that, for a given yaw angle, the power output from the turbine is determined primarily by the height of the hill, rather than the length of the hill. The magnitude of the centerline wake deficit and equivalent wake radius are reduced due to the presence of hills and are not very sensitive to the yaw angle. The theoretical prediction of the wake recovery appears to qualitatively agree with the LES statistics. The yaw-induced spanwise wake deflection is not affected by the hill height significantly. Streamwise vorticity distribution within the lower half of the wake intensifies due to the presence of strong mean velocity gradients present near the surface of the hill, which, in turn, leads to a reduction in the distortion of the shape of a wake deficit cross section. [ABSTRACT FROM AUTHOR]

An advanced driver-assistance system (ADAS) is critical to driver–vehicle-interaction systems. Driving behaviour modelling and control significantly improves the global performance of ADASs. A haptic assistive system assists the driver by providing a specific torque on the steering wheel according to the driving–vehicle–road profile to improve the steering control. However, the main problem is designing a compensator dealing with the high-level uncertainties in different driving scenarios with haptic driver assistance, where different personalities and diverse perceptions of drivers are considered. These differences can lead to poor driving performance if not properly accounted for. This paper focuses on designing a data-driven model-free compensator considering various driving behaviours with a haptic feedback system. A backpropagation neural network (BPNN) models driving behaviour based on real driving data (speed, acceleration, vehicle orientation, and current steering angle). Then, the genetic algorithm (GA) optimises the integral time absolute error (ITEA) function to produce the best multiple PID compensation parameters for various driving behaviours (such as speeding/braking, lane-keeping and turning), which are then utilised by the fuzzy logic to provide different driving commands. An experiment was conducted with five participants in a driving simulator. During the second experiment, seven participants drove in the simulator to evaluate the robustness of the proposed combined GA proportional-integral-derivative (PID) offline, and the fuzzy-PID controller applied online. The third experiment was conducted to validate the proposed data-driven controller. The experiment and simulation results evaluated the ITAE of the lateral displacement and yaw angle during various driving behaviours. The results validated the proposed method by significantly enhancing the driving performance. [ABSTRACT FROM AUTHOR]

With the increasing levels of intelligence and automation, the relationship between humans and vehicles has evolved from a utilitarian perspective to a partnership. Among the crucial factors for enhancing user experiences are the analysis of driving tasks, the construction of user needs models, and the design of intelligent interfaces. Based on this background, this paper proposes a cognitive task analysis model using intelligent steering wheel information interaction design as the vehicle. The model aims to extract key design elements to assist designers in making design decisions, thereby improving the human-machine cooperation performance of intelligent automobiles and enhancing user perceptual experiences. Firstly, within the context of human-machine cooperation systems, a cognitive task analysis method integrating the SRK model is proposed. By analyzing the behavioral decision characteristics between the vehicle and the user, a framework for the human-machine interface (HMI) logic of the steering wheel and a dynamic layout prototype are established. Secondly, the design of the steering wheel's HMI interaction is based on an analysis of users' affective needs and rational physiological characteristics. This paper integrates the analysis of users' affective needs to identify design elements that align with a high level of user satisfaction. Lastly, the design methodology model is applied to a navigation scenario, resulting in the creation of a steering wheel HMI prototype within a human-machine cooperation system. The prototype is then subjected to a combined subjective and objective experimental analysis, thereby validating the superiority of the steering wheel HMI's detection indicators over those of the central control HMI and establishing the design pattern for the steering wheel HMI. [ABSTRACT FROM AUTHOR]

In this paper, a new approach to the optimized design of outer rotor Permanent Magnet Synchronous Motors (PMSMs) for in-wheel light electric vehicle (LEV) applications is presented. The optimized design study is based on various dimensions such as back iron depth, permanent magnet depth and air gap length. The novel method is developed to reveal the quality factor of design (QFD), which implies the maximum possible performance results, and determine the best possible design for in-wheel PMSMs for direct-drive LEV applications. Therefore, the thickness of the back iron, permanent magnet and air gap dimensions are altered accordingly to obtain an optimized design. This design study is conducted for an in-wheel PMSM that has rated values of 2.5 kW, 150 V, 900 min−1, and 24-slot/20-pole configuration intended for LEV propulsion. These designs are simulated in order to obtain the maximized combination of efficiency, shaft power, shaft torque and a minimized combination of total weight, iron losses, copper losses, input current and cogging torque. The measure of the optimized parameters is named QFD, which indicates the goodness of the design through the use of radar charts. The values of the essential coefficients of QFD may vary for different applications, e.g., the design of PMSMs used in traction applications has some certain criteria that imply high-performance operation. Additionally, the QFD can guide motor manufacturers as a starting point for a design study. [ABSTRACT FROM AUTHOR]

Steering torque constitutes the primary motorcycle control input for the lateral dynamics; consequently, estimating it is important. Conventionally, this is done with complete motorcycle models, requiring significant identification effort. The simplified models in the literature only describe the steering torque under specific cases. This work defined a steering assembly model with few parameters to estimate the steering torque analytically for stationary and transient manoeuvres. The model equations followed from existing motorcycle models through simplifying hypotheses; transfer functions describing the roll response and the Lane Change Roll Index (LCRI) were obtained from these equations. Measured steering torque signals from different datasets, including diverse motorcycle classes, were used as the reference for validation. A good agreement resulted between the estimated and measured torques, in the time and speed-acceleration domains and in terms of LCRI. When using the roll as the motorcycle response, manoeuvrability was highest at lower frequencies. The scooter was the most manoeuvrable; the sports and touring motorcycles were the least manoeuvrable at low and high frequencies, respectively. Concerning design parameters, the front-wheel spin inertia and front twist stiffness influenced manoeuvrability the most. The model allows recreating the steering torque signal for new and pre-existing datasets using commonly measured signals; the signal can describe the riding style and the effort required. Few parameters are required, facilitating its use and reducing the computational burden, allowing its use for steering assistance systems. [ABSTRACT FROM AUTHOR]

In this paper, a multiobjective optimisation method considering the rail profile at high speed is presented. Based on nonuniform rational B-spline (NURBS) theory, a parameterised model of the rail profile is constructed to determine the variables. The wear index, wheel–rail lateral force and contact stress are selected as the objective function. Based on multibody dynamics theory is applied to establish the vehicle–track coupling dynamic model of the high-speed train CRH380A. An improved NSGA-II algorithm called CN-NSGA-II is proposed in this study. The multi-objective optimisation problem of rail profile is improved by the vehicle–track coupling dynamics model and CN-NSGA-II algorithm. The Pareto front solution is calculated, and the TOPSIS algorithm is used to select the optimal solution and obtain the optimised profile. The analysis of the objective function values of the CN60 profiles and the optimised profile indicate that the maximum wheel–rail lateral force of the left and right wheels of the steering wheelset decreases by 13.3% and 13.71%, the maximum contact stress decreases by 10.35% and 3.21%, and the maximum wear number decreases by 36.09% and 11.4%. The new optimised rail profile can effectively reduce the wheel–rail lateral force, contact stress and wear number in high-speed running conditions. [ABSTRACT FROM AUTHOR]

To enhance the path tracking capability and driving stability of intelligent vehicles, a controller is designed that synergizes active front wheel steering (AFS) and direct yaw moment (DYC), specifically tailored for distributed-drive electric vehicles. To address the challenge of determining the weight matrix in the linear quadratic regulator (LQR) algorithm during the path tracking design for intelligent vehicles on conventional roads, a genetic algorithm (GA)-optimized LQR path tracking controller is introduced. The 2-degree-of-freedom vehicle dynamics error model and the desired path information are established. The genetic algorithm optimization strategy, utilizing the vehicle's lateral error, heading error, and output front wheel steering angle as the objective functions, is employed to optimally determine the weight matrices Q and R. Subsequently, the optimal front wheel steering angle control (AFS) output of the vehicle is calculated. Under extreme operating conditions, to enhance vehicle dynamics stability, while ensuring effective path tracking, the active yaw moment is crafted using the sliding mode control with a hyperbolic tangent convergence law function. The control weights of the sliding mode surface related to the center-of-mass lateral declination are adjusted based on the theory of the center-of-mass lateral declination phase diagram, and the vehicle's target yaw moment is calculated. Validation is conducted through Matlab/Simulink and Carsim co-simulation. The results demonstrate that the genetic algorithm-optimized LQR path tracking controller enhances vehicle tracking accuracy and exhibits improved robustness under conventional road conditions. In extreme working conditions, the designed path tracking and stability cooperative controller (AFS+DYC) is implemented to enhance the vehicle's path tracking effect, while ensuring its driving stability. [ABSTRACT FROM AUTHOR]

The transportation of emergency supplies is characterized by real-time, urgent, and non-contact, which constitute the basic guarantee for emergency rescue and disposal. To improve the yaw stability of the four-wheel-drive unmanned emergency supplies transportation vehicle (ESTV) during operation, a two-layer model predictive controller (MPC) method based on a Kalman filter is proposed in this paper. Firstly, the dynamics model of the ESTV is established. Secondly, the improved Sage–Husa adaptive extended Kalman filter (SHAEKF) is used to decrease the impact of noise on the ESTV system. Thirdly, a two-layer MPC is designed for the yaw stability control of the ESTV. The upper-layer controller solves the yaw moment and the front wheel steering angle of the ESTV. The lower-layer controller optimizes the torque distribution of the four tires of the ESTV to ensure the self-stabilization of the ESTV operation. Finally, analysis and verification are carried out. The simulation results have verified that the improved SHAEKF can decrease the state estimation error by more than 78% and achieve the noise reduction of the ESTV state. Under extreme conditions of high velocity and low adhesion, the average relative error is within 6.77%. The proposed control method can effectively prevent the instability of the ESTV and maintain good yaw stability. [ABSTRACT FROM AUTHOR]

Automatic guidance of agricultural vehicles requires automatic execution of operation commands received from the navigation controller by using electronically controlled mechanisms for wheel steering, speed changing and work implementing. Automatic steering contributes as a prerequisite technique in automatic and semi-automatic agricultural navigation. This research aimed to develop an electric automatic steering system that was compact in its structure and integrated into original steering mechanism in a simply and convenient way for aftermarket modification. A brushless motor and reducer assembly was utilized to provide an adequate steering torque instead of manual maneuver. A rapid assembling approach was proposed by passing the steering shaft through the hollow output shaft. A digital proportional-integral-differential (PID) algorithm was implemented to calculate the rotation speeds and directions by comparing the desired angle and the actual angle, which was implemented in a printed circuit board with a microcontroller unit (MCU) and interface chips. An unmanned wheeled tractor was applied as test platform to integrate the newly developed electric automatic steering system. Tests were conducted to evaluate its performance in terms of stability and responsiveness. An autonomous navigation system guided the tractor along target paths in the field by sending steering commands to the electric automatic steering system. The results show that the steering angle error was less than 0.81° when desired steering angle was less than 10°. The lateral error difference was no more than 4.76 cm when repeating following the same target path, which indicated that the electric automatic steering system responded accurately and robustly to steering commands. [ABSTRACT FROM AUTHOR]

• The interrelatedness of 8 driving performance parameters was investigated. • Average steering angle is strongly correlated to standard deviation of steering angle. • Mean lateral position is a relatively insensitive measure of driving performance. • Out-of-lane is relatively independent of the other parameters. Introduction: Loss of attention leads to less steady driving within the lane and is one of the main causes of road accidents. To improve road safety, vehicle-based parameters such as steering wheel angle and lateral position are used to objectively assess driving performance, especially in monotonous driving tasks. Method: The present driving simulator study investigated the extent to which eight commonly used parameters are independent indicators of driving performance. Fifteen participants undertook a monotonous highway driving task for 1 h. Four steering angle parameters were examined: average steering angle (ASA), standard deviation of steering angle (SDSA), steering angle range (SAR), and steering reversal rate (SRR); as well as four lateral position parameters: mean lateral position (MLP), standard deviation of lateral position (SDLP), lateral position range (LPR), and the out-of-lane duration. Measurements were averaged across 2-minute epochs. Repeated measures correlation analysis evaluated the similarity between each parameter, and the variance inflation factor test evaluated the multicollinearity of all the parameters. Results: The results demonstrated that some parameters are highly correlated and should not be used together to assess driving performance. It is recommended that the optimal combination is ASA and SAR to assess steering angle, and SDLP and out-of-lane to assess lateral position. Out-of-lane, as a factor directly contributing to road safety, is recommended because it has the least correlation with other parameters. Practical Applications: If implemented, these recommendations may improve the assessment of driving performance in future studies. [ABSTRACT FROM AUTHOR]

This paper presents a new sensorless control method for four-wheel steering vehicles. Compared to the existing sensor-based control, this approach improved dynamic stability, manoeuvrability and robustness in case of malfunction of the front steering angle sensor. It also provided a software redundancy and backup solution, as well as improved fault tolerance. The strategy of the sensorless control is based on the sliding mode method to estimate the replacement of the front steering input from the errors between the vehicle's measured and desired values of the vehicle's sideslip angle and yaw rate. The simulation results demonstrate that the observer effectively estimated the front wheel steering angle at both low- and high-speed scenarios in the cornering and lane change manoeuvres. Furthermore, the sensorless control approach can achieve equivalent control performances to the sensor-based controller including a small and stable yaw rate response and zero sideslip angle. The results of the study offer a potential solution for improving manoeuvrability, stability and sensor fault tolerance of four-wheel steering vehicles. [ABSTRACT FROM AUTHOR]

In precision agriculture, path planning for agricultural robots with complete covering a three-dimensional landscape is an essential task. For robots with front wheels steering the normal curvature of the trajectories should be limited to some value determined by the characteristics of the vehicle. The paper considers a method of deformation of these paths to account for obstacles for trajectories described by homogeneous cubic B-splines. We propose an optimization problem that allows calculating paths with minimizing skips in the coverage. The considered problem is convex and belongs to the class of second-order cone programming, which entails the possibility of its computationally efficient solution. The computational examples are presented. [ABSTRACT FROM AUTHOR]

Most research on automating gravel pile transport using wheel loaders has been performed primarily through simulations. Thus, studies should evaluate the usefulness of automatic gravel pile transportation by demonstrating it with an actual wheel loader. This study demonstrates automatic driving control using a retrofitted 3-ton wheel loader for gravel pile transportation. The driving model of a retrofitted wheel loader, in which multiple control systems are interlocked, is considered a simple control model with one input and one output for the pedal and vehicle velocity as well as for the steering wheel and steering angular velocity. In this study, we propose a simple and practical method for constructing a driving model via simple response analysis using an actual machine by constructing a feedforward control model based on control input/output using step responses. In this study, feedforward control was applied to the translation of the vehicle, which has a large dead time. By generating the path following the target point from the vehicle predicted position after the dead time from the driving model, the appropriate control input value calculation considering the dead time was performed. By applying the proposed method to a retrofitted wheel loader in a real environment and evaluating the control performance through control experiments, the effectiveness of the proposed method in practice was demonstrated. [ABSTRACT FROM AUTHOR]

In this research, an 8 × 8 scaled electric combat vehicle (SECV) is built. The scaled vehicle is evaluated in both experimental and simulated methods to analyze its performance. The scaled vehicle is developed to apply the Ackermann condition by implementing the individual steering and individual wheel speed control system at low speed. Individual eight-wheel rotational velocity control and individual eight-wheel steering angle control in real time are developed and installed on the remotely controlled scaled vehicle to meet a perfect Ackermann condition. Three different steering scenarios are developed and applied: a traditional steering scenario (first and second axle steering), fixed third axle steering scenario (first, second, and fourth axle steering), and all-wheel steering scenario. Stationary evaluation, turn radius evaluation, and double lane change evaluation are conducted to verify the application of the Ackermann condition. The differences between the experimental results and the simulated data are within an acceptable range. An important demonstration of this research is the novel validation of physical and simulated data in the application of the Ackermann condition for eight-wheel steering and velocity control for the three steering scenarios. [ABSTRACT FROM AUTHOR]

A driver's active emergency response in dangerous traffic scenes consists of two parts, including reaction behavior and physiological state. In dangerous traffic scenes, the driver's active emergency response has an important impact on human collision injury. Clarifying the driver's active emergency response is an important prerequisite for the study of human collision injury under nonstandard posture. Therefore, this study investigates the driver's active emergency response in different inevitable collision scenes using driving simulator. A driving simulator with a high-speed camera system and human physiological signal acquisition system was first built. Then, three typical vehicle dangerous collision scenes were developed, including frontal collision, side collision, and rear-end collision. Finally, twenty participants (15 males and 5 females) were recruited for a driving experiment, and their active emergency responses were recorded and analyzed. All subjects would rotate the steering wheel to the left or right in the active emergency state, and the rotation of the hand would also cause the subject's upper body to tilt in the same direction. The maximum angle for male subjects to rotate the steering wheel was 59.98°, while for the female subjects, it was 44.28°. In addition, the maximum grip force between the male subjects and the steering wheel was 280.5 N, compared to 192.5 N for female subjects. Compared to the female participants, the male participants not only have a greater rotation angle and a greater grip force on the steering wheel, but also have greater pressure on the brake pedal, and the foot moves quickly from the accelerator pedal to the brake pedal and presses the brake pedal. Drivers have different active emergency responses to different vehicle collision scenes. Quantitative statistics of driver's active emergency response will have important guiding significance for the analysis of the impact of human active emergency response on human injury characteristics in subsequent vehicle collision experiments. [ABSTRACT FROM AUTHOR]

In order to rapidly evaluate the working performance of an Electric Power Steering System (EPS) and clarify the key indicators that affect its working condition, targeted maintenance measures can be taken to improve the vehicle's handling, stability, and safety. This paper took an EPS system as the research object and decomposed it into five working indicators: steering lightness, steering returnability, steering wheel mid-zone performance, steering wheel vibration, and power assist characteristics. On this basis, the subjective exponential extension Analytic Hierarchy Process (AHP) was combined with the objective improved entropy weight method, and then the combination weighting method of game theory was employed to ensure that the results were reliable. At the same time, a cloud model was introduced to understand the actual situation of each indicator by using cloud parameters and multi-level feedback results. According to the principle of maximum membership and the comprehensive evaluation method, the EPS performance evaluation result was obtained. The results showed that the comprehensive cloud parameter of EPS was U (74.31, 6.08, 0.50), which indicated that its working performance was in good condition, with some minor damage that did not affect its normal use; however, attention should be paid to the maintenance and repair of vulnerable components to ensure the handling, stability, and safety of the vehicle. [ABSTRACT FROM AUTHOR]

Autonomous vehicles stand at the forefront of technological advancement, promising to alter fundamentally how we navigate, with implications for everything from intercity logistics to daily commuting. The expected benefits of this technology include advancements in safety protocols, streamlining of traffic flow, and superior comfort for those onboard. However, the path to widespread integration is paved with significant barriers that need to be overcome. This article provides a thorough review of the historical development of autonomous driving technologies, beginning with the early vehicles powered by internal combustion engines and extending to the present excitement over Apple's Project Titan. Rumors suggest this particular project involves the creation of an electric vehicle fully capable of operating without traditional manual controls, including steering wheels and seats. The article concludes by spotlighting the potential for autonomous vehicles to enable disruptive changes in the transport sector, while also considering the myriad of challenges that must be surmounted for their adoption on a grand scale. [ABSTRACT FROM AUTHOR]

Due to the negative impact of chemical inter-row weed control on the environment, mechanical weed control is increasingly used in practice. Machine-tractor units (MTU) are used with the row cultivator's rear and frontal central position for its implementation. We have designed a unit in which side cultivators are used along with the central one. This paper considers the transverse movement kinematics of such an MTU's outside right and left cultivators' working devices in the horizontal plane. The present emulation of side machines is made by changing the longitudinal coordinate of their location relative to the tractor's front and rear axles. Calculations have established that the frontal cultivator responds more intensively to the control action by changing the turning angle of the tractor's steering wheels. However, if the value of this parameter is less than 2.75°, a rear-mounted cultivator is preferred, because in this case, the values of lateral deviations for the external, left, and right working device are smaller. When the turning angle of tractor wheels is from 1° to 3° (typical for MTU row work), a threefold increase in the working width of the cultivator causes a slight antiphase deviation in its external working devices (an increase or decrease in the amplitude of these deviations does not exceed 4%). The model that we have developed allows us to select the values of the MTU design parameters for which the lateral displacement will be very small (close to zero). As the turning angle of the tractor wheels increases to 3°, the external left and right working devices of the cultivators react inversely. This means that in the case of the rear machine, the values of lateral displacements increase, while in the case of the front machine, they decrease. At a turning angle of the tires of the tractor wheels close to 2.5°, the lateral displacements for the rear and front machines are the same. [ABSTRACT FROM AUTHOR]

In this paper, a chained architecture of virtual track train (VTT), gantry virtual track train (G-VTT), was proposed. The distributed virtual driving model (DVD model) for G-VTT was proposed to realise the automatic tracking of paths with arbitrary ommon curves. The core of the DVD model is the cascade modular wheel steering angle (WSA) control algorithm based on the optimal lateral acceleration (OLA) of tracking points. A local tracking objective function based on 2-norm was established to find the OLA in the preview window, thereby the optimal local trajectory was realised. The cascade structure of the algorithm gives the DVD model the advantages of modularity and high scalability, facilitating the cascade control of an n-unit G-VTT. The proposed control strategy was verified by co-simulation of G-VTT dynamics model and the DVD model. Results show that the DVD model can effectively improve the path-following ability and small-curve-passing capacity of the G-VTT with remarkable reduction of the lateral deviation (up to about 99.16%) and turning passageway width (about 59.71%); Besides, the yaw and anti-jackknifing stability of the train are also significantly improved, and the DVD model is well adaptable to speed, curve radius, and payload. [ABSTRACT FROM AUTHOR]