Wheel Angle Research Articles

Assessing the Effects of Modalities of Takeover Request, Lead Time of Takeover Request, and Traffic Conditions on Takeover Performance in Conditionally Automated Driving

When a conditionally automated vehicle controlled by the machine faces situations beyond the capability of the machine, the human driver is requested to take over the vehicle. This study aims to assess the short-term effects of three factors on the takeover performance: (1) traffic conditions (complex and simple); (2) modality of takeover request (auditory and auditory + visual); (3) lead time of takeover request (TORlt, 5 s and 7 s). The scenario is the obstacle ahead. Indicators include: (1) Take Over Reaction Time (TOrt); (2) approximate entropy (ApEn), operating order of steering wheel Angle and pedal torque; (3) the choice of target lane and speed of lane-changing; (4) mean and standard deviation of acceleration and velocity; (5) quantifiable lateral cross-border risk and longitudinal collision risk. A driving simulation experiment is conducted to collect data for analysis. The effects of the three factors on takeover performance are analyzed by analysis of variance (ANOVA) and non-parametric tests. The results show that when the traffic conditions are complex, drivers have a larger ApEn of the steering wheel angle and brake pedal torque, and a smaller ApEn of acceleration pedal torque. In the 5 s TORlt case, drivers have a smaller ApEn of brake pedal torque the interaction between TORlt, traffic conditions, and modality of TOR affects ApEn of accelerator pedal torque. 5 s TORlt/complex traffic condition makes the scene more urgent, which is easy to cause driver to make sudden and simultaneous turning and sudden braking dangerous behavior meanwhile. Compared with other combinations of modality and TORlt, the combination of 7 s and auditory + visual significantly reduces the lateral cross-border risk and longitudinal collision risk.

Improvements on the Dynamical Behavior of a HiL-Steering System Test Bench

Shorter available development times and fewer available vehicle prototypes have increased the subsystem-based investigation on test rigs within the automotive development process. Steering systems exhibit a direct interface to the driver, therefore, posing high requirements to the control performance of a test bench, especially for the perception of steering feel. This work proposes three approaches to improve the force control performance of permanent magnet linear motors incorporated on a steering test bench. The first method improves control accuracy when a harmonic force signal is introduced into the steering system by adjusting the reference force signal based on the identified peak values of the measured and reference forces. The second method allows the inclusion of the actuator’s inertia and the occurring ratios between steering wheel angle and rack displacement into the control scheme to reduce performance deterioration due to inertia. The third approach considers delay time in the actuator control and estimates its future position for delay compensation. A validation of the proposed methods is conducted, displaying an improvement for all three applications. The proposed methods extend the applicability of a steering test bench within the automotive development process by enabling more accurate and reproductible control performance.

Comparing the Effects of Visual Distraction in a High-Fidelity Driving Simulator and on a Real Highway

Driving simulators have been widely used in driving behavior analysis and intelligent driving algorithm development. However, the validity of driving behavior data derived from driving simulators remains unclear. In this study, 30 Chinese drivers were recruited to participate in two experiments: on-road and simulator experiments. An instrumented vehicle and a high-fidelity simulator were used in the on-road and simulator experiments, respectively, to investigate the effects of high speed (60, 80, and 100 km/h) and a visual distraction task on the lateral driving performance, including lane deviation (LD), standard deviation of the lane position (SDLP) rate, standard deviation of the steering wheel angle (SDSWA) rate, and steering wheel reversal rates (SRRs) (at levels of 1.3° and 2.5°). It was found that the visual distraction task impaired the drivers’ lane-keeping ability. Furthermore, the driving task had similar effects on the LD, SDLP rate, and SRRs (2.5°) in the on-road and simulator experiments. The effects of the driving speed on the LD, SDLP rate, and SDSWA rate were comparable in both driving environments. However, the results confirmed that even a high-fidelity driving simulator could not achieve perfect absolute validity. The results provided preliminary evidence that the high-fidelity driving simulator used in this study might be an effective tool for investigating the effects of visual distractions task on lateral driving behavior.

Trajectory tracking control considering the transmission backlash of the dual-motor autonomous steering system

In order to solve the tracking error caused by transmission backlash in the dual-motor autonomous steering system, and thus improve the trajectory tracking accuracy of autonomous vehicles, a novel steering control strategy combining sliding model control (SMC) with variable weights and linear quadratic regulator (LQR) is proposed in this paper. Firstly, the vehicle dynamics model is built and the steering system model with contact and backlash modes is established by combining it with the simplified gear backlash system. Secondly, the front wheel angle control based on SMC is designed and the weights of contact mode and backlash mode are optimized by Particle Swarm Optimization (PSO) algorithm. Finally, the results of simulation under typical conditions show that the proposed strategy has better accuracy of angle tracking and stronger adaptability to the change of system load. Furthermore, it is also proved by simulation and Hardware-in-Loop (HiL) test under different trajectory tracking conditions that the LQR controller with proposed strategy has better accuracy of trajectory tracking for autonomous vehicles.

Effect of Process Parameters on Spinning Force and Forming Quality of Deep Cylinder Parts in Multi-Pass Spinning Process

In this paper, based on MSC Simufact.Forming v16.0 simulation software, the process parameters in the multi-pass spinning production of deep cylinders with a large diameter–thickness ratio are optimized, and the ten-pass spinning process of a deep cylinder with a diameter of 500 mm, thickness of 2 mm and depth of 700 mm is realized. By controlling the four process parameters of mandrel speed, feed rate, spinning wheel fillet radius and spinning wheel angle of attack, the influence of the four process parameters on the spinning force and the wall thickness deviation of the formed workpiece is studied. The results show that the radial spinning force and tangential spinning force are at their minimum when the mandrel speed, feed rate, spinning wheel fillet radius and spinning wheel angle of attack are 500 rpm, 1 mm/rev, 6 mm and 35°, respectively. At these setup conditions, the spinning efficiency is the highest and the workpiece is not prone to defects. The wall thickness deviation of the workpiece will decrease with the increase in the mandrel speed; with the increase in the feed rate, the radius of the round corner and spinning wheel angle of attack, the wall thickness deviation increases first and then decreases.

The programmable steering machine for the electric lightweight vehicle

In this paper a programmable steering machine (PSM) and the lightweight electric powered vehicle, designed and made at the Kazimierz Pulaski University of Technology and Humanities in Radom (UTH Radom) have been presented. Both these technical objects are the result of the project carried out by the Student Research Group ?Turbodoładowani?. The steering machine has been developed with the programmable algorithms allowing to execute a controllable movement of the vehicle steering wheel. After execution, the system does not need an interaction with the driver. For this reason, a higher repetition of the vehicle traction measurements can be achieved. Such confirmation obtained in tests within which the time waveforms of rotation angle of the steering wheel by a set value of 45, 90, 180 and 360 degrees was recorded. In particular, the accuracy index for mentioned test conditions was calculated. Obtained results, expressed by the average value of the sensitivity index were lower than 2% within the tests carried out for ?45 degree maneuvers. In case of other tests i.e., for ?90, ?180 and ?360 degree maneuvers the accuracy index value was lower than 0.3%. In this way, it was confirmed that the tested PSM reached the appropriate operating parameters necessary for vehicle traction tests.

Pre-emptive Braking Control for Stability Improvement of Autonomous Vehicles on a Curved Road Based on Vehicle-to-Everything Technology

<div>Vehicle-to-everything (V2X) technology has played an important role in improving the active safety of autonomous vehicles. In order to improve the stability of the autonomous vehicle on the curved road, this article presents a pre-emptive braking control method based on V2X technology. Instead of using the active safety system to try to stabilize the vehicle in case of danger, the pre-emptive braking action is proposed to reduce the vehicle speed in advance to a level that allows safe navigation of the turn to avoid danger. It is assumed that the friction and curvature of the curved road ahead can be obtained through V2X technology. Combined with a linear two degrees of freedom (2-DOF) bicycle model, an optimal control method is adopted to calculate the front and rear wheel steering angles to track the centerline of the curve lane. A more complex vehicle dynamics model established earlier is selected for simulation analysis to verify the proposed control method. The results show that the proposed control method can improve greatly the stability of the autonomous vehicle on the curved road.</div>

Mechanism Analysis and Control of Lateral Instability of 4WID Vehicle Based on Phase Plane Analysis Considering Front Wheel Angle

The current vehicle stability judgment methods in the stability control of distributed drive electric vehicles focuses on using the phase plane method, but the existing phase plane stability boundary delineation methods ignore the influence of the front wheel rotation angle. Therefore, a new phase plane partitioning method (NPPPM) is proposed in this paper, and a new phase plane stability domain library is established. Based on this, the direct yaw moment control of distributed drive electric vehicle through phase plane analysis is established. Firstly, the influence of the front wheel turning angle on the phase plane is considered in the design of the phase plane stability boundary, and a new stability boundary function is fitted by the least squares method. This approach avoids the lateral instability caused by too large a front-wheel turning angle and improves the accuracy of vehicle stability judgment. Next, a hierarchical direct transverse moment controller is constructed using Matlab/Simulink. Among them, the upper layer controller adopts sliding mode control with the sideslip angle error of the center of mass as the tracking target. The lower controller adopts the optimal configuration algorithm with the optimal road adhesion utilization as the objective function. Finally, the effectiveness and superiority of the proposed phase plane partitioning method (NPPPM) for distributed drive electric vehicles with sinusoidal and angular order inputs for the front wheel rotation angle are verified by simulation.

Optimal coordinated control of active steering and direct yaw moment for distributed-driven electric vehicles

This paper considers the problem of optimal coordination of active steering and direct yaw moment for general electric vehicles equipped with in-wheel motors and steer-by-wire devices. As a typical over-actuated system, vehicle planar motion shows flexible control performance under different wheel actuations, thus achieving the optimal integration among them is of critical significance. Based on the modeling from wheel actuations of motor torques and wheel angles to vehicle planar motion states, this paper proposes an optimal hierarchical control framework composed of a reference state generator, a linear time-varying model predictive controller (LTV-MPC), and an optimal control allocator. Motor torques and wheel angles are decoupled as equivalent distributed variables by linearization technique. Then an optimal allocation framework is formed towards the trade-off between driving security and power saving, and a compensation module based on the back-propagation neural network (BPNN) is designed to guarantee the control allocation accuracy. Holistic multi-objective optimization is demonstrated through Co-simulations with Matlab/Simulink and TruckSim. Results verify the effectiveness of the proposed control strategy.

Distraction pattern classification and comparisons under different conditions in the full-touch HMI mode

Understanding driver distraction patterns is an important part of human–machine interaction (HMI), which is beneficial for the development of control strategies in human–machine co-driving systems. However, comparatively few studies have focused on driver distraction patterns. To address this issue, this study proposes a framework to characterize distraction patterns using glance behavior and manual behavior, and classifies distraction patterns into: aggressive, moderate, and conservative patterns based on real road experiments. Subsequently, differences in distraction behavior and effects on lateral vehicle control ability across distraction pattern groups, as well as distraction behavior differences exhibited by drivers in the same distraction pattern group under different conditions, are analyzed. Firstly, the results show that the aggressive distraction patterns have a smaller number of eyes-off-road (NoEOR) incidences but longer mean single eyes-off-road time (MSEORT), maximum single eyes-off-road time (MaxEORT) and a higher percentage of long eyes-off-road (PoLEOR) incidences than the other patterns. There are slight differences in the single eyes-off-road times (EORTs) between the conservative and moderate patterns and in the manual behavior for the aggressive and moderate distraction patterns. Secondly, the same distraction pattern exhibited by drivers for different road and secondary task conditions has differences in terms of the behavioral performance. Finally, there is few differences in the lateral motion of a vehicle with different distraction patterns. Surprisingly, the standard deviation of the steering wheel angle (SDSWA) is the smallest in the aggressive distraction pattern.

Coordinated Lateral Stability Control of Autonomous Vehicles Based on State Estimation and Path Tracking

This paper proposes a coordinated control system of autonomous vehicles’ lateral stability based on state estimation and path tracking, which contributes to enhancing path-tracking capability and at the same time maintaining the stability of autonomous vehicles driving in complex and dynamic road conditions. First, an extended Kalman filter observer is designed to estimate the vehicle parameters. Then, based on the single-point preview theory, an adaptive preview time model is built for the accuracy of path tracking. When designing the direct yaw-moment controller, sideslip angle and yaw rate, two critical parameters for lateral dynamic stability control are considered, and the single-wheel braking method is utilized to reasonably distribute the additional yaw moment to a certain wheel. To make full use of active front steering and direct yaw-moment control, the critical steering angle of the front wheel is determined, and a switching function is formulated. Co-simulation shows that the suggested coordinated control system can effectively strengthen maneuvering stability and simultaneously satisfy tracking accuracy.

Research on automatic emergency steering collision avoidance and stability control of intelligent driving vehicle.

In view of the need for emergency steering to avoid collision when the vehicle is in a dangerous scene, and the stability control of the vehicle during collision avoidance. This paper proposes a planning and control framework. A path planner considering the kinematics and dynamics of the vehicle system is used to formulate the safe driving path under emergency conditions. LQR lateral control algorithm is designed to calculate the output steering wheel angle. On this basis, adaptive MPC control algorithm and four-wheel braking force distribution control algorithm are designed to achieve coordinated control of vehicle driving stability and collision avoidance safety. The simulation results show that the proposed algorithm can complete the steering collision avoidance task quickly and stably.

Autonomous Vehicle Dataset with Real Multi-Driver Scenes and Biometric Data.

The development of autonomous vehicles is becoming increasingly popular and gathering real-world data is considered a valuable task. Many datasets have been published recently in the autonomous vehicle sector, with synthetic datasets gaining particular interest due to availability and cost. For a real implementation and correct evaluation of vehicles at higher levels of autonomy, it is also necessary to consider human interaction, which is precisely something that lacks in existing datasets. In this article the UPCT dataset is presented, a public dataset containing high quality, multimodal data obtained using state-of-the-art sensors and equipment installed onboard the UPCT's CICar autonomous vehicle. The dataset includes data from a variety of perception sensors including 3D LiDAR, cameras, IMU, GPS, encoders, as well as driver biometric data and driver behaviour questionnaires. In addition to the dataset, the software developed for data synchronisation and processing has been made available. The quality of the dataset was validated using an end-to-end neural network model with multiple inputs to obtain the speed and steering wheel angle and it obtained very promising results.

Driver Vigilance State Estimation Based on Multisource Data

Accurate estimation of driver vigilance state is important for improving driving safety and preventing accidents. Previous studies have extensively investigated the potential of driving performance measures (e.g., lane position, steering wheel angle, and lateral acceleration) and eye movement measures (e.g., glance frequency and duration) on the driver’s vigilance state estimation. However, the change in these measures may be caused by various factors. Therefore, comprehensively involving more direct measures (e.g., drivers’ electroencephalogram (EEG) response) is expected to increase the vigilance estimation performance. This study comprehensively uses measures across different functional domains (i.e., driving performance, eye movement, and EEG) to establish an estimation model of the driver’s vigilance state. Twenty participants were assigned a 2-h sustained-attention driving task in a simulator, in which they followed a lead car closely. According to their brake reaction times (RTs) to the brake signal of the lead vehicle, their vigilance states were divided into three levels (i.e., low, medium, and high). A logistic regression model that estimates drivers’ vigilance state level based on multisource data was established. This model is a promising approach to driver states detection and reveals the factors that should be considered in subsequent model development processes. It can be combined with driver vigilance arousal methods to improve driving safety.

Kajian Eksperimen Pengaruh Variasi Perubahan Sudut Kincir terhadap Kinerja Paddle Wheel Aerator

The main problem discussed in this study is the effort to improve the quality of shrimp pond water, especially the low dissolved oxygen levels by modifying the angle of the aerator wheel. The results showed that dissolved oxygen (DO) levels were very important in shrimp pond culture and that the paddle wheel aerator was a useful tool in intensive aquaculture systems because it could produce DO as needed. The study used a model designed using Solidworks software and tested at a scale of 1:10 with 3 variations of angles (15°, 35°, and 55°) at each wheel angle. The test results show that the angle variation affects the rotational speed and DO production, with the highest rotational speed and DO production achieved at an angle variation of 55°. However, the best parameters for the aerator were found at a variation of the wheel angle of 35° and a rotational speed of 300 rpm, because it produced the highest dissolved oxygen with relatively low power.

Research on the Stability Control Strategy of Distributed Electric Vehicles Based on Cooperative Reconfiguration Allocation

Aiming at the problem of body instability caused by actuator failure in a distributed electric vehicle drive system, a fault-tolerant control strategy of longitudinal and lateral force cooperative reconstruction with active steering control was proposed, and a layered control structure was adopted based on the vehicle model. In the upper controller, the resultant force and torque are calculated according to the vehicle parameter state and MPC algorithm; the lower controller is the cooperative reconfiguration allocation layer, and the minimum tire load rate, longitudinal and lateral force constraints and front wheel angle control are considered. Finally, offline simulation experiments and hardware-in-the-loop experiments are completed to verify the effectiveness and real-time performance of the designed strategy. The results show that the designed strategy can significantly improve the driving stability and safety of the vehicle when the actuator fails.

Distracted driving behavior in patients with insomnia

Insomnia is one of the most common sleep disorders and is characterized by a subjective perception of difficulty falling asleep. Drivers with insomnia are vulnerable to distraction and exhibit higher levels of risk while driving. This study investigated the effect of two sources of in-vehicle distractions on the driving performance of drivers with insomnia and good sleepers by analyzing different driving behavior measures. Twenty-one drivers with insomnia and twenty-one healthy volunteers were recruited to complete simulated driving dual tasks. The primary task required the participants to perform: (a) a lane-keeping task, and (b) a lane-change task. The secondary task required the participants to deal with: (a) baseline (non-task), (b) internal distraction task, and (c) external distraction task. The internal distraction task required participants to complete quantitative reasoning tasks, while the external distraction task was a 0-back test. The relationship between distracted driving ability and cognitive function was also investigated. The results demonstrate that for lane-keeping tasks, drivers with insomnia had significantly higher standard deviations (SD) for speed, throttle position, acceleration, and lateral position than healthy drivers under internal distraction, but the driving performance did not differ significantly between groups under internal distraction or baseline. In the lane-change task, drivers with insomnia had higher SDs for steering wheel angle, steer angular velocity, lateral acceleration, and lateral speed than healthy drivers under external distraction. Moreover, external distraction impaired driving behavior in the healthy group, while internal distraction impaired driving ability in both groups. Healthy drivers with cognitive impairment displayed impaired lane-keeping abilities under internal distractions and impaired lane-changing abilities under external distractions. Driving performance in the insomnia group was not significantly associated with cognitive function. The results demonstrate that insomnia and distraction impair driving ability, and driver performance is affected differently by the distraction source (internal or external). The driving ability of healthy drivers with decreased cognition was impaired, but not that of insomniacs.The findings of this study provide new insights for preventing and estimating the potential influence of distracted driving behavior in individuals with insomnia.

Enhanced Driver Drowsiness Detection using Deep Learning

The primary reason for road accidents is drowsiness reported by National Highway Traffic Safety Administration (NHTSA). To overcome this issue, researchers have proposed and implemented various methods based on driver behaviour and vehicle movements. Vehicle-based methods often rely on a set of predetermined parameters to detect drowsiness, such as changes in steering wheel angle or lane deviation. However, these parameters may not always accurately reflect a driver’s level of alertness. Therefore, it is essential to develop an effective approach for driver drowsiness detection. Deep learning techniques such as convolutional neural networks (CNN) are structured solutions to detect drowsiness based on drivers’ facial features. The proposed approach based on CNN focuses on the eyes and mouth region using the nose as a central point. CNN is operated with rectified linear activation function (ReLU) which gives 94.95% accuracy as compared to existing methods even in different situations namely low light, different angles, and transparent glasses.

Hierarchical Planar Platoon Control Toward Tracking Safety for Distributed-Driven Vehicles

This paper deals with the planar platoon control problem of distributed-driven electric vehicles considering modeling deviations and driving disturbances. The follower vehicle is arranged to track the leader vehicle on curved roads with high tracking accuracy and driving security. A hierarchical integrated control framework is proposed based on disturbance-rejection robust model predictive control (DRRMPC) strategy and optimal control allocation (OCA) approach. The upper layer is a feedforward control layer, where a linear time-varying (LTV) model predictive controller (MPC) produces constrained predictive control towards a nominal model. The time-interval-based reference states are obtained from a “shadow following” trajectory generator through vehicle-to-vehicle (V2V) communication. In the middle layer, the feedback control consisting of a robust controller (RC) and the disturbance compensation out of an extended state observer (ESO), take effect to reconfigure the LTV vehicle model into the nominal model with an off-line calculated constraint tube. The bottom layer performs optimal control allocation among decoupled motor torques and wheel angles towards minimal tire force utilization. Hardware-in-the-Loop (HIL) experiment validates the effectiveness and practicability of the proposed platoon control strategy.

An Adaptive Inverse Model Control Method of Vehicle Yaw Stability with Active Front Steering Based on Adaptive RBF Neural Networks

In order to enhance vehicle yaw stability, this paper proposes a novel adaptive inverse model control (AIMC) method for active front steering (AFS) design based on adaptive radial basis function (RBF) neural networks. To approximate the uncertain nonlinearity and modeling errors in vehicle dynamics, an adaptive RBF neural network (ARBFNN) controller is firstly designed. For smaller tracking errors and better robustness, a RBF networks-based AIMC system is further established. Apart from the ARBFNN control law, another two RBF neural networks are utilized, which work as model identifier and inverse model controller, respectively. After being trained offline, both of them are updated by using online learning algorithms. For faster learning and stability, adaptive learning rates are developed. Consequently, the inverse model controller is able to generate required steering wheel angle. Finally, the comparative studies are carried out and the simulation results illustrate the robustness and effectiveness of proposed control strategy.