Wheel Angle Research Articles

Adaptive Active Disturbance Rejection Control for Path Tracking of Intelligent Vehicle

The path tracking controller often cannot maintain the lateral stability of vehicles on large curvy roads with low adhesion road, which is easy to cause vehicle instability. In this paper, an adaptive active disturbance rejection control (ADRC) is designed to generate a front wheel angle and an additional yaw moment at the same time to guarantee the vehicle tracking accuracy and vehicle stability at the same time. The vehicle two‐degree‐of‐freedom reference model and the path tracking error model are built to derive the error dynamics state equation. The multiinput multioutput ADRC decoupling control is studied. The BP neural network is designed to online adjust the four output parameters that are difficult to be determined in the nonlinear state feedback to form an adaptive ADRC. The yaw moment distribution scheme is studied next. By the joint simulation and hardware‐in‐the‐loop experiment, the results show that the proposed adaptive ADRC can obtain good vehicle tracking accuracy and vehicle stability at the same time not only on both high and low adhesion roads but on the road with large curvatures.

Warning and active steering rollover prevention control for agricultural wheeled tractor.

Tractor rollover is regarded as the most fatal incident in agricultural production, but some of which can be avoided by timely anti-rollover warning and active control. There have been a lot of researches on the tractor rollover model building and rollover protective structure designing but few on the anti-rollover control. The purpose of this study is to develop a cheaper and practical anti-rollover control system based on active steering technique and to prove the efficiency of the proposed scheme for the wheeled tractors. A three-degree-of-freedom rollover dynamics model including automatic steering system is established. A control scheme by adjusting the roll angle to keep the stability based on the adaptive sliding mode control is proposed with the estimated lateral velocity according to the feedback correction principle. Front wheel angle tracking controller is designed adopting internal model control (IMC) theory. Simulation results exhibit that the active anti-rollover control can calculate the stability index in real time and can keep it within the stable range by adjusting the front steering wheel angle. It is prospective for the proposed scheme to provide a valuable reference to reduce tractor rollover accidents.

Design and Experiments of Autonomous Path Tracking Based on Dead Reckoning

Path tracking is an important component of autonomous driving and most current path tracking research is based on different positioning sensors, such as GPS, cameras, and LIDAR. However, in certain extreme cases (e.g., in tunnels or indoor parking lots), if these sensors are unavailable, achieving accurate path tracking remains a problem that is worthy of study. This paper addresses this problem by designing a dead reckoning method that is solely reliant on wheel speed for localization. Specifically, a differential drive model is first used for estimating the current relative vehicle position in real time by rear wheel speed, and the deviation between the current path and the reference path is then calculated using the pure pursuit algorithm as a means of obtaining the target steering wheel angle and vehicle speed. The steering wheel and vehicle speed signals are then output by two PID controllers in order to control the vehicle, and the automatic driving path tracking is ultimately realized. Through exhaustive tests and experiments, the stop position error and tracking process error are compared under different conditions, and the effects of vehicle speed, look-ahead distance, starting position angle, and driving mode on tracking accuracy are analyzed. The experimental results show the average error of the end position to be 0.26 m, 0.383 m, and 0.505 m when using BMW-i3 to drive one lap automatically at speeds of 5 km/h, 10 km/h, and 15 km/h in a test area with a perimeter of approximately 200 m.

Driving with a Haptic Guidance System in Degraded Visibility Conditions: Behavioral Analysis and Identification of a Two-Point Steering Control Model

The objective of this study is to determine the ability of a two-point steering control model to account for the influence of a haptic guidance system in different visibility conditions. For this purpose, the lateral control of the vehicle was characterized in terms of driving performance as well as through the identification of anticipation and compensation parameters of the driver model. The hypothesis is that if the structure of the model is valid in the considered conditions, the value of the parameters will change in coherence with the observed behavior. The results of an experiment conducted on a driving simulator demonstrate that the identified model can account for the cumulative influence of the haptic guidance system and degraded visibility. The anticipatory gain is sensitive to changes in driving conditions that have a direct influence on the produced trajectory, and the compensatory gain is sensitive to a decrease in the variability of the lateral position. However, a model with only the steering wheel angle as output is not able to determine whether the change in lateral position variability is due to the driver’s lack of anticipation or to the assistance provided by the haptic guidance system.

Interactive Lane Keeping System for Autonomous Vehicles Using LSTM-RNN Considering Driving Environments

This paper presents an interactive lane keeping model for an advanced driver assistant system and autonomous vehicle. The proposed model considers not only the lane markers but also the interaction with surrounding vehicles in determining steering inputs. The proposed algorithm is designed based on the Recurrent Neural Network (RNN) with long short-term memory cells, which are configured by the collected driving data. A data collection vehicle is equipped with a front camera, LiDAR, and DGPS. The input features of the RNN consist of lane information, surrounding targets, and ego vehicle states. The output feature is the steering wheel angle to keep the lane. The proposed algorithm is evaluated through similarity analysis and a case study with driving data. The proposed algorithm shows accurate results compared to the conventional algorithm, which only considers the lane markers. In addition, the proposed algorithm effectively responds to the surrounding targets by considering the interaction with the ego vehicle.

Exploring the influence of driver affective state and auditory display urgency on takeover performance in semi-automated vehicles: Experiment and modelling

As semi-automated vehicles become more available to the general public, it is important to investigate human factors, including both the driver side and the interface side. Despite much research on semi-automated vehicles, little research has conducted considering both driver states and takeover request display design. The present study investigated the effects of drivers’ affective states and auditory display urgency on takeover response time and performance quality. Thirty-six participants experienced takeover scenarios in a semi-automated vehicle using a driving simulator, while playing an online game. For takeover quality, angry drivers drove faster, took longer to change lanes and had lower steering wheel angles than neutral drivers, which made riskier driving. However, there was no difference in eye glance behaviors. Higher frequency and more repetitions of the auditory displays led to faster takeover reaction times, but there was no time difference between angry and neutral drivers. Drivers’ response time to takeover displays from both affect groups was modelled using the QN-MHP framework, which resulted in a R2 of 0.505 with the empirical data collected. In sum, results suggest that drivers’ anger state influenced takeover quality, while display urgency influenced takeover response time. This study is expected to make a significant contribution to research on the influence of emotion, specifically, anger on takeover performance in semi-automated vehicles as well as to the takeover display design.

Comparative Analysis of Vehicle-Based and Driver-Based Features for Driver Drowsiness Monitoring by Support Vector Machines

Driver drowsiness is a serious threat to road safety. Most driver monitoring systems (DMSs) already embedded in vehicles to detect drowsiness use vehicle-based features (i.e., measures) computed by outward-facing cameras for lane tracking or steering wheel angle sensors to analyze lane keeping and steering control behavior. Such DMSs are referred to as indirect DMSs as they monitor drowsiness indirectly through driving performance. In this work, we extend this classical technique by using a driver monitoring camera for tracking driver-based features associated with eye blinking behavior and head movements. We refer to DMSs based only on a driver monitoring camera as direct DMSs as they monitor drowsiness directly through observable driver-based behavioral cues. In this work, we conduct a comparative analysis between an indirect and direct DMS. We also combine vehicle-based and driver-based features to examine the potential of a so-called hybrid DMS. To this end, we use a database collected from 70 participants in driving simulator experiments. The comparative analysis is performed by means of the correlation-based feature selection technique and support vector machines independently. With a balanced accuracy of 87.1%, the direct DMS significantly outperforms the indirect DMS, which reaches a balanced accuracy of only 77.9%. The hybrid DMS achieves a slightly better balanced accuracy of 87.7%. This work motivates the development and use of direct or hybrid DMSs to detect driver drowsiness and increase road safety.

Freeway Traffic Safety Evaluation Using Virtual Reality: Focus on Compound Curve

The traditional method of designing freeway geometric characteristics may not well consider traffic safety and evaluation problems associated with vehicle, driver, road, and environmental features. This paper looks into accident-prone compound curves and puts forward a traffic safety evaluation method that can comprehensively assess various influencing factors. This method integrates driving simulation and Virtual Reality (VR) technology. Methodically, this paper first used three-dimensional (3D) design software to build the digital model and spatial scene model of the compound curves from the aspects of geometric structure, spatial characteristics, terrain information, and so on. Next, drivers were invited to conduct a series of driving simulation experiments upon the human–computer interaction safety experience platform, and driver physiological data and vehicle driving information collected. Last but not least, the mean values of heart rate changes, steering wheel angle changes, and driving trajectory changes were derived to synthesise the comprehensive traffic safety evaluation framework. Based on the analysis of the results of the orthogonal test, select the road plane design indicators that have a significant impact on the traffic safety evaluation, and carry out regression analysis. The research shows that the novel traffic safety evaluation method integrating with VR technology can comprehensively consider various influencing factors, and designers can dynamically adjust the design metrics according to the traffic safety level.

Improvement of HCF life of automotive safety components considering a novel design of wheel alignment based on a Hybrid multibody dynamic, finite element, and data mining techniques

The main aim of the present study is to improve the High-Cycle Fatigue (HCF) life of automotive safety components subjected to multi-input random non-proportional loading. A case study was conducted on the automotive knuckle as one of the high-critical mechanical components of the suspension and steering systems. The steering knuckle is subjected to the highest road- and steering-induced random loads that are exerted on several points (i.e., joints of knuckle with lower control arm, steering linkage, and Macpherson strut). Moreover, incorrect adjustment of wheel angles increases the intensity of road loads. In this regard, previous studies have shown that wheel angles affect the service time of this component. In the present study, the authors attempted to present a novel design of wheel alignment to reduce the intensity of 3D stress components, and subsequently, to increase the fatigue life of the steering knuckle. To this end, a Hybrid multibody dynamic, finite element, and data mining techniques was used. Accordingly, different load histories extracted through Multi-Body Dynamics (MBD) analysis of a full vehicle model were applied on various joints of this component. Next, a transient dynamic analysis was performed, and the time histories of 3D stress fields were obtained in the critical zone. The fatigue life of the component was predicted using the Critical Plane Method (CPM) via employing the Rain-flow cycle counting technique and Miner-Palmgren damage accumulation rule. Finally, the results of Taguchi Sensitivity Analysis (TSA) were used to investigate the effect of wheel primary angles, including Camber and Toe, on the fatigue life of the cast iron knuckle. Moreover, Response Surface Method (RSM) was utilized to optimize the wheel alignment, and the cyclic lifetime of the component was compared under the pre- and post-optimization process. The results reveal that knuckle life improves by 33.66% using the optimum settings of wheel alignment.

A Novel Control Framework of Brain-Controlled Vehicle Based on Fuzzy Logic and Model Predictive Control

Brain-controlled vehicles (BCVs) have vital practical values for the disabled and healthy people. To improve the performance of existing BCVs and lower the workload generated by BCVs to drivers, in this paper, we propose a novel control framework of BCVs, which consists of a brain-computer interface (BCI) with a probabilistic output model, an adaptive fuzzy logic-based interface model, and a model predictive control (MPC) shared controller. The BCI with a probabilistic output model can output all commands in a probabilistic form rather than a specific single command once. The adaptive fuzzy logic-based interface can convert the probabilities into the vehicle’s input signals (including the vehicle acceleration and the increment of steering wheel angle) according to the vehicle state and road information. The MPC shared controller can ensure the control authority of brain-control drivers and reduce drivers’ workload on the premise of maintaining safety. We establish an experimental platform to validate the proposed method by using the intersection selection and obstacle avoidance scenarios with eight subjects. The experimental results show the effectiveness of the proposed method in improving driving performance and decreasing drivers’ workload. This work can contribute to the research and development of BCVs and provide some new insights into the study of intelligent vehicles and human-vehicle integration.

Adaptive control of dual-motor autonomous steering system for intelligent vehicles via Bi-LSTM and fuzzy methods

In order to achieve better trajectory tracking of intelligent vehicle on different road surfaces, which include underling the weather of rain and snow, an adaptive tire cornering stiffness strategy (ACS) and a trajectory tracking autonomous steering control strategy were proposed in this paper. Firstly, the tire–road friction coefficient estimator was designed to obtain the tire–road friction coefficient based on Bi-LSTM neural network, then combining the tire cornering stiffness coefficient by fuzzy controller to obtain the online tire cornering stiffness. Secondly, considering the uncertainty of tire cornering stiffness, a trajectory tracking upper-level controller based on model predictive control (MPC) algorithm was proposed to calculate the optimal front wheel angle. Thirdly, in order to better adapt to various road conditions, a dynamic equivalent stiffness model is proposed to replace the self-aligning torque model during vehicle steering, and a lower-level controller for pinion gear target position tracking was designed based on the sliding mode control (SMC) algorithm. In addition, this paper also designed a dual-motor control strategy, which cooperates with the pinion gear target position tracking lower-level controller to quickly and accurately achieve the target front wheel angle. Finally, the simulation and HiL test results show that the proposed control algorithm greatly improves the trajectory tracking accuracy and stability of the intelligent vehicle on roads with different tire–road friction coefficients.

A Novel Design and Simulation of Adjustable Steering Geometry, Fiyanshu P. Nagrare, Prachi S. Somkuwar, Monika B. Parate,

Designing and Analysing an Electric Kart is a vast platform for the students. So many competitions are used to organize for the students on this platform. It's beneficial to the students to enhance their learning and gain practical knowledge of various systems of Kart like steering system, braking system, transmission system, etc. This thesis presents the effect of multiple parameters on the performance of the steering system of the e-kart. It covers the design and analysis of stresses generated on steering components and to obtain the optimum safe design. The conceptual model of the E-Kart steering system is developed and used to illustrate the purpose of this study. The inner wheel and outer wheel angle of the steering system are studied using the steering equation. The simulation results reveal that the stresses generated on the steering system components are below the permissible stresses to assure that all members are safe for their maximum Stress. Also, several iterations are conducted on meshing to determine its stability, followed by the convergence tool. This research is intended to help undergraduates by giving a neat overview of E-kart's design and Simulation to participate in national competitions. This project work aims to point out the findings and studying on the steering system of the Kart.

Lane-Changing Trajectory Tracking and Simulation of Autonomous Vehicles Based on Model Predictive Control

In order to realize the lane-changing maneuver of Connected Autonomous Vehicles (CAV), a lateral controller based on model predictive control is developed with the three degrees of freedom vehicle dynamic model. Then the controller is synthesized to track the reference trajectory fitted by the quintic Bézier curve. The controller is validated by MATLAB/CarSim under different road adhesion conditions and driving speeds. Results show that for different road adhesion conditions and driving speeds, the controller does not need to adjust the control parameters and can continuously correct the deviation from the expected trajectory. During the tracking process, the front wheel angle, front wheel angle increment, centroid side deflection angle, and front wheel side deflection angle are kept within the limited constraint range. The established control algorithm has good control robustness and tracking driving stability. The research can provide a theoretical basis and technical support for lane-changing safety and control of CAV.

Research on imaging method of driver's attention area based on deep neural network

In the driving process, the driver's visual attention area is of great significance to the research of intelligent driving decision-making behavior and the dynamic research of driving behavior. Traditional driver intention recognition has problems such as large contact interference with wearing equipment, the high false detection rate for drivers wearing glasses and strong light, and unclear extraction of the field of view. We use the driver's field of vision image taken by the dash cam and the corresponding vehicle driving state data (steering wheel angle and vehicle speed). Combined with the interpretability method of the deep neural network, a method of imaging the driver's attention area is proposed. The basic idea of this method is to perform attention imaging analysis on the neural network virtual driver based on the vehicle driving state data, and then infer the visual attention area of the human driver. The results show that this method can realize the reverse reasoning of the driver's intention behavior during driving, image the driver's visual attention area, and provide a theoretical basis for the dynamic analysis of the driver's driving behavior and the further development of traffic safety analysis.

Verification of hardware-in-the-loop test bench for evaluating steering wheel angle sensor performance for steer-by-wire system

Verification of hardware-in-the-loop test bench for evaluating steering wheel angle sensor performance for steer-by-wire system

Electric motor steer-by-wire angle tracking by proportional differential sliding mode controller with disturbance observer

This paper focuses on the front wheel angle tracking control problem of the steer-by-wire system subject to unmodeled dynamics, uncertain parameters, external disturbances and measurement noises. Considering the measurement uncertainties of front wheel angle and angular velocity, a new steering dynamics model with matched and unmatched disturbances is built for control design. An unmatched disturbance observer is designed to attenuate the effects of unmatched measurement uncertainties and further improve the angle tracking precision of steady-state. A matched disturbance observer is developed to cancel the effects of matched disturbances, and to alleviate the chattering of sliding mode control. Based on the outputs of these two disturbance observers and the sliding mode controller (SMC), a proportional differential sliding mode control (pdSMC) approach is developed by a new sliding manifold for angle tracking of the steer-by-wire system. The developed approach has been downloaded into a steering control unit, and tested in real-world conditions using vehicle test bench to fully realize electric motor steering by wire in engineering practice. Experimental results show that compared with the SMC controller, the pdSMC controller reduces the mean absolute error of the front wheel angle by 52% and 58.9% in the steering tests of step response and slalom path, respectively.

Evaluation of the optimal quantity of in-vehicle information icons using a fuzzy synthetic evaluation model in a driving simulator

In-Vehicle Information (IVI) features such as navigation assistance play an important role in the travel of drivers around the world. Frequent use of IVI, however, can easily increase the cognitive load of drivers. The interface design, especially the quantity of icons presented to the driver such as those for navigation, music, and phone calls, has not been fully researched. To determine the optimal number of icons, a systematic evaluation of the IVI Human Machine Interface (HMI) was examined using single-factor and multivariate analytical methods in a driving simulator. When one-way ANOVA was performed, the results showed that the 3-icon design scored best in subjective driver assessment, and the 4-icon design was best in the steering wheel angle. However, when a new method of analyzing the data that enabled a simultaneous accounting of changes observed in the dependent measures, 3 icons had the highest score (that is, revealed the overall best performance). This method is referred to as the fuzzy synthetic evaluation model (FSE). It represents the first use of it in an assessment of the HMI design of IVI. The findings also suggest that FSE will be applicable to various other HMI design problems.

Research on decoupling control for the longitudinal and lateral dynamics of a tractor considering steering delay

To enhance the efficiency of tractor operation, the longitudinal and lateral dynamic control of a self-driving tractor is studied in this paper, and a control system that decouples control of the longitudinal and lateral movement of the tractor is proposed. The trajectory controller calculates the desired speed and desired yaw rate signals of each subsystem, and a PID controller regulates the longitudinal speed of the tractor. A pure pursuit algorithm calculates the desired front wheel angle of the tractor. To decrease the system time delay in the automatic steering system, an automatic steering scheme based on improved Smith predictive control is proposed. Through decoupling control of the longitudinal and lateral controllers, the tractor’s path tracking performance is assured.

A graph method of description of driving behaviour characteristics under the guidance of navigation prompt message

AbstractTo verify whether a graph is suitable for describing driver behaviour performance under the effects of navigation information, this study applies two types of prompt messages: simple and detailed. The simple messages contain only direction instructions, while the detailed messages contain distance, direction, road and lane instructions. A driving simulation experiment was designed to collect the empirical data. Two vehicle operating indicators (velocity and lateral offset), and two driver manoeuvre indicators (accelerator power and steering wheel angle) were selected, and T-test was used to compare the differences of behavioural performance. Driving behaviour graphs were constructed for the two message conditions; their characteristics and similarities were further analysed. Finally, the results of T-test of behavioural performance and similarity results of the driving behaviour graphs were compared. Results indicated that the two different types of prompt messages were associated with significant differences in driving behaviours, which implies that it is feasible to describe the characteristics of driving behaviours guided by navigation information using such graphs. This study provides a new method for systematically exploring the mechanisms affecting drivers’ response to navigation information, and presents a new perspective for the optimisation of navigation information.

Research on Lateral and Longitudinal Coordinated Control of Distributed Driven Driverless Formula Racing Car under High-Speed Tracking Conditions

Aiming at the problem that it is difficult to ensure the trajectory tracking accuracy and driving stability of the distributed driven driverless formula racing car under high-speed tracking conditions, a lateral and longitudinal coordinated control strategy is proposed. Based on the adaptive model predictive control theory, the lateral motion controller is designed, and the prediction time domain of the controller is changed in real time according to the change of vehicle speed. Based on the sliding mode variable structure control theory, a longitudinal motion controller is designed to accurately track the desired vehicle speed. Considering the coupling between the lateral and longitudinal controls, the lateral controller inputs the longitudinal speed and displacement of the vehicle, using the feedback mechanism to update the prediction model in real time, the longitudinal controller takes the front wheel angle as the input, the driving torque is redistributed through the differential drive control, and the lateral and longitudinal coordinated control is carried out to improve the trajectory tracking accuracy and driving stability. The typical working conditions are selected for co-simulation test verification. The results show that the lateral and longitudinal coordinated control strategy can effectively improve the vehicle trajectory tracking control accuracy and driving stability.