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A control strategy based on H∞-type static output feedback combined with dynamic feed-forward is proposed to improve the high-speed lateral performance of an A-double combination vehicle(tractor–se...

The paper introduces a novel modular estimation approach for lateral vehicle and tire dynamics using a simplified vehicle model and a non-linear estimation algorithm. A dynamics-oriented representation of lateral tire forces with a single track lateral vehicle model (STVM) has been introduced. Subsequently, extended Kalman filter (EKF) based distributed observer modules for each dynamical parameter has been designed and combined into a Unified Estimation Scheme (UES). Finally, a linear quadratic regulator (LQR) based Active Front Steering (AFS) control system has been designed using the estimated parameters. The accuracy and computational efficiency of the designed scheme has been analyzed and compared to non-modular UKF, EKF, and Particle Filter (PF) algorithms, through Monte-Carlo Simulations using the CarSim dataset for both high and low [Formula: see text] surfaces, followed by further validation using real-time dataset. The results show that the proposed system significantly improve the accuracy and speed of estimation, as well as stable performance in closed loop control.

This paper proposes a control strategy for the design of an automated steering control for an autonomous electric vehicle. The proposed Active Disturbance Rejection Control (ADRC) with Model Predective Control (MPC) is not only capable of alleviating the disturbance but also shows robustness against structured uncertainties which may arise due to models that represent the vehicle dynamics. Simulations have been carried out to assess the effectiveness of the proposed control strategy. Simulation results show that the proposed scheme is better in terms of tracking performance than MPC and ADRC. The steering control system, with the proposed strategy, can achieve faster response, higher tracking accuracy, and improved robustness performance in dealing with model uncertainties and external disturbances.

Steering ship models are important for the study of autonomous ship manoeuverability and design of ship motion control system. It is always a difficult task to find the mathematical model by first ...

Keeping a vehicle in its current lane without veering into adjacent lanes is important in hard-braking manoeuvres on split- μ roads. Whether the emergency braking is initiated by a human driver or ...

One of the significant challenges in contemporary robotics concerns navigation systems and autonomous robots’ location, being the detection and avoidance of obstacles an essential part of this system. This article proposed Paraconsistent Annotated Evidential Logic Eτ in the control algorithm for a terrestrial robot, and thus contribute with navigation systems, to avoid possible collisions with obstacles. A prototype terrestrial robot was then built with ultrasonic sensors for obstacle detection and a servomotor in steering control. Several performance tests were performed. It was noticed that by shortening the perception distances of the sensors, the performance of the robot’s displacement proved to be satisfactory.

Differential steering is an emerging steering concept based on traction force differences between left and right sides of vehicles equipped with independently actuated wheels. Actually, this concep...

While current light-driven microswimmers require high-intensity light, UV light, or toxic fuels to propel them, powering them with low-intensity UV-free visible light without fuels is essential to enable their potential high-impact applications. Therefore, in this study, a new material for light-driven microswimmers in the form of CoO is introduced. Janus CoO-TiO2 microswimmers powered with low-intensity, UV-free visible light inside water without using any toxic fuels like H2O2 is proposed. The microswimmers show propulsion under full spectrum of visible light with 17 times lower intensity than the mean solar intensity. They propel by breaking down water into oxygen and oxide radicals, which enables their potential applications for photocatalysis and drug delivery. The microswimmers are multiwavelength responsive, from the ultraviolet to the infrared region. The direction of swimming changes with the change in the illumination from the visible to UV light. In addition to being responsive, they are wavelength steerable and exhibit inherent magnetic properties enabling magnetic steering control of the CoO-TiO2 microswimmers. Thus, these microswimmers, which are propelled under low-intensity visible light, have direction-changing capability using light of different wavelengths, and have steering control capability by external magnetic fields, could be used in future potential applications, such as active and local cargo delivery, active photocatalysis, and hydrogen evolution.

Automated steering technology offers significant benefits to the safety and efficiency of vehicles, but desire to keep the human driver in the loop requires better understanding of the interaction between driver and vehicle. An existing noncooperative-game-theoretic framework for modelling such interaction is revisited in this paper, leading to the development of two alternative driver steering control models. Both models bears Nash-equilibrium properties but involve different assumptions about driver steering control behaviour. A simulation study is performed to demonstrate the difference between the two driver models in terms of their simulated driver steering angles and vehicle lateral motions. An experiment using a fixed-base driving simulator is conducted to measure six test subjects’ steering control behaviour in response to the lane- change manoeuvres generated by an automated steering control system. The two proposed driver models are fitted to the measured driver steering angle data to investigate their capabilities for representing individual driver’s steering control behaviour. Key model parameters are identified following a system identification procedure. It is found that the two driver models have equivalent capability for capturing the trend exhibited in all the six test subjects’ steering angle histories, but less good at reproducing the severe overshoot or oscillation involved in two subjects’ measured steering angles. It is found that the inclusion of an arm neuromuscular system model can improve the performance of the proposed driver models on representing human driver steering control behaviour.

Sleepiness remains a major contributor to road crashes. Driver monitoring systems identify early signs of sleepiness and alert drivers, using real-time analysis of eyelid movements, EEG activity, and steering control. Other vehicle adaptations warn drivers of lane departures or collision hazards, with higher vehicle automation actively taking over vehicle control to prevent run off the road incidents and institute emergency braking. Similarly, road adaptations warn drivers (rumble strips) or mitigate crash severity (barriers). Infrastructure to encourage drivers to use countermeasures, such as rest stops for napping, is also important. The effectiveness of adaptations varies for different road users.

The Steer-by-Wire (SbW) system is probably the most significant innovation among drive-by-Wire technologies in the automotive industry. Without the mechanical link, the most challenging issue is to control the wheels to closely follow the driver’s command. To improve the robustness of the model predictive control (MPC) in the presence of modeling uncertainties and disturbances in the steering control processes, a sliding mode predictive tracking control (SMPC) strategy for a SbW system with uncertain dynamics is proposed. The simulation and experimental results demonstrate that the performance of the proposed SMPC tracking controller is superior to both SMC and MPC methods for the steering angle tracking task.

The developments in Steer-by-Wire Systems allow for the re-thinking of the commonly known steering system meaning both the development of new controls replacing the steering wheel as well as the design of the feedback corresponding to inform the human driver about the vehicle state. By conducting a user study in a high fidelity driving simulator this paper investigates the quality of vehicle stabilization performed via Active Drivesticks as alternative controls in comparison to the conventional steering wheel in a high-speed scenario. On a subjective level, assessed driving pleasure, experienced comfort and usability were high. Still, the subjective evaluation of safety feeling, experienced stress as well as necessary effort, was in favor of the conventional steering wheel, which subjects have been using over many years of driving. In addition, objective driving state variables that were measured to assess the participants’ performance in controlling the target path show a slight advantage for the classical steering control. In conclusion, it could be shown that despite the difference in experience and training with the different controls, the vehicle stabilization performed via Drivesticks is almost as good as the one performed via the conventional steering wheel in a high-speed driving scenario.

Skillful behavior requires the anticipation of future action requirements. This is particularly true during high-speed locomotor steering where solely detecting and correcting current error is insufficient to produce smooth and accurate trajectories. Anticipating future steering requirements could be supported using "model-free" prospective signals from the scene ahead or might rely instead on model-based predictive control solutions. The present study generated conditions whereby the future steering trajectory was specified using a breadcrumb trail of waypoints, placed at regular intervals on the ground to create a predictable course (a repeated series of identical "S-bends"). The steering trajectories and gaze behavior relative to each waypoint were recorded for each participant (N = 16). To investigate the extent to which drivers predicted the location of future waypoints, "gaps" were included (20% of waypoints) whereby the next waypoint in the sequence did not appear. Gap location was varied relative to the S-bend inflection point to manipulate the chances that the next waypoint indicated a change in direction of the bend. Gaze patterns did indeed change according to gap location, suggesting that participants were sensitive to the underlying structure of the course and were predicting the future waypoint locations. The results demonstrate that gaze and steering both rely upon anticipation of the future path consistent with some form of internal model.

In a fully autonomous driving situation, passengers hand over the steering control to a highly automated system. Autonomous driving behaviour may lead to confusion and negative user experience. When establishing such new technology, the user’s acceptance and understanding are crucial factors regarding success and failure. Using a driving simulator and a mobile application, we evaluated if system transparency during and after the interaction can increase the user experience and subjective feeling of safety and control. We contribute an initial guideline for autonomous driving experience design, bringing together the areas of user experience, explainable artificial intelligence and autonomous driving. The AVAM questionnaire, UEQ-S and interviews show that explanations during or after the ride help turn a negative user experience into a neutral one, which might be due to the increased feeling of control. However, we did not detect an effect for combining explanations during and after the ride.

In this paper, the authors suggest practical techniques and machine learning algorithms to develop an intelligent autopilot steering control system. The system consists of a module with the neural network models of sea-craft movement patterns, a module with a neural network classifier, and a module containing an optimizer that calculates the best fuzzy controller operating. The paper presents the findings of reviewing neural network paths for six types of marine vessels. It emphasizes that it is necessary to develop an original neural network model on different maritime navigation conditions. Therefore, one requires a neural network classifier that can select an optimal neural network model of a marine vessel movement patterns under certain waterway navigation conditions. The authors figure out the neural network models of sea vessel movement patterns using a certified IC-2005 signal simulator for an autopilot. The researchers apply machine learning algorithms to generate parameters of neural network models and classify ship movement patterns. The authors also carry out a computer simulation of sea vessels with different speed and on various navigation conditions to test if it is easy to use the neural network classifier reported. The paper covers the correspondence of the mean square root error of learning to navigation conditions for various sea vessels, including a trawler with a slow response.

Inter-roll thrust force due to roll misalignment in a 4-high strip rolling mill affects the difference in roll forces measured by load cell on the work side and that on the drive side. Since the roll force difference is utilized for strip steering control, in which roll gap tilting control is done assuming the roll force difference reflects strip off-centering behavior, the inter-roll thrust force gives a serious disturbance to the steering control and may cause troubles like tail crash. In this paper, an inter-roll thrust force decoupling method for the steering control is proposed. The method utilizes measured thrust counter forces acting against work roll chocks to obtain effective roll force difference for the steering control. Validity of the decoupling method is confirmed using a laboratory 4-high mill which can control cross angles between rolls and measure the work roll thrust counter forces.

In this paper we implement and evaluate two ways of controlling the steering angle of an autonomous vehicle, PID control with manual tuning followed by gradient descent algorithm tuning-which is able to enhance the performance through self-adjusting the controller parameters-and using supervised machine learning through the end-to-end deep learning for self-driving car which implement Convolutional Neural Network (CNN) to predict the steering angle for a given instance of a track. The verification testing went through two phases: software simulation using python for first run testing and C++ for simulation followed by track testing with a vehicle prototype. The proposed PID steering control system exhibits more stable steering commands-less oscillations-which makes it better than CNN Behavioral cloning control model. However, CNN Behavioral Cloning model may show better results after many several hours of training.

Understanding the relationship between driving behavior and visual information is an important issue in order to understand driving behavior holistically. In this study, we constructed a driver model that reproduces the driver’s steering behavior from visual information based on the Convolutional Neural Network (CNN) with human physical characteristics. We obtained the driving behavior in a simulator study to train the proposed CNN model. Which region in the visual field influencing drivers’ steering behavior was analyzed using the results of the feature maps generated by the trained CNN model and the driver’s gaze behavior. The results indicate that the drivers perform steering action using the information within 20 degrees from the gaze point.

This paper aims to investigate the drivetrain load response caused by induction and wake steering control on two floating wind turbines (FWTs) in a wind farm. In this study, two DTU 10 MW turbines, supported on the nautilus floater, are modelled using FAST.Farm. The downstream turbine is placed at the distance of seven rotor diameters (D) from the upstream turbine in the positive wind direction. Partial wake shading is considered for wake steering control and full wake shading is considered for induction control. An ambient wind speed of 8m/s is used and a representative sea state is selected. The test cases are defined based on different blade pitch and yaw angles of the upstream turbine. Power generation of the offshore wind farm is studied under different test cases. A decouped analysis approach is used to investigate drivetrain response. Global responses are obtained from FAST.Farm. These loads are used as input of the 10 MW wind turbine drivetrain model for the gears and bearings load response analysis. Results show that both induction and wake steering control lead to a limited increase in power generation of the wind farm. Additionally, both control methods affect the drivetrain response statistics, while the features are different. This study facilitates a better understanding on drivetrain dynamic behaviour in a wind farm perspective, which serves as a reference for the wind farm optimizaton in the future. Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.

Previous studies argued that body turns are executed in an ordered sequence: the eyes turn first, followed by the head and then by the trunk. The purpose of this study was to find out whether this sequence holds even if body turns are not explicitly instructed, but nevertheless are necessary to reach an instructed distal goal. We asked participants to shop for grocery products in a simulated supermarket. To retrieve each product, they had to walk down and aisle, and then turn left or right into a corridor that led towards the target shelf. The need to make a turn was never mentioned by the experimenter, but it nevertheless was required in order to approach the target shelf. Main variables of interest were the delay between eye and head turns towards the target shelf, as well as the delay between head and trunk turns towards the target shelf. We found that both delays were consistently positive, and that their magnitude was near the top of the range reported in literature. We conclude that the ordered sequence of eye - then head - then trunk turns can be observed not only with a proximal, but also with a distal goal.

Recent research efforts on automated driving reveal the need to address driver’s interaction with vehicle automation. This paper focuses on the design of an override mode for automated driving systems, a topic little studied in literature. In this paper, a shared control framework for driver’s override of automatic steering control is proposed. This framework formulates the control transfer between the driver and the system as a constrained optimization problem which is solved online by a model predictive controller. At a higher level in the framework, the system detects driver’s lane-change intention so that the low-level controller can actively assist the driver in a lane-change maneuver. The whole framework was evaluated in a user test based on a driving simulator. The test results show that the proposed shared control framework has the advantage of allowing the driver to regain control with ease while ensuring the smoothness of control transition.

Arguments are made in favor of the use of transporters intended for transporting large-sized indivisible heavy objects, which are composed of separate self-propelled modules. The basic principles of operating such a loosely coupled vehicle using modern information technologies are given in the context of steering control. Some versions of transporters are presented as are schemes of taxi patterns they can perform. The operation of a three-level movement control system of a loosely coupled transporter is considered including its functional arrangement.

This paper presents a new steer-by-wire concept using an all-wheel drive vehicle layout with in-wheel motors while completely omitting the application of any dedicated steering device. Steering is ...

Publication électronique en juillet 2017; publication au format papier en avril 2018. L'article final contient 17 pages.; International audience; In recent years there has been an increasing interest in improving vehicle characteristics through the use of Vehicle Control Systems (VCS). In particular, VCS for the lateral (steering) and longitudinal (velocity) dynamics are used to improve the handling properties of a vehicle. Nonetheless, the introduction of the additional elements required for implementing these control systems also increases the possibility of faults. This problem can be mitigated by using Fault Tolerant Control(FTC) systems. The most common approach for steering FTC design is based on the use of a linear Bicycle Model (BM). Using this model decentralized steering controllers can be designed. However, the BM lacks significant lateral and longitudinal cross-coupling dynamics. In fact, the steering and velocity control problem could be viewed as a multivariable cross-coupled problem. In this article VCS for the steering and velocity are designed. The resulting controllers are decentralized and capable of practically eliminating the cross-coupling. A further problem, which has not been widely reported, is the propagation of the failure of one subsystem to other subsystems. It is shown that when the Velocity Control System (VelCS) fails, then the steering subsystem has a degraded performance due to cross-coupling. The main contribution of this article consists in showing that it is possible to detect and accommodate a failure of the VelCS within the steering control system, i.e. without requiring communication among subsystems. This enables a fully independent operation even if faults occur, that is a Decentralized Fault-Tolerant Control Scheme.

In the past decade, several publications have shown that it is advisable to design an advanced driver assistance system using a shared control structure. This paper is concerned with the modelling ...

The article offers an algorithm for the operation of the steering and active drive control system of a saddle-type tractor – trailer combination. The algorithm allows reducing the width of the cornering corridor when maneuvering at low speeds. In the combination vehicle under study, all the wheels of the tractor and semi-trailer are driving and steered. The algorithm of the steering control system takes into account the current values of the folding angle and speed of the combination vehicle. The active drive control is based on the analysis of the forces in the coupling device. The efficiency and effectiveness of the proposed algorithm has been proved using computational experiments.

To avoid crashes caused by driver error in avoiding obstacles, a driver-centered steering assist controller with an adaptive authority allocation system is proposed for cooperative control purposes. To begin with, a concept of space collision risk (SCR) is introduced creatively for assessing the vehicle's safety status in which the relative distance and the relative angle between the vehicle and the obstacle are taken into consideration. Meanwhile, the SCR-based authority allocation system is addressed to allocate steering authorities between the human driver and the assist controller adaptively to reduce SCR to zero as soon as possible. After that, an autonomous steering controller based on the model predictive control (MPC) technique and the artificial potential field (APF) method, considering not only the vehicle stability constraints but also roads and obstacles constraints, is developed to aid the human driver when necessary. In the end, the proposed algorithm is simulated in the CarSim-Simulink co-simulation platform under a series of typical scenarios, which shows the feasibility and effectiveness of the presented shared steering control method.

This paper presents a new application of active rear-wheel steering control to improve the lateral vehicle behaviour. In the state of the art, yaw or lateral velocity is used as control variable th...

This paper addresses the problem of trajectory tracking and control of a low-cost flying agent, equipped with a microcontroller for real-time navigation. A flying agent (aircraft-type quadcopter) has been designed and developed for steering control along a planned trajectory. A novel control scheme is developed using MATLAB/Simulink to ensure the right balance among pitch, roll and yaw of the flying agent wings. The proposed control scheme utilises a PID controller which regulates the error dynamics to stabilise the navigation. Various trajectories have been generated and verified through simulation studies as well as tested in a real model. The real-time experiments verify the manoeuvrability of the controller, which also checks the compatibility with hardware interfaces.

Secure Quality of Service (QoS) directing calculations are a crucial piece of remote systems that plan to give administrations QoS and security ensures. In vehicular specially appointed systems (VANETs), vehicles perform directing capacities, and in the meantime go about as end-frameworks subsequently steering control messages are transmitted unprotected over remote channels.

This paper deals with Global Chassis Control (GCC) of ground vehicles. It focuses on the coordination of suspensions and steering/braking vehicle controllers based on the interaction between the vertical and lateral behaviors of the vehicle. Indeed, the roll motion of the car can generate increasing load transfers that affect considerably the suspension system and vehicle stability. The load transfers can be described using the lateral acceleration. Then, the coordination is highlighted, in this work, through the relationship between the suspension behavior and the lateral acceleration in the framework of the Linear Paramter Varying (LPV) approach. The proposed control law is designed in hierarchical way to improve the overall dynamics of the vehicle. This global control strategy includes two types controllers. The first one is the longitudinal/lateral nonlinear Flatness controller. Based on the adequate choice of the flat outputs, the flatness proof of a 3DoF two wheels nonlinear vehicle model has been established. Then, the combined longitudinal and lateral vehicle control is designed. The algebraic estimation techniques have been used in order to have an accuracy estimation of the derivatives and filtering of the reference flat outputs. Such control strategy is developed in order to cope with coupled driving maneuvers like obstacle avoidance via steering control and stop-and-go control via braking or driving wheel torque. The second part of the proposed strategy consists of the LP V /H∞ suspension controller. This controller uses the lateral acceleration as a varying parameter to take into account the load transfers that affects directly the suspension system and therefore to achieve the desired performance. The coordination between the vehicle vertical and lateral dynamics is highlighted in this study, and the LP V /H∞ framework ensures a specific collaborative coordination between the suspension and the steering/braking controllers. Simulations on a complex full vehicle model have been validated using experimental data obtained on-board vehicle, with an identification procedure on a real Renault Megane Coupe.

This paper illustrates the potential of a gyroscopic stabiliser for the stabilisation of single-track vehicles, at low and high speed as well as during braking. Alternative systems are considered, including single and twin counter-rotating gyroscopes, spinning and precessing with respect to different axes, either freely (passive stabilisers) or in a controlled way (active stabilisers). A suitable mathematical model has been developed and stability has been investigated both by eigenvalue calculation and time domain simulations. It has been found that the most effective configuration is one where the gyroscope(s) spin with respect to an axis parallel to the wheels' spin axis and swing with respect to the vehicle yaw axis. Passive systems may effectively stabilise both weave and wobble at medium and high speed, but cannot stabilise the vehicle at low and zero speed. On the contrary, actively controlled gyroscopes are capable of stabilising the vehicle in its whole range of operating speed, as well as during braking. The alteration of the original vehicle handling characteristics is negligible when active counter-rotating gyroscopes are used, and still acceptable if a single gyroscope is adopted instead.

Within the Shift2Rail project Run2Rail, an innovative single axle running gear with only one suspension step is proposed. A composite material frame shall be used both as structural and as suspensi ...

A practical approach on remote monitoring of axle loads for heavy commercial vehicles with mechanical suspension was presented. At present, there are motor vehicles act implemented in most of the countries against operating commercial with overload. The major root cause for many truck accidents is overloading. When a truck is loaded more than its rated load, it will increase the risk of affecting vehicle stability while driving. Due to overloading of trucks, the driver will loss the steering control during turning and braking, and this loss of control leads to major accidents on roads. In some cases, even when the cargo load is within the rated load, any one of the individual vehicle axle will get overloaded due to non-uniform distribution of payload. Uniform distribution of loads is important for safe vehicle operation. Overloading of trucks will reduce the life of tires and parts related to suspension from their actual service life, which leads to increase in service cost for the vehicle. The best way to overcome these problem is to monitor each axle loads by avoiding traditional method of weigh bridge measurement. Axle load monitoring system presented in this paper works under the technique of capturing axle load with respect to the deflection of mechanical spring suspension. In addition, the system supports remote data capturing using telematics device. This will avoid the physical presence for measuring.

End-to-end deep learning has gained considerable interests in autonomous driving vehicles in both academic and industrial fields, especially in decision making process. One critical issue in decision making process of autonomous driving vehicles is steering control. Researchers has already trained different artificial neural networks to predict steering angle with front-facing camera data stream. However, existing end-to-end methods only consider the spatiotemporal relation on a single layer and lack the ability of extracting future spatiotemporal information. In this paper, we propose an end-to-end driving model based on Convolutional Long Short-Term Memory (Conv-LSTM) neural network with a Multi-scale Spatiotemporal Integration (MSI) module, which aiming to encode the spatiotemporal information from different scales for steering angle prediction. Moreover, we employ future sequential information to enhance spatiotemporal features of the end-to-end driving model. We demonstrate the efficiency of proposed end-to-end driving model on the public Udacity dataset with comparison of some existing methods. Experimental results show that the proposed model has better performances than other existing methods, especially in some complex scenarios. Furthermore, we evaluate the proposed driving model on a real-time autonomous vehicle, and results show that the proposed driving model is able to predict the steering angle with high accuracy compared to skilled human driver.

In this paper, a vision-based path-tracking control strategy using four-wheel steering (4WS) is experimentally investigated via an automated ground vehicle (AGV). A low-cost monocular camera is used to continuously perceive the upcoming lane boundaries via capturing the preview road image frames. Based on the applied image processing algorithms, the vehicle lateral offset error with respect to the road center line and the heading angle error with respect to the road curvature are calculated in real time for the control purpose. The 4WS path-tracking controller is designed to minimize the two path-tracking errors of the AGV. The AGV with the 4WS system is utilized to perform the experimental tests on road to validate the path-tracking control design. For comparison, the road test is also conducted for the path-tracking control with only the front wheel steering. The experimental results show that the proposed 4WS is able to achieve better path-tracking performance.

One of the biggest financial loss due to workplace accidents at coal mining companies is caused by dump truck drivers' sleepiness during hauling activities, resulting in loss of steering control. Drowsiness is an indicator of occupational fatigue as a body reaction to prevent another serious illness. An efficient fatigue management policy is needed to reduce the number of coal mining workplace accidents. Health and safety department should assess the driver's physical condition is fit to work before hauling activity is started as a preventive program. This study was analyzing the physical factors that significantly influence the dump truck drivers occupational fatigue. A mathematical model to assess the probability of whether dump truck drivers are suspected of having occupational fatigue or not is the result of this study. This study used logistic regression statistical method to give an accurate occupational fatigue calculation which can be implemented in Indonesia coal mining companies.

There are so many reasons for vehicle accidents and the first reason amongst is less alertness of the driver; this may be because of driver’s fatigue or drowsiness. With the introduction of the automatic driver assistance system ADAS, there is possibility to avoid the accidental damage which is happening due to the said reason. To develop such system, two problems are required to be solved. One is prediction of the drowsiness and the other one is controlling the movement of vehicle. The theme of the paper; is to review various control algorithms used to develop shared steering control systems appropriate to implement the ADAS. For this a vehicle model is developed and described to solve the problem with model based development technique. The vehicle model developed is a two-dimensional 2D and 3D mathematical state-space model of laterally moving vehicle which is most suitable to design a shared steering control system. Finally the response of the vehicle state-space model, simulated for unit pulse input, is analyzed with MATLAB software.

International audience; When negotiating bends car drivers perform gaze polling: their gaze shifts between guiding fixations (GFs; gaze directed 1-2 s ahead) and look-ahead fixations (LAFs; longer time headway). How might this behavior change in autonomous vehicles where the need for constant active visual guidance is removed? In this driving simulator study, we analyzed this gaze behavior both when the driver was in charge of steering or when steering was delegated to automation, separately for bend approach (straight line) and the entry of the bend (turn), and at various speeds. The analysis of gaze distributions relative to bend sections and driving conditions indicate that visual anticipation (through LAFs) is most prominent before entering the bend. Passive driving increased the proportion of LAFs with a concomitant decrease of GFs, and increased the gaze polling frequency. Gaze polling frequency also increased at higher speeds, in particular during the bend approach when steering was not performed. LAFs encompassed a wide range of eccentricities. To account for this heterogeneity two sub-categories serving distinct information requirements are proposed: mid-eccentricity LAFs could be more useful for anticipatory planning of steering actions, and far-eccentricity LAFs for monitoring potential hazards. The results support the idea that gaze and steering coordination may be strongly impacted in autonomous vehicles.

Due to various defects of driverless technology, driver-automation cooperative steering control system is still an accepted method in transportation system. In this paper, a cooperative control system for obstacle avoidance is proposed to combine the advantages of driver and automatic controller. With the system, a MPC-based automatic controller and an optimal preview acceleration driver model are employed for avoiding obstacles. Besides, an authority allocation mechanism based on level of lane departure and space collision risk is devised to allocate the weights between driver and controller based on the real-time status of vehicle, in which the driver’s command and the automatic controller’s one will be balanced by weighted summation. Finally, the effectiveness of proposed cooperative control system is verified by CarSim-Simulink cosimulation platform.

In recent years, lidar has been used as primary sensors for self-driving cars, however, due to their high expense, it becomes infeasible for mass production. Hence, we present the working of a self-driving car prototype that relies upon a cheaper alternative, viz. cameras. The primary objective of our prototype is to navigate safely, quickly, efficiently and comfortably through our virtual environment using computer vision. We have performed detection of lanes, traffic cars, obstacles, signals, etc. and have used the concept of stereo vision for depth calculation. Trajectory planning and steering control have also been implemented. Experimental results show that camera-based self-driving cars are viable and thus our paper can provide a foundation for all future real-world implementations.

Electric vehicles are the new target for researchers from many fields to find new innovation to reduce the usage of fossil fuel which currently almost finished. This paper presents a strategy of independent speed steering control for four in-wheel direct drive type by using brushless direct current (BLDC) motors. The hybrid Fuzzy-PI speed controller has been proposed to control the speed of each motor. Hysteresis current controller is introduced to generate the switching signal for each switch in inverter. Ackermann-Jeantand steering model is combined to determine the reference speed each wheel of vehicle during turn in a curve road. By using GUI's interface controlling in MATLAB/SIMULINK, the results of simulation each motor are demonstrated. Four different cases are simulated and given satisfy performance.

Many automation applications in the agriculture industry focus on navigation or steering control. But there are few studies on functional controls such as header automation. This paper introduces a video-based prediction system for header-height control of a combine harvester. To achieve this goal, we propose a lighting-invariant spatial segmentation method to locate the field region. Crop presence detection is performed by training a classifier on texture features and the percentage of crops in the field can be estimated. Then the time to lift the header is predicted based on observing the trend of crop presence. The framework is tested on both bean and wheat harvesting video sequences and the decreasing crop percentage can be successfully estimated.

To avoid casualties and economic loss caused by vehicle yawing motion during the tire blowout, first, by changing several key parameters of the characteristics, this article uses CarSim software and MATLAB/Simulink to establish a vehicle model of tire blowout based on the UniTire model. This model is implemented to simulate tire blowout caused by the change of the vehicle motion state. Second, considering the driver error and radical-operated steering wheel after tire blowout leads to runaway car problems. This article takes the target trajectory and actual trajectory of error and error rate as the system input; an adaptive fuzzy proportional–integral–derivative controller is designed to determine the vehicle steering wheel angle during the tire blowout and replace the driver to control the direction of the vehicle. The results indicate that the designed controller is capable of ensuring the vehicle constancy and keeping the vehicle on the original track.

This study aimed to dynamically analyze a simulated bimodal tram by determining the motion equations for computer-aided analysis with Newton-Euler equations of motion, which algebraically represent motion. We utilized the multi-body dynamics analysis program, MSC.Adams, to evaluate the equations of motion, running stability, curving performance, and ride comfort of the simulated bimodal tram in comparison with single fleet and double fleet trams. For the double fleet tram, the characteristic equation was deduced to control the all-wheel steering mechanism by independently moving all the wheels characterizing an articulated vehicle. Our study aimed to design a double fleet bimodal tram. We confirmed that domestic and international standards were satisfied via the design and analysis of the simulation. Steering control equations are the significant preliminary data for coupling device research and development for double fleet bimodal trams.

Taking the steering performance of all-wheel steering system for the three-wheel forklift as the research object, based on a 2-dof model of three-wheel steer-by-wire system, two kinds of control strategies are proposed in this paper, one with front and rear isometric reverse rotation, and the other with yaw rate feedback. According to actual data of the forklift TFC20, the simulation analyses of the steering performance of all-wheel steering system for the three-wheel forklift is given. Compared with the conventional steering control method, the simulation results show that the first control strategy improved the steering flexibility, and the second control strategy improved the steering stability.