Journal articles: 'Autonomous steering'

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Mohamad, Amir Ashraf, Fadhlan Hafizhelmi Kamaru Zaman, and Fazlina Ahmat Ruslan. "Improving steering convergence in autonomous vehicle steering control." Indonesian Journal of Electrical Engineering and Computer Science 13, no. 1 (January 1, 2019): 279. http://dx.doi.org/10.11591/ijeecs.v13.i1.pp279-285.

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<p>Steering control is a critical design element in autonomous vehicle development since it will determine whether the vehicle can navigate safely or not. For the prototype of UiTM Autonomous Vehicle 0 (UiTM AV0), Vexta motor is used to control the steering whereas Pulse Width Modulation (PWM) signal is responsible to drive the motor. However, by using PWM signal it is difficult to converge to the desired steering angle and furthermore time taken for steering angle to converge is much longer. Thus, Proportional Integral Derivative (PID) has been introduced in this autonomous vehicle steering controller to improve the convergence of the steering. Meanwhile a microcontroller was used to control the Vexta Motor direction and perform the calculation of the desired steering angle. Simulation results showed PID controller showed better time taken and preicison of successful convergence of the desired steering angle compared to the PWM controller. Analysis results showed that PID controller significantly reduce the overshooting of steering angle and significantly improve the time taken for convergence by up to 37 seconds faster than PWM controller in UiTM AV0.</p>

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Pushpakanth, Abhishek, and Mangesh N. Dhavalikar. "Development of Steering Control System for Autonomous Vehicle." International Journal of Recent Technology and Engineering (IJRTE) 11, no. 2 (July 30, 2022): 50–53. http://dx.doi.org/10.35940/ijrte.b7105.0711222.

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Automation can help us to reduce the number of crashes on our roads. Through research it is identified that 94 percent of the accidents that occur are because of driver behavior or error as a factor and self-driving vehicles can help reduce driver error. High levels of automation have the potential to reduce risky and dangerous driver behavior and prevent accidents. The main aim is to convert the manual operated steering of the vehicle into fully autonomous steering. The objective of Steering Control System is to control the vehicle’s steering while the vehicle is in motion and also to take accurate decisions while making a turn from the given inputs. The main purpose to develop a steering system for the autonomous vehicle is to replace the manual steering of the vehicle into driverless steering. The steering control is responsible for the vehicle’s steering i.e., at what desired angle the vehicle need to turn. A PID (Proportional Integral Derivative) controller and an encoder is basically used to control the system based on the necessary conditions and requirement’s. For the vehicle’s steering the encoder is used to generate pulses when the steering wheel is turned so that those pulse values can be sent to the DC Motor which is attached to the front axle which is responsible for the vehicle to turn. This autonomous vehicle is a Level-4 automation system and the benefit of this automation is that the vehicle can be even operated in manual mode whenever it’s necessary.

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Shi, Haozhe, Guoqing Geng, Xing Xu, Ju Xie, and Shenguang He. "Path Tracking Control of Intelligent Vehicles Considering Multi-Nonlinear Characteristics for Dual-Motor Autonomous Steering System." Actuators 12, no. 3 (February 23, 2023): 97. http://dx.doi.org/10.3390/act12030097.

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In the path tracking control of intelligent vehicles, the traditional linear control method is prone to high tracking errors for uncertain parameters of the steering transmission system and road conditions. Therefore, considering the mechanical friction in the dual-motor autonomous steering system and the nonlinearity of tires, this paper proposes a path tracking control strategy of intelligent vehicles for the dual-motor autonomous steering system that considers nonlinear characteristics. First, a dual-motor autonomous steering system considering mechanical friction and the variation of tire cornering stiffness under different tire–road friction coefficients was established based on the structure of an autonomous steering system. Second, a tire–road friction coefficient estimator was designed based on a PSO-LSTM neural network. The tire cornering stiffness under different tire–road friction coefficients was estimated through the recursive least-square algorithm. Then, the control strategy of the dual-motor autonomous steering system was designed by combining the LQR path tracking controller with the adaptive sliding mode control strategy based on field-oriented control. Here, mechanical friction and the variation of tire cornering stiffness were considered. Finally, simulation and HiL tests validated the method proposed in this paper. The results show that the proposed control strategy significantly improves the tracking accuracy and performance of the dual-motor autonomous steering system for intelligent vehicles.

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INOUE, Keiichi. "STEERING CONTROL SYSTEM FOR AUTONOMOUS TRACTOR." Proceedings of the JFPS International Symposium on Fluid Power 2008, no. 7-1 (2008): 53–58. http://dx.doi.org/10.5739/isfp.2008.53.

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Eidehall, Andreas, Jochen Pohl, Fredrik Gustafsson, and Jonas Ekmark. "Toward Autonomous Collision Avoidance by Steering." IEEE Transactions on Intelligent Transportation Systems 8, no. 1 (March 2007): 84–94. http://dx.doi.org/10.1109/tits.2006.888606.

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TSUGAWA, Sadayuki, and Satoshi MURATA. "Steering Control Algorithm for Autonomous Vehicle." Transactions of the Institute of Systems, Control and Information Engineers 2, no. 10 (1989): 360–62. http://dx.doi.org/10.5687/iscie.2.360.

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Liu, Runqiao, Minxiang Wei, and Nan Sang. "Emergency obstacle avoidance trajectory tracking control based on active disturbance rejection for autonomous vehicles." International Journal of Advanced Robotic Systems 17, no. 3 (May 1, 2020): 172988142092110. http://dx.doi.org/10.1177/1729881420921105.

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To solve the problem of understeer and oversteer for autonomous vehicle under high-speed emergency obstacle avoidance conditions, considering the effect of steering angular frequency and vehicle speed on yaw rate for four-wheel steering vehicles in the frequency domain, a feed-forward controller for four-wheel steering autonomous vehicles that tracks the desired yaw rate is proposed. Furthermore, the steering sensitivity coefficient of the vehicle is compensated linearly with the change in the steering angular frequency and vehicle speed. In addition, to minimize the tracking errors caused by vehicle nonlinearity and external disturbances, an active disturbance rejection control feedback controller that tracks the desired lateral displacement and desired yaw angle is designed. Finally, CarSim® obstacle avoidance simulation results show that an autonomous vehicle with the four-wheel steering path tracking controller consisting of feed-forward control and feedback control could not only improve the tire lateral forces but also reduce tail flicking (oversteer) and pushing ahead (understeer) under high-speed emergency obstacle avoidance conditions.

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Arifin, Bustanul, Bhakti Yudho Suprapto, Sri Arttini Dwi Prasetyowati, and Zainuddin Nawawi. "Steering Control in Electric Power Steering Autonomous Vehicle Using Type-2 Fuzzy Logic Control and PI Control." World Electric Vehicle Journal 13, no. 3 (March 17, 2022): 53. http://dx.doi.org/10.3390/wevj13030053.

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The steering system in autonomous vehicles is an essential issue that must be addressed. Appropriate control will result in a smooth and risk-free steering system. Compared to other types of controls, type-2 fuzzy logic control has the advantage of dealing with uncertain inputs, which are common in autonomous vehicles. This paper proposes a novel method for the steering control of autonomous vehicles based on type-2 fuzzy logic control combined with PI control. The primary control, type-2 fuzzy logic control, has three inputs—distance, navigation, and speed. The fuzzy system’s output is the steering angle value. This was used as input for the secondary control, PI control. This control is in charge of adjusting the motor’s position as a manifestation of the steering angle. The study results applied to the EPS system of autonomous vehicles revealed that type-2 fuzzy logic control and PI control produced better and smoother control than type-1 fuzzy logic control and PI. The slightest disturbance in the type-1 fuzzy logic control showed a significant change in steering, while this did not occur in the type-2 fuzzy logic control. The results indicate that type-2 fuzzy logic control and PI control could be used for autonomous vehicles by maintaining the comfort and safety of the users.

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Li, Guo Qiang, and Xing Ye Wang. "Research on Electronic Pneumatic Steering and Braking Control Technology for Autonomous Tracked Vehicles." Applied Mechanics and Materials 577 (July 2014): 359–63. http://dx.doi.org/10.4028/www.scientific.net/amm.577.359.

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To realize the autonomous driving of a certain tracked vehicle, the paper has a research on its steering and braking control technology. According to the steering and braking device’s structure and work principle on the original vehicle, the paper design an electronic pneumatic steering and braking control system before analyzing the design request of the system and introduce the system’s work principle. Applying this system to the original vehicle’s autonomous transformation, a test was conducted on the vehicle, the test prove that the electronic pneumatic steering and braking control system can well satisfied the tracked vehicles’ request of steering and braking.

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Leng, Bo, Yehan Jiang, Yize Yu, Lu Xiong, and Zhuoping Yu. "Distributed drive electric autonomous vehicle steering angle control based on active disturbance rejection control." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 235, no. 1 (August 6, 2020): 128–42. http://dx.doi.org/10.1177/0954407020944288.

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Based on active disturbance rejection control technique and characteristics of electric power steering, a steering angle tracking controller is designed, which consists of an aligning moment estimator to deal with modeling error and nonlinearity of electric power steering. The aligning moment estimator is based on an extended state observer and takes steering system friction and differential drive steering torque, which is a unique phenomenon in a distributed drive electric vehicle, into consideration. According to the estimated aligning moment and tracking differentiator, the steering angle tracking controller is designed based on a nonlinear state feedback control and feedforward compensation control laws. Results of various simulations and experiments, including pivot steering, step input steering, and sinusoidal input steering, show that the proposed controller has good performance in tracking reference steering angle and is convenient to implement. With the aligning moment estimator, the proposed controller shows better results in comparative experiments than a conditional integral-based steering angle tracking controller.

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Sutisna, Setya Permana, Radite Praeko Agus Setiawan, I. Dewa Made Subrata, and Tineke Mandang. "Tracking Control of an Autonomous Head Feeding Combine." Instrumentation Mesure Métrologie 20, no. 2 (April 30, 2021): 85–90. http://dx.doi.org/10.18280/i2m.200204.

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This study aims to develop an autonomous combine harvester. A manual steering combine harvester was modified autonomously using navigation systems of an RTK-DGPS, a gyroscope, and crawler speed sensors. These sensors could determine the combine position and heading required to guide the path. The control system is processed for these navigation sensors' data to make the decision of combine movement. Moreover, it commands the actuator to move the steering lever mechanism. The steering control's desired heading angle was determined from lateral error, heading error, and the traveling speed. In this study, the combined harvester's average forward traveling speed was set at 0.17 m/s, adjusted to a navigation sensor's sampling rate of 5 Hz and the steering mechanism delay. The preliminary test showed the combine could turn by pivoting one of its tracks which turned the radius was into 0.4 m. Furthermore, a guidance control system of the combine harvester was developed based on this test result. The developed guidance control system was successfully guiding the combine to follow the harvesting path. The test results showed that the root mean square of the lateral error was less than 0.1 m.

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Abu El-Sebah, Mohamed I., Fathy A. Syam, Emad A. Sweelem, Mohamed M. El-Sotouhy, and Mohamed M. El Sotouhy. "A Proposed Controller for an Autonomous Vehicles Embedded System." WSEAS TRANSACTIONS ON CIRCUITS AND SYSTEMS 22 (February 7, 2023): 1–9. http://dx.doi.org/10.37394/23201.2023.22.1.

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Many research have observable development in the automated vehicle driving field during the last few decades. This research proposed a simple optimum Intelligent PID (SO PID) controller to simplify the automated vehicle motion control. Control of an autonomous vehicle’s steering routines plays an essential key role. Several steering control procedures are proposed that improve automated vehicle performance. The design of secure embedded control systems must overcome the difficulties associated with designing both computing and control systems. Also, this research introduces a model of the autonomous car prototype controlled via an Arduino microcontroller board and the GPS Module to receive the car coordinates. The car moves safely, and autonomously consequently avoiding the risk of human faults. Several algorithms such as angle and distance calculations to the waypoint and obstacle detection are combined to control the car movement.

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Vu, Trieu Minh, Reza Moezzi, Jindrich Cyrus, Jaroslav Hlava, and Michal Petru. "Feasible Trajectories Generation for Autonomous Driving Vehicles." Applied Sciences 11, no. 23 (November 24, 2021): 11143. http://dx.doi.org/10.3390/app112311143.

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This study presents smooth and fast feasible trajectory generation for autonomous driving vehicles subject to the vehicle physical constraints on the vehicle power, speed, acceleration as well as the hard limitations of the vehicle steering angle and the steering angular speed. This is due to the fact the vehicle speed and the vehicle steering angle are always in a strict relationship for safety purposes, depending on the real vehicle driving constraints, the environmental conditions, and the surrounding obstacles. Three different methods of the position quintic polynomial, speed quartic polynomial, and symmetric polynomial function for generating the vehicle trajectories are presented and illustrated with simulations. The optimal trajectory is selected according to three criteria: Smoother curve, smaller tracking error, and shorter distance. The outcomes of this paper can be used for generating online trajectories for autonomous driving vehicles and auto-parking systems.

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Zhang, Bingli, Jin Cheng, Pingping Zheng, Aojia Li, and Xiaoyu Cheng. "Research and Experiment on Path-Tracking Control of Autonomous Tractor Based on Lateral Deviation and Yaw Rate Feedback." Applied Engineering in Agriculture 37, no. 5 (2021): 891–99. http://dx.doi.org/10.13031/aea.14538.

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HighlightsAutomatic navigation technology in autonomous tractors is one of the key technologies in precision agriculture.A path-tracking control algorithm based on lateral deviation and yaw rate feedback is proposed.The modified steering angle was obtained by comparing the ideal yaw rate with the actual yaw rate.The results demonstrate the efficiency and superior accuracy of the proposed algorithm for tractor path-tracking control.Abstract. The performance of path-tracking control systems for autonomous tractors affects the quality and efficiency of farmland operations. The objective of this study was to develop a path-tracking control algorithm based on lateral deviation and yaw rate feedback. The autonomous tractor path lateral dynamics model was developed based on preview theory and a two-degree-of-freedom tractor model. According to the established dynamic model, a path-tracking control algorithm using yaw angular velocity correction was designed, and the ideal steering angle was obtained by lateral deviation and sliding mode control. The modified steering angle was obtained by a proportional-integral-derivative feedback controller after comparing the ideal yaw rate with the actual yaw rate, which was then combined with the ideal steering angle to obtain the desired steering angle. The simulation and experimental results demonstrate the efficiency and superior accuracy of the proposed tractor path-tracking control algorithm, enabling its application in automatic navigation control systems for autonomous tractors. Keywords: Autonomous tractor, Path-tracking control, Sliding mode control, Yaw rate feedback.

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Kapsalis, Dimitrios, Olivier Sename, Vicente Milanés, and John J. Martinez. "Gain‐scheduled steering control for autonomous vehicles." IET Control Theory & Applications 14, no. 20 (December 2020): 3451–60. http://dx.doi.org/10.1049/iet-cta.2020.0698.

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Herpin, John, Afef Fekih, Suresh Golconda, and Arun Lakhotia. "Steering Control of the Autonomous Vehicle: CajunBot." Journal of Aerospace Computing, Information, and Communication 4, no. 12 (December 2007): 1134–42. http://dx.doi.org/10.2514/1.35050.

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Hunt, K. J., R. Haas, and J. C. Kalkkuhl. "Local Controller Network for Autonomous Vehicle Steering." IFAC Proceedings Volumes 29, no. 1 (June 1996): 8101–6. http://dx.doi.org/10.1016/s1474-6670(17)58997-1.

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Hunt, K. J., R. Haas, and J. C. Kalkkuhl. "Local controller network for autonomous vehicle steering." Control Engineering Practice 4, no. 8 (August 1996): 1045–51. http://dx.doi.org/10.1016/0967-0661(96)00104-9.

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Kapadia, Mubbasir, and Norman I. Badler. "Navigation and steering for autonomous virtual humans." Wiley Interdisciplinary Reviews: Cognitive Science 4, no. 3 (February 6, 2013): 263–72. http://dx.doi.org/10.1002/wcs.1223.

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Jiang, Haobin, Aoxue Li, Xinchen Zhou, and Yue Yu. "Establishment and tracking control of trapezoidal steering wheel angle model for autonomous vehicles." International Journal of Advanced Robotic Systems 17, no. 6 (November 1, 2020): 172988142098278. http://dx.doi.org/10.1177/1729881420982781.

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Human drivers have rich and diverse driving characteristics on curved roads. Finding the characteristic quantities of the experienced drivers during curve driving and applying them to the steering control of autonomous vehicles is the research goal of this article. We first recruited 10 taxi drivers, 5 bus drivers, and 5 driving instructors as the representatives of experienced drivers and conducted a real car field experiment on six curves with different lengths and curvatures. After processing the collected driving data in the Frenet frame and considering the free play of a real car’s steering system, it was interesting to observe that the shape enclosed by steering wheel angles and the coordinate axis was a trapezoid. Then, we defined four feature points, four feature distances, and one feature steering wheel angle, and the trapezoidal steering wheel angle (TSWA) model was developed by backpropagation neural network with the inputs were vehicle speeds at four feature points, and road curvature and the outputs were feature distances and feature steering wheel angle. The comparisons between TSWA model and experienced drivers, model predictive control, and preview-based driver model showed that the proposed TSWA model can best reflect the steering features of experienced drivers. What is more, the concise expression and human-like characteristic of TSWA model make it easy to realize human-like steering control for autonomous vehicles. Lastly, an autonomous vehicle composed of a nonlinear vehicle model and electric power steering (EPS) system was established in Simulink, the steering wheel angles generated by TSWA model were tracked by EPS motor directly, and the results showed that the EPS system can track the steering angles with high accuracy at different vehicle speeds.

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Fu, Yi, Howard Li, and Mary Kaye. "Design and Lyapunov Stability Analysis of a Fuzzy Logic Controller for Autonomous Road Following." Mathematical Problems in Engineering 2010 (2010): 1–20. http://dx.doi.org/10.1155/2010/578406.

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Autonomous road following is one of the major goals in intelligent vehicle applications. The development of an autonomous road following embedded system for intelligent vehicles is the focus of this paper. A fuzzy logic controller (FLC) is designed for vision-based autonomous road following. The stability analysis of this control system is addressed. Lyapunov's direct method is utilized to formulate a class of control laws that guarantee the convergence of the steering error. Certain requirements for the control laws are presented for designers to choose a suitable rule base for the fuzzy controller in order to make the system stable. Stability of the proposed fuzzy controller is guaranteed theoretically and also demonstrated by simulation studies and experiments. Simulations using the model of the four degree of freedom nonholonomic robotic vehicle are conducted to investigate the performance of the fuzzy controller. The proposed fuzzy controller can achieve the desired steering angle and make the robotic vehicle follow the road successfully. Experiments show that the developed intelligent vehicle is able to follow a mocked road autonomously.

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L, Padmasree, Preethi Eluri, Sai Subrahmanya Akhil Badampudi, and Sreedhar Reddy Mukkamalla. "Real Time Vision System for Autonomous Vehicles." Review of Computer Engineering Studies 7, no. 4 (December 31, 2020): 87–90. http://dx.doi.org/10.18280/rces.070402.

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With the increase in vehicle accidents regularly, there is a need to control these accidents and save precious lives. The main reason for accidents on roads are mainly observed by driver misconception, recklessness and over speeding. So, there is a need to develop a Vision system which has a ability to explore its surroundings and move accordingly. The Vision system is divided into 3 subsystems as Visual perception subsystem, Brake and Acceleration subsystem and Steering control subsystem. The Visual perception means the ability to interpret surrounding environment using light in the visual spectrum reflected by the objects in the environment. This subsystem uses distance measuring sensors such as Light Detection and Ranging (LiDAR) and Ultrasonic sensors for detecting objects and sends the data to brake and acceleration subsystem using Arduino IDE software. According to the data received either the brake or acceleration is initiated, it means that when the distance measuring sensor values reach the threshold values then the brakes are applied or else acceleration is implemented. In order to have a smooth ride the acceleration should be uniform without any jerks though speed changes. This is resolved by using Proportional-Integral- Derivative (PID) controller which reduces the gradual difference between the desired and input speed. The Steering control subsystem involves lane detection and path tracking. The lane detection is done using Python and OpenCv which uses various image processing steps, gives the steering angle by calculating the curvature radius of lanes. Therefore path tracking system is initialized taking the steering angle and direction as input for controlling the position of the vehicle.

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Wang, Xianbin, and Shuming Shi. "Analysis of Vehicle Steering and Driving Bifurcation Characteristics." Mathematical Problems in Engineering 2015 (2015): 1–16. http://dx.doi.org/10.1155/2015/847258.

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The typical method of vehicle steering bifurcation analysis is based on the nonlinear autonomous vehicle model deriving from the classic two degrees of freedom (2DOF) linear vehicle model. This method usually neglects the driving effect on steering bifurcation characteristics. However, in the steering and driving combined conditions, the tyre under different driving conditions can provide different lateral force. The steering bifurcation mechanism without the driving effect is not able to fully reveal the vehicle steering and driving bifurcation characteristics. Aiming at the aforementioned problem, this paper analyzed the vehicle steering and driving bifurcation characteristics with the consideration of driving effect. Based on the 5DOF vehicle system dynamics model with the consideration of driving effect, the 7DOF autonomous system model was established. The vehicle steering and driving bifurcation dynamic characteristics were analyzed with different driving mode and driving torque. Taking the front-wheel-drive system as an example, the dynamic evolution process of steering and driving bifurcation was analyzed by phase space, system state variables, power spectral density, and Lyapunov index. The numerical recognition results of chaos were also provided. The research results show that the driving mode and driving torque have the obvious effect on steering and driving bifurcation characteristics.

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Bao, Chunjiang, Jiwei Feng, Jian Wu, Shifu Liu, Guangfei Xu, and Haizhu Xu. "Model predictive control of steering torque in shared driving of autonomous vehicles." Science Progress 103, no. 3 (July 2020): 003685042095013. http://dx.doi.org/10.1177/0036850420950138.

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The current path tracking control method is usually based on the steering wheel angle loop, which often makes the driver lose control of the automatic driving control loop. In order to involve the driver in the automatic driving control loop, and to solve the vehicle path tracking control problem with system robustness and model uncertainty, this paper puts forward a steering torque control method based on model predictive control algorithm. Based on the vehicle model, this method introduces the steering system model and the steering resistance torque model, and calculates the optimal control torque of the vehicle through the real-time vehicle status, so as to make up for the model mismatch, interference and other uncertainties, and ensure the real-time participation of the driver in the automatic driving control loop. To combine the nonlinear vehicle dynamics model with the steering column model, and to take the vehicle state parameters as the feedback variables of the model predictive controller model, then input the solution of the steering superposition control rate into the vehicle model, the design of the steering controller is realized. Finally, to carry out the simulation of lane keeping based on CarSim software and Simulink control model, and the hardware in-the-loop test on the hardware in-the-loop experimental platform of CarSim/LabVIEW-RT. The simulation and test results indicate that the designed torque loop path tracking control method based on model predictive control can help the driver track the target path better.

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Sasaki, Minoru, Hidenobu Tanaka, and Satoshi Ito. "Development of an Autonomous Two-Wheeled Vehicle Robot." Advanced Engineering Forum 2-3 (December 2011): 390–95. http://dx.doi.org/10.4028/www.scientific.net/aef.2-3.390.

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This paper describes a development of an autonomous two-wheeled vehicle robot. The model of the two-wheeled vehicle using steering control is derived. The control systems are designed by linear quadratic regulator and linear quadratic integral method. Stabilization is achieved by measuring roll angle and roll rate and controlling the steering torque. The experimental results and simulation results show stable running control of the two-wheeled vehicle robot and coincident with each other. The approach is validated through these results.

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Devi, T. Kirthiga, Akshat Srivatsava, Kritesh Kumar Mudgal, Ranjnish Raj Jayanti, and T. Karthick. "Behaviour Cloning for Autonomous Driving." Webology 17, no. 2 (December 21, 2020): 694–705. http://dx.doi.org/10.14704/web/v17i2/web17061.

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The objective of this project is to automate the process of driving a car. The result of this project will surely reduce the number of hazards happening everyday. Our world is in progress and self driving car is on its way to reach consumer‟s door-step but the big question still lies that will people accept such a car which is fully automated and driverless. The idea is to create an autonomous Vehicle that uses only some sensors (collision detectors, temperature detectors etc.) and camera module to travel between destinations with minimal/no human intervention. The car will be using a trained Convolutional Neural Network (CNN) which would control the parameters that are required for smoothly driving a car. They are directly connected to the main steering mechanism and the output of the deep learning model will control the steering angle of the vehicle. Many algorithms like Lane Detection, Object Detection are used in tandem to provide the necessary functionalities in the car.

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Rochan, M. Ranjith, K. Aarthi Alagammai, and J. Sujatha. "Enhanced navigation using computer vision-based steering angle calculation for autonomous vehicles." Encyclopedia with Semantic Computing and Robotic Intelligence 02, no. 01 (June 2018): 1850007. http://dx.doi.org/10.1142/s2529737618500077.

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A key requirement in the development of intelligent and driverless vehicles is steering angle computation for efficient navigation. This paper presents a novel method for computing steering angle for driverless vehicles using computer vision-based techniques of relatively lower computing cost. The proposed system consists of four major stages. The first stage includes dynamic road region extraction using Gaussian mixture model and expectation maximization algorithm. The second stage is to compute the steering angle based on the extracted road region. Subsequently, Kalman filtering technique is used to cancel spurious angle transition noises. In addition, future steering angle is estimated which in turn gives informative feedback for smooth navigation of the vehicle. The proposed algorithm was tested both on a simulator and real-time images and was found to give a good estimation of actual steering angle required for navigation. Further, it was also observed that this works in different lighting conditions as well as for both structured and unstructured road scenarios.

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Mendonça, Emerson Coelho. "STEERING AUTOPILOT APPLIED TO A AUTONOMOUS UNDERWATER VEHICLE." Brazilian Journal of Development 6, no. 8 (2020): 54694–707. http://dx.doi.org/10.34117/bjdv6n8-037.

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Kim, Ju-Young, Kyungdeuk Min, and Young Chol Kim. "Empirical Modeling of Steering System for Autonomous Vehicles." Journal of Electrical Engineering and Technology 12, no. 2 (March 1, 2017): 937–43. http://dx.doi.org/10.5370/jeet.2017.12.2.937.

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Beji, Lotfi, Azgal Abichou, and Rajia Slim. "Longitudinal and Steering Stabilization of an Autonomous Vehicle." IFAC Proceedings Volumes 36, no. 17 (September 2003): 485–90. http://dx.doi.org/10.1016/s1474-6670(17)33441-9.

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Sutton, R., R. S. Burns, and P. J. Craven. "Intelligent Steering Control of an Autonomous Underwater Vehicle." Journal of Navigation 53, no. 3 (September 2000): 511–25. http://dx.doi.org/10.1017/s0373463300008973.

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This paper considers the development of three autopilots for controlling the yaw responses of an autonomous underwater vehicle model. The autopilot designs are based on the adaptive network-based fuzzy inference system (ANFIS), a simulated, annealing-tuned control algorithm and a traditional proportional-derivative controller. In addition, each autopilot is integrated with a line-of-sight (LOS) guidance system to test its effectiveness in steering round a series of waypoints with and without the presence of sea current disturbance. Simulation results are presented that show the overall superiority of the ANFIS approach.

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Huang, Zexin, Matthew Best, and James Knowles. "Optimal predictive steering control for autonomous runway exits." Advances in Mechanical Engineering 12, no. 12 (December 2020): 168781402098086. http://dx.doi.org/10.1177/1687814020980861.

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In this paper, we present a real-time optimal controller, Predictive Steering Control (PSC), to perform high-speed runway exit manoeuvres. PSC is developed based on a time-varying LQR with look-ahead. The aircraft’s ground dynamics are described by a high-fidelity nonlinear model. The proposed controller is compared with an Expert Pilot Model (EPM), which represents a pilot, in several different speed runway exit manoeuvres. With an improved road preview mechanism and optimal feedback gain, the predictive steering controller outperforms the expert pilot’s manual operations by executing the runway exit manoeuvre with a lower track error. To investigate the optimality of PSC, its solution is further optimised using a numerical optimal controller Generalized Optimal Control (GOC). PSC is shown to be close to the final optimal solution. To study robustness, PSC is tested with various aircraft configurations, road conditions and disturbances. The simulation results show that PSC is robust to disturbances within a normal range of operational parameters.

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Heitz, Thomas, Arne Schacht, Tim Bayer, and Daniel Kreutz. "Steering Concepts for Highly Automated and Autonomous Driving." ATZ worldwide 120, no. 11 (October 26, 2018): 18–23. http://dx.doi.org/10.1007/s38311-018-0154-0.

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Pears, N. E., and J. R. Bumby. "The steering control of an experimental autonomous vehicle." Transactions of the Institute of Measurement and Control 13, no. 4 (October 1991): 190–200. http://dx.doi.org/10.1177/014233129101300404.

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Yang, Yiqin, Zhe Wu, Qingyang Xu, and Fabao Yan. "Deep Learning Technique-Based Steering of Autonomous Car." International Journal of Computational Intelligence and Applications 17, no. 02 (June 2018): 1850006. http://dx.doi.org/10.1142/s1469026818500062.

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Deep neural network (DNN) has many advantages. Autonomous driving has become a popular topic now. In this paper, an improved stack autoencoder based on the deep learning techniques is proposed to learn the driving characteristics of an autonomous car. These techniques realize the input data adjustment and solving diffusion gradient problem. A Raspberry Pi and a camera module are mounted on the top of the car. The camera module provides the images needed for training the DNN. There are two stages in the training. In the pre-training process, an improved autoencoder is trained by the unsupervised learning mechanism, and the characterization of the track is extracted. In the fine-tuning stage, the whole network is trained according to the labeled data, and then this model learns the driving characteristics better according to the samples. In the experimental stage, the car will predict the action of the car by the trained model in the autonomous mode. The experiment exhibits the effectiveness of the proposed model. Compared with the traditional neural network, the improved stack autoencoder has a better generalization ability and faster convergence speed.

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Falcone, Paolo, Francesco Borrelli, Jahan Asgari, Hongtei Eric Tseng, and Davor Hrovat. "Predictive Active Steering Control for Autonomous Vehicle Systems." IEEE Transactions on Control Systems Technology 15, no. 3 (May 2007): 566–80. http://dx.doi.org/10.1109/tcst.2007.894653.

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Ohkawa, Shinya, Yoshihiro Takita, Hisashi Date, and Kazuhiro Kobayashi. "Development of Autonomous Mobile Robot Using Articulated Steering Vehicle and Lateral Guiding Method." Journal of Robotics and Mechatronics 27, no. 4 (August 20, 2015): 337–45. http://dx.doi.org/10.20965/jrm.2015.p0337.

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<div class=""abs_img""> <img src=""[disp_template_path]/JRM/abst-image/00270004/03.jpg"" width=""300"" /> Autonomous robot “AR Chair”</div> This paper is discusses an autonomous mobile robot entered in the Real World Robotics Challenges 2014 (RWRC) in Tsukuba. Our project was to develop a wheelchair able to navigate stairs autonomously. Step 1 develops a center articulated vehicle, called the AR Chair, which has 4 wheels and a controller including LIDARs. The center articulated vehicle has a stiff structure and travels with the front and rear wheels on the same path, so there is no inner wheels difference. The robotic vehicle carries users weighing up to 100 kg. The autonomous controller is the same as the Smart Dump 7 combined with the RWRC 2013 to achieve the challenge, excluding the geometrical relationship of the steering angle and communication command for motor drivers to the AR Chair. The advantage of the robot is shown by experimental data from the RWRC 2014’s final run. </span>

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Lee, Junho, and Hyuk-Jun Chang. "Multi-parametric model predictive control for autonomous steering using an electric power steering system." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 233, no. 13 (January 22, 2019): 3391–402. http://dx.doi.org/10.1177/0954407018824773.

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Electric power steering systems have been used to generate assist torque for driver comfort. This study makes use of the functionality of electric power steering systems for autonomous steering control without driver torque. A column-type electric power steering test bench, equipped with a brushless DC motor as an assist motor, and the Infineon TriCore AURIX TC 277 microcontroller was used in this study. Multi-parametric model predictive control is based on a model predictive control–based approach that employs a multi-parametric quadratic programming technique. This technique allows the reduction of the huge computational burden resulting from the online optimization in model predictive control. The proposed controller obtains an optimal input based on multi-parametric quadratic programming at each sampling time. The weighting matrix definition, which is the main task when designing the proposed controller, was analyzed. The experimental results of the step response of the steering wheel angle verified the tracking ability of the proposed controller for different ranges of the prediction horizon. Since the computational loads are directly related to functional safety, the results of this study support the use of the multi-parametric model predictive control scheme as an effective control method for autonomous steering control.

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Li, Jianshi, Jingtao Lou, Yongle Li, Shiju Pan, and Youchun Xu. "Trajectory Tracking of Autonomous Vehicle Using Clothoid Curve." Applied Sciences 13, no. 4 (February 20, 2023): 2733. http://dx.doi.org/10.3390/app13042733.

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This paper proposes a clothoid-curve-based trajectory tracking control method for autonomous vehicles to solve the problem of tracking errors caused by the discontinuous curvature of the control curve calculated by the pure pursuit tracking algorithm. Firstly, based on the Ackerman steering model, the motion model is constructed for vehicle trajectory tracking, Then, the position of the vehicle after the communication delay of the control system is predicted as the starting point of the clothoid control curve, and the optimization interval of the curve end point is determined. The clothoid control curves are calculated, and their parameters are verified by the vehicle motion and safety constraints, so as to obtain the optimal clothoid control curve satisfying the constraints. Finally, considering the servo system response delay time of the steering system, the steering angle target control value is obtained by previewing the curvature of the clothoid control curve. The field experiment is conducted on the test road, which consists of straight, right-angle turns and lane-change elements under three sets of speed limitations, and the test results show that the proposed clothoid-curve-based trajectory tracking control method greatly improved the tracking accuracy compared with the pure pursuit method; in particular, the yaw deviation is improved by more than 50%.

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Tian, Jie, and Mingfei Yang. "Research on trajectory tracking and body attitude control of autonomous ground vehicle based on differential steering." PLOS ONE 18, no. 2 (February 8, 2023): e0273255. http://dx.doi.org/10.1371/journal.pone.0273255.

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The differential steering can be used not only as the backup system of steer-by-wire, but also as the only steering system. Because the differential steering is realized through the differential moment between the coaxial left and right driving wheels, the sharp reduction of the load on the inner driving wheel will directly lead to the failure of the differential steering when the four-wheel independent drive electric vehicle approaches the rollover. Therefore, this paper not only realizes the trajectory tracking of autonomous ground vehicle through the differential steering, but also puts forward the body attitude control to improve the handling stability. Firstly, the dynamic and kinematic models of differential steering autonomous ground vehicle (DSAGV) and its roll model are established, and the linear three-degree of freedom vehicle model is selected as the reference model to generate the ideal body roll angle. Secondly, a model predictive controller (MPC) is designed to control the DSAGV to track the given reference trajectory, and obtain the required differential moment and the resulting front-wheel steering angle. Then, a sliding mode controller (SMC) is adopted to control the DSAGV to track the ideal body roll angle, and obtain the required roll moment. The simulation results show that the proposed MPC and SMC can not only make the DSAGV realize the trajectory tracking, but also achieve the body attitude control.

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Seet, Gerald, R. S. Senanayake, and Eicher Low. "Autonomous Mobile Robot for Hospitals." Journal of Robotics and Mechatronics 7, no. 3 (June 20, 1995): 263–69. http://dx.doi.org/10.20965/jrm.1995.p0263.

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In recent years, the AGV has been making its way out of the traditional manufacturing and warehouse environments into newer application areas such as hospitals. This is in no small part due to the shortage of nursing and ancillary staff, and to the increase in the cost of employing and training such personnel. The freeing of hospital staff from mundane tasks also enhances job value and frees personnel to patient care. Experimental systems have been applied to the transfer of meals, documents, and other materials. These systems were developed based on general-purpose mobile vehicle platforms, modified for hospital application. However, the different constraints imposed by a hospital environment result in a less than optimum design. An omni-directional vehicle (ODV) has been specifically designed for hospital application. The compact size allows the vehicle to maneuver within the tight corridors of a hospital with minimum interference to other users. The steering-drive unit located at each corner provides steering and drive through differential velocity control of the wheels, and its modular design enables easy maintenance and repair. This paper identifies the unique requirements imposed by the hospital environment and explains how the vehicle has been designed to meet and exploit the unique features of such a situation.

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He, Zhiwei, Linzhen Nie, Zhishuai Yin, and Song Huang. "A Two-Layer Controller for Lateral Path Tracking Control of Autonomous Vehicles." Sensors 20, no. 13 (July 1, 2020): 3689. http://dx.doi.org/10.3390/s20133689.

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This paper presents a two-layer controller for accurate and robust lateral path tracking control of highly automated vehicles. The upper-layer controller, which produces the front wheel steering angle, is implemented with a Linear Time-Varying MPC (LTV-MPC) whose prediction and control horizon are both optimized offline with particle swarm optimization (PSO) under varying working conditions. A constraint on the slip angle is imposed to prevent lateral forces from saturation to guarantee vehicle stability. The lower layer is a radial basis function neural network proportion-integral-derivative (RBFNN-PID) controller that generates electric current control signals executable by the steering motor to rapidly track the target steering angle. The nonlinear characteristics of the steering system are modeled and are identified on-line with the RBFNN so that the PID controller’s control parameters can be adjusted adaptively. The results of CarSim-Matlab/Simulink joint simulations show that the proposed hierarchical controller achieves a good level of path tracking accuracy while maintaining vehicle stability throughout the path tracking process, and is robust to dynamic changes in vehicle velocities and road adhesion coefficients.

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Vu, Trieu Minh, Reza Moezzi, Jindrich Cyrus, and Jaroslav Hlava. "Model Predictive Control for Autonomous Driving Vehicles." Electronics 10, no. 21 (October 24, 2021): 2593. http://dx.doi.org/10.3390/electronics10212593.

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The field of autonomous driving vehicles is growing and expanding rapidly. However, the control systems for autonomous driving vehicles still pose challenges, since vehicle speed and steering angle are always subject to strict constraints in vehicle dynamics. The optimal control action for vehicle speed and steering angular velocity can be obtained from the online objective function, subject to the dynamic constraints of the vehicle’s physical limitations, the environmental conditions, and the surrounding obstacles. This paper presents the design of a nonlinear model predictive controller subject to hard and softened constraints. Nonlinear model predictive control subject to softened constraints provides a higher probability of the controller finding the optimal control actions and maintaining system stability. Different parameters of the nonlinear model predictive controller are simulated and analyzed. Results show that nonlinear model predictive control with softened constraints can considerably improve the ability of autonomous driving vehicles to track exactly on different trajectories.

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Stania, Marek. "Mechatronics Systems of Autonomous Transport Vehicle." Solid State Phenomena 198 (March 2013): 96–101. http://dx.doi.org/10.4028/www.scientific.net/ssp.198.96.

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The purpose of this paper is to present the development and realization of the elaborate mechatronic systems, having its main application in the logistic industry. The innovative, patented steering system is its unique feature. The steerage is based on the torque difference between the drive wheels. This solution allows for the unlimited maneuverability during the motion of the vehicle.

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Rasib, Marya, Muhammad Atif Butt, Shehzad Khalid, Samia Abid, Faisal Raiz, Sohail Jabbar, and Kijun Han. "Are Self-Driving Vehicles Ready to Launch? An Insight into Steering Control in Autonomous Self-Driving Vehicles." Mathematical Problems in Engineering 2021 (February 18, 2021): 1–22. http://dx.doi.org/10.1155/2021/6639169.

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In the last couple of years, academia-industry collaboration has demonstrated rapid advancements in the development of self-driving vehicles. Since it is evident that self-driving vehicles are going to reshape the traditional transportation systems in near future through enhancement in safe and smart mobility, motion control in self-driving vehicles while performing driving tasks in a dynamic road environment is still a challenging task. In this regard, we present a comprehensive study considering the evolution of steering control methods for self-driving vehicles. Initially, we discussed an insight into the traditional steering systems of the vehicles. To the best of our knowledge, currently, there is no taxonomy available, which elaborates steering control methods for self-driving vehicles. In this paper, we present a novel taxonomy including different steering control methods which are categorized into deterministic and heuristic steering control methods. Concurrently, the abovementioned techniques are critically reviewed elaborating their strengths and limitations. Based on the analysis, key challenges/research gaps in existing steering control methods along with the possible solutions have been briefly discussed to improve the effectiveness of the steering system of self-driving vehicles.

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Reda, Ahmad, and József Vásárhelyi. "Model-Based Control Strategy for Autonomous Vehicle Path Tracking Task." Acta Universitatis Sapientiae, Electrical and Mechanical Engineering 12, no. 1 (December 1, 2020): 35–45. http://dx.doi.org/10.2478/auseme-2020-0003.

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Abstract Despite the advanced technologies used in recent years, the lack of robust systems still exists. The automated steering system is a critical and complex task in the domain of the autonomous vehicle’s applications. This paper is a part of project that deals with model-based control strategy as one of the most common control strategies. The main objective is to present the implementations of Model Predictive Control (MPC) for an autonomous vehicle steering system in regards to trajectory tracking application. The obtained results are analysed and the efficiency of the use of MPC controller were discussed based on its behaviour and performance.

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García Cuenca, Laura, Javier Sanchez-Soriano, Enrique Puertas, Javier Fernandez Andrés, and Nourdine Aliane. "Machine Learning Techniques for Undertaking Roundabouts in Autonomous Driving." Sensors 19, no. 10 (May 24, 2019): 2386. http://dx.doi.org/10.3390/s19102386.

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This article presents a machine learning-based technique to build a predictive model and generate rules of action to allow autonomous vehicles to perform roundabout maneuvers. The approach consists of building a predictive model of vehicle speeds and steering angles based on collected data related to driver–vehicle interactions and other aggregated data intrinsic to the traffic environment, such as roundabout geometry and the number of lanes obtained from Open-Street-Maps and offline video processing. The study systematically generates rules of action regarding the vehicle speed and steering angle required for autonomous vehicles to achieve complete roundabout maneuvers. Supervised learning algorithms like the support vector machine, linear regression, and deep learning are used to form the predictive models.

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Gao, Lulu, Chun Jin, Yuchao Liu, Fei Ma, and Zhipeng Feng. "Hybrid Model-Based Analysis of Underground Articulated Vehicles Steering Characteristics." Applied Sciences 9, no. 24 (December 4, 2019): 5274. http://dx.doi.org/10.3390/app9245274.

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Owing to the harsh environment of underground mines, autonomous underground articulated vehicles (UAVs) with precise control and positioning system are particularly important. However, the ambiguity of steering characteristics hinders the development of UAVs. This study presents a model-based method to uncover the steering characteristics of a UAV. Firstly, a hybrid model of UAV was established, which included a dynamic model of articulated frames and a model of the hydraulic power steering system. Secondly, a field test of a typical UAV, a load-haul-dump (LHD) with 4 m3 capacity, was carried out. In order to verify the correctness of the established model and the accuracy of the involved parameters, the field test results were used to verify the dynamic model in time and frequency domains. Then, the steering characteristics of the UAV were uncovered based on the verified hybrid model, and the results showed that the increased load would increase ‘oversteering’ under the same articulation angle and that the error of trajectory exceeded 0.3 m. In addition, the deviations of trajectories between the two frames were revealed during the transient steering process, and the maximum deviation reached 0.21 m when the velocity was 2 m/s and the articulation angle was 15°. The comprehensive results indicate that the steering characteristics of UAVs cannot be ignored in regard to precise autonomous control and positioning.

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Islam, Mhafuzul, Mashrur Chowdhury, Hongda Li, and Hongxin Hu. "Vision-Based Navigation of Autonomous Vehicles in Roadway Environments with Unexpected Hazards." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 12 (July 31, 2019): 494–507. http://dx.doi.org/10.1177/0361198119855606.

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Vision-based navigation of autonomous vehicles primarily depends on the deep neural network (DNN) based systems in which the controller obtains input from sensors/detectors, such as cameras, and produces a vehicle control output, such as a steering wheel angle to navigate the vehicle safely in a roadway traffic environment. Typically, these DNN-based systems in the autonomous vehicle are trained through supervised learning; however, recent studies show that a trained DNN-based system can be compromised by perturbation or adverse inputs. Similarly, this perturbation can be introduced into the DNN-based systems of autonomous vehicles by unexpected roadway hazards, such as debris or roadblocks. In this study, we first introduce a hazardous roadway environment that can compromise the DNN-based navigational system of an autonomous vehicle, and produce an incorrect steering wheel angle, which could cause crashes resulting in fatality or injury. Then, we develop a DNN-based autonomous vehicle driving system using object detection and semantic segmentation to mitigate the adverse effect of this type of hazard, which helps the autonomous vehicle to navigate safely around such hazards. We find that our developed DNN-based autonomous vehicle driving system, including hazardous object detection and semantic segmentation, improves the navigational ability of an autonomous vehicle to avoid a potential hazard by 21% compared with the traditional DNN-based autonomous vehicle driving system.

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Wang, Xiaobo, Qin Li, Hongshan Zha, and Bo Wang. "Integrated active steering control strategy for autonomous articulated vehicles." International Journal of Heavy Vehicle Systems 1, no. 1 (2020): 1. http://dx.doi.org/10.1504/ijhvs.2020.10026640.

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Journal articles on the topic 'Autonomous steering'

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