steering angle
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Author(s):  
Jesús Arturo Sánchez-Sánchez ◽  
Montserrat Navarro-Espino ◽  
Yonatan Betancur Ocampo ◽  
José Eduardo Barrios Vargas ◽  
Thomas Stegmann

Abstract A nanoelectronic device made of twisted bilayer graphene (TBLG) is proposed to steer the direction of the current flow. The ballistic electron current, injected at one edge of the bottom layer, can be guided predominantly to one of the lateral edges of the top layer. The current is steered to the opposite lateral edge, if either the twist angle is reversed or the electrons are injected in the valence band instead of the conduction band, making it possible to control the current flow by electric gates. When both graphene layers are aligned, the current passes straight through the system without changing its initial direction. The observed steering angle exceeds well the twist angle and emerges for a broad range of experimentally accessible parameters. It is explained by the twist angle and the trigonal shape of the energy bands beyond the van Hove singularity due to the Moiré interference pattern. As the shape of the energy bands depends on the valley degree of freedom, the steered current is valley polarized. Our findings show how to control and manipulate the current flow in TBLG. Technologically, they are of relevance for applications in twistronics and valleytronics.


Author(s):  
Farong Kou ◽  
Xinqian Zhang ◽  
Jiannan Xu

Steering Angle is related to the design and optimization of steering mechanism and suspension, but it is not equal to the angle of knuckle around kingpin because of the existence of wheel alignment parameters. To calculate the steering angle, this paper derives based on homogeneous transformation its function expression by analyzing spatial geometric relation between the two angles and calculating coordinates related to steering trajectory of wheel center. Then, multi-body model of McPherson suspension with steering system is built and the calculation correctness is verified by comparing the function curve plotted by MATLAB software with the curve simulated by Adams/Car software. The calculation and simulation indicate that between the two angles, there is a ratio which is related to wheel alignment parameters and greater than 1.


2022 ◽  
Vol 71 (2) ◽  
pp. 2285-2302
Author(s):  
Hajira Saleem ◽  
Faisal Riaz ◽  
Asadullah Shaikh ◽  
Khairan Rajab ◽  
Adel Rajab ◽  
...  

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 126
Author(s):  
Md. Kalim Amzad Chy ◽  
Abdul Kadar Muhammad Masum ◽  
Kazi Abdullah Mohammad Sayeed ◽  
Md Zia Uddin

The rapid expansion of a country’s economy is highly dependent on timely product distribution, which is hampered by terrible traffic congestion. Additional staff are also required to follow the delivery vehicle while it transports documents or records to another destination. This study proposes Delicar, a self-driving product delivery vehicle that can drive the vehicle on the road and report the current geographical location to the authority in real-time through a map. The equipped camera module captures the road image and transfers it to the computer via socket server programming. The raspberry pi sends the camera image and waits for the steering angle value. The image is fed to the pre-trained deep learning model that predicts the steering angle regarding that situation. Then the steering angle value is passed to the raspberry pi that directs the L298 motor driver which direction the wheel should follow. Based upon this direction, L298 decides either forward or left or right or backwards movement. The 3-cell 12V LiPo battery handles the power supply to the raspberry pi and L298 motor driver. A buck converter regulates a 5V 3A power supply to the raspberry pi to be working. Nvidia CNN architecture has been followed, containing nine layers including five convolution layers and three dense layers to develop the steering angle predictive model. Geoip2 (a python library) retrieves the longitude and latitude from the equipped system’s IP address to report the live geographical position to the authorities. After that, Folium is used to depict the geographical location. Moreover, the system’s infrastructure is far too low-cost and easy to install.


Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 39
Author(s):  
Valentin Baier ◽  
Michael Schardt ◽  
Maximilian Fink ◽  
Martin Jakobi ◽  
Alexander W. Koch

LiDAR sensors are a key technology for enabling safe autonomous cars. For highway applications, such systems must have a long range, and the covered field of view (FoV) of >45° must be scanned with resolutions higher than 0.1°. These specifications can be met by modern MEMS scanners, which are chosen for their robustness and scalability. For the automotive market, these sensors, and especially the scanners within, must be tested to the highest standards. We propose a novel measurement setup for characterizing and validating these kinds of scanners based on a position-sensitive detector (PSD) by imaging a deflected laser beam from a diffuser screen onto the PSD. A so-called ray trace shifting technique (RTST) was used to minimize manual calibration effort, to reduce external mounting errors, and to enable dynamical one-shot measurements of the scanner’s steering angle over large FoVs. This paper describes the overall setup and the calibration method according to a standard camera calibration. We further show the setup’s capabilities by validating it with a statically set rotating stage and a dynamically oscillating MEMS scanner. The setup was found to be capable of measuring LiDAR MEMS scanners with a maximum FoV of 47° dynamically, with an uncertainty of less than 1%.


2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yushan Li ◽  
Jitai Yu ◽  
Ziliang Zhao ◽  
Bin Guo

Electro Hydraulic Coupling Steering (EHCS) system is a new type of intelligent commercial vehicle steering system, having strong nonlinear characteristics. Besides, the change of load would cause the change of control system parameters, making not easy to establish an accurate control model of it. To realize the robustness of EHCS under the change of load, the controller based on the adaptive control method is proposed in this paper. To this end, the cosimulation model of EHCS is first established, where the constructed control model is simplified to a 2-degree-of-freedom model under reasonable simplification and assumption. Then, the steering angle controller is designed based on the model reference adaptive theory. Finally, some simulations are given to show the effectiveness of the proposed control method.


Aerospace ◽  
2021 ◽  
Vol 8 (12) ◽  
pp. 400
Author(s):  
Hanafy M. Omar

In this work, we propose a systematic procedure to design a fuzzy logic controller (FLC) to control the lateral motion of powered parachute (PPC) flying vehicles. The design process does not require knowing the details of vehicle dynamics. Moreover, the physical constraints of the system, such as the maximum error of the yaw angle and the maximum allowed steering angle, are naturally included in the designed controller. The effectiveness of the proposed controller was assessed using the nonlinear six degrees of freedom (6DOF) mathematical model of the PPC. The genetic algorithm (GA) optimization technique was used to optimize the distribution of the fuzzy membership functions in order to improve the performance of the suggested controller. The robustness of the proposed controller was evaluated by changing the values of the parafoil aerodynamic coefficients and the initial flight conditions.


Author(s):  
Mithun M. S. ◽  
Utpol Tarafdar ◽  
Hemanth Sankar P.

Author(s):  
Manas Metar

Abstract: The road traffic collisions and injuries, is still a major concern in automotive field. A disproportionate number of fatal accidents happen at nighttime. The major part of the collision is contributed by the human factors. Yet the technology in automobile is continuously assisting drivers while driving. Driving in the dark is not always easy as roads aren’t always illuminated, in such cases a powerful headlight is needed to illuminate most of the road. But such headlights cause glare to the oncoming traffic and again the chances of collision increase, with risking lives of passengers. Therefore, a need of smart headlamps which can illuminate the road far ahead without glaring the oncoming traffic is generated. This research aims to build a Laser based adaptive headlight system which can fulfil the need. The headlight design is proposed using Tinkercad software in which Arduino circuit has been used and software design is presented. The system works well with responding to the steering angle and controls the intensity of light preventing oncoming traffic from getting glared. Keywords: Adaptive headlight system, laser headlights, Arduino, Arduino software design, design of headlight system, cornering lights, effects of laser headlights, Tinkercad


2021 ◽  
Author(s):  
Md Khairul Islam ◽  
Mst. Nilufa Yeasmin ◽  
Chetna Kaushal ◽  
Md Al Amin ◽  
Md Rakibul Islam ◽  
...  

Deep learning's rapid gains in automation are making it more popular in a variety of complex jobs. The self-driving object is an emerging technology that has the potential to transform the entire planet. The steering control of an automated item is critical to ensuring a safe and secure voyage. Consequently, in this study, we developed a methodology for predicting the steering angle only by looking at the front images of a vehicle. In addition, we used an Internet of Things-based system for collecting front images and steering angles. A Raspberry Pi (RP) camera is used in conjunction with a Raspberry Pi (RP) processing unit to capture images from vehicles, and the RP processing unit is used to collect the angles associated with each image. Apart from that, we've made use of deep learning-based algorithms such as VGG16, ResNet-152, DenseNet-201, and Nvidia's models, all of which were trained using labeled training data. Our models are End-to-End CNN models, which do not require extracting elements from data such as roads, lanes, or other objects before predicting steering angle. As a result of our comparative investigation, we can conclude that the Nvidia model's performance was satisfactory, with a Mean Squared Error (MSE) value of 0. But the Nvidia model outperforms the other pre-trained models, even though other models work well.<br>


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