Tactile feedback for virtual automotive steering wheel switches

Author(s):  
Lisa Diwischek ◽  
Jason Lisseman
2021 ◽  
Author(s):  
Ahmed Farooq ◽  
Hanna Venesvirta ◽  
Hasse Sinivaara ◽  
Mikko Laaksonen ◽  
Arto Hippula ◽  
...  

1999 ◽  
Vol 13 (4) ◽  
pp. 234-244
Author(s):  
Uwe Niederberger ◽  
Wolf-Dieter Gerber

Abstract In two experiments with four and two groups of healthy subjects, a novel motor task, the voluntary abduction of the right big toe, was trained. This task cannot usually be performed without training and is therefore ideal for the study of elementary motor learning. A systematic variation of proprioceptive, tactile, visual, and EMG feedback was used. In addition to peripheral measurements such as the voluntary range of motion and EMG output during training, a three-channel EEG was recorded over Cz, C3, and C4. The movement-related brain potential during distinct periods of the training was analyzed as a central nervous parameter of the ongoing learning process. In experiment I, we randomized four groups of 12 subjects each (group P: proprioceptive feedback; group PT: proprioceptive and tactile feedback; group PTV: proprioceptive, tactile, and visual feedback; group PTEMG: proprioceptive, tactile, and EMG feedback). Best training results were reported from the PTEMG and PTV groups. The movement-preceding cortical activity, in the form of the amplitude of the readiness potential at the time of EMG onset, was greatest in these two groups. Results of experiment II revealed a similar effect, with a greater training success and a higher electrocortical activation under additional EMG feedback compared to proprioceptive feedback alone. Sensory EMG feedback as evaluated by peripheral and central nervous measurements appears to be useful in motor training and neuromuscular re-education.


2012 ◽  
Author(s):  
Anthony D. McDonald ◽  
Chris Schwarz ◽  
John D. Lee ◽  
Timothy L. Brown

2020 ◽  
Vol 1 (2) ◽  
pp. 103
Author(s):  
Markus Nanang Irawan ◽  
Sri Widyawati

<pre><span>Individuals autism often have non-adaptive behavioral problems because of their barriers in communication and social interaction. The problem of non-adaptive behavior is often a nuisance to others because its appearance is not appropriate and not in accordance with the environment, age, and expectations of responsibility. One case of non-adaptive behavior that arises is the behavior while in a vehicle where the individual shows the behavior of singing loudly, knocking windows, pinching the driver, even holding the steering wheel. Based on these problems, this study aims to reduce non-adaptive behavior while in a vehicle. Participant is an adult autism. The research method is experiment by giving Social Stories to participants before riding the vehicle then recording to the possibility appearance of non adaptive behavior. The results of graph analysis showed a decrease in non adaptive behavior of adult autism adults while in a vehicle. This study became one of the important studies because it tries to understand the dynamics of behavior problems of individual autisme in adulthood.<strong></strong></span></pre><pre><span> </span></pre>


Author(s):  
Guilherme Cortelini da Rosa ◽  
Carlos Henrique Lagemann ◽  
Rafael Crespo Izquierdo ◽  
Julio Damyan Imbriaco Silveira ◽  
Marcelo André Toso ◽  
...  
Keyword(s):  

Author(s):  
Mhafuzul Islam ◽  
Mashrur Chowdhury ◽  
Hongda Li ◽  
Hongxin Hu

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.


Author(s):  
Hiroaki Nishino ◽  
Ryotaro Goto ◽  
Yuki Fukakusa ◽  
Jiaqing Lin ◽  
Tsuneo Kagawa ◽  
...  

Author(s):  
U Neureder

Many studies of mechanisms contributing to steering wheel nibble have been carried out in the past. This paper deals with some aspects that have not yet been studied, or those that have been presented by several authors but are deemed to be controversial. Firstly, an overview of stimulation sources (disturbance factors), and the significance these have with respect to steering nibble, is given. As an example of the controversial aspects of the problem, this paper deals with the assumption of dry friction in steering gear models and its conflict with the observed transfer of vibration caused by small (realistic) amounts of imbalance or tyre force variation. After modelling the steering gear resistance correctly, it is possible to identify, in the steering gear, a natural frequency that contributes reasonably to the nibble phenomenon. Based on this new model, a CAE study on parameter sensitivity, using the ‘design of experiments’ approach, is presented.


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