Enhancing Navigation Information with Tactile Output Embedded into the Steering Wheel

<pre>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.</pre><pre> </pre>

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RESEARCH ON VIBRATION STEERING WHEEL VEHICLE BAJA UNIVATES TEAM

10.26678/abcm.cobem2017.cob17-0594 ◽

2017 ◽

Author(s):

Guilherme Cortelini da Rosa ◽

Carlos Henrique Lagemann ◽

Rafael Crespo Izquierdo ◽

Julio Damyan Imbriaco Silveira ◽

Marcelo André Toso ◽

...

Keyword(s):

Steering Wheel

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STATISTICAL ESTIMATION OF NAVIGATION INFORMATION RELIABILITY TRANSMITTED FROM THE AIRCRAFT BOARD UNDER AUTOMATIC DEPENDENT SUPERVISION

Reliability and Quality of Complex Systems ◽

10.21685/2307-4205-2016-4-7 ◽

2016 ◽

Author(s):

D.A. Zatuchny ◽

Keyword(s):

Statistical Estimation ◽

Information Reliability ◽

Navigation Information

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Vision-Based Navigation of Autonomous Vehicles in Roadway Environments with Unexpected Hazards

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198119855606 ◽

2019 ◽

Vol 2673 (12) ◽

pp. 494-507 ◽

Cited By ~ 1

Author(s):

Mhafuzul Islam ◽

Mashrur Chowdhury ◽

Hongda Li ◽

Hongxin Hu

Keyword(s):

Object Detection ◽

Autonomous Vehicles ◽

Autonomous Vehicle ◽

Semantic Segmentation ◽

Steering Wheel ◽

Potential Hazard ◽

Driving System ◽

Hazardous Object ◽

Vision Based Navigation ◽

Navigational System

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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Investigation into steering wheel nibble

Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering ◽

10.1243/0954407021529101 ◽

2002 ◽

Vol 216 (4) ◽

pp. 267-277 ◽

Cited By ~ 13

Author(s):

U Neureder

Keyword(s):

Design Of Experiments ◽

Natural Frequency ◽

Dry Friction ◽

Parameter Sensitivity ◽

Steering Wheel ◽

New Model ◽

The Past ◽

Force Variation

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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Design of Steering Wheel and Magnetic Shift Mechanism of FSAE Racing Car

2021 7th International Symposium on Mechatronics and Industrial Informatics (ISMII) ◽

10.1109/ismii52409.2021.00037 ◽

2021 ◽

Author(s):

Zhengming Wang ◽

Siran Wang

Keyword(s):

Steering Wheel

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Lane Departure Warning Estimation Using Yaw Acceleration

Open Engineering ◽

10.1515/eng-2021-0008 ◽

2020 ◽

Vol 11 (1) ◽

pp. 102-111

Author(s):

Em Poh Ping ◽

J. Hossen ◽

Wong Eng Kiong

Keyword(s):

Vehicle Dynamics ◽

Dynamic Responses ◽

Dynamics Simulation ◽

Steering Wheel ◽

Mathematical Representation ◽

Vehicle Speed ◽

Lane Departure Warning ◽

Model Based ◽

Lane Departure ◽

Proposed Model

AbstractLane departure collisions have contributed to the traffic accidents that cause millions of injuries and tens of thousands of casualties per year worldwide. Due to vision-based lane departure warning limitation from environmental conditions that affecting system performance, a model-based vehicle dynamics framework is proposed for estimating the lane departure event by using vehicle dynamics responses. The model-based vehicle dynamics framework mainly consists of a mathematical representation of 9-degree of freedom system, which permitted to pitch, roll, and yaw as well as to move in lateral and longitudinal directions with each tire allowed to rotate on its axle axis. The proposed model-based vehicle dynamics framework is created with a ride model, Calspan tire model, handling model, slip angle, and longitudinal slip subsystems. The vehicle speed and steering wheel angle datasets are used as the input in vehicle dynamics simulation for predicting lane departure event. Among the simulated vehicle dynamic responses, the yaw acceleration response is observed to provide earlier insight in predicting the future lane departure event compared to other vehicle dynamics responses. The proposed model-based vehicle dynamics framework had shown the effectiveness in estimating lane departure using steering wheel angle and vehicle speed inputs.

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