Texting, Drugs and Driving: A “Triple Threat” To Driving Safety?

2016 ◽  
Vol 06 (02) ◽  
Author(s):  
Ammar A Blanchette A ◽  
Sale D LaForest D
Keyword(s):  
Driving Safety ◽  
Drugs And Driving ◽  
Triple Threat

Drugs and driving

JAMA ◽  
1966 ◽  
Vol 195 (5) ◽  
pp. 376-379 ◽  
Author(s):  
J. G. Perry
Keyword(s):  
Drugs And Driving

Got the Results, Now What Do You Do?

2012 ◽  
Vol 2 (1) ◽  
pp. 25-33 ◽  
Author(s):  
Michaela Heese
Keyword(s):  
Safety Culture ◽  
Success Factors ◽  
Service Providers ◽  
Transformation Process ◽  
Driving Safety ◽  
Critical Success ◽  
Culture Transformation ◽  
Survey Results ◽  
Air Navigation Services ◽  
Navigation Service

Members of the Civil Air Navigation Services Organisation have committed themselves to measure and improve safety culture within their organizations by 2013 ( CANSO, 2010 ). This paper attempts to offer support to air navigation service providers that have already implemented a standardized safety culture survey approach, in the process of transforming their safety culture based on existing survey results. First, an overview of the state of the art with respect to safety culture is presented. Then the application of the CANSO safety culture model from theory into practice is demonstrated based on four selected case studies. Finally, a summary of practical examples for driving safety culture change is provided, and critical success factors supporting the safety culture transformation process are discussed.

Triple Threat For Hepatitis C Patients

2007 ◽  
Vol 37 (1) ◽  
pp. 20
Author(s):  
PATRICE WENDLING
Keyword(s):  
Hepatitis C ◽  
Triple Threat

Lampu Sein Helm Sepeda Berbasis Voice Recognition

2019 ◽  
Vol 11 (01) ◽  
pp. 20-25
Author(s):  
Indra Saputra ◽  
Parulian Silalahi ◽  
Bayu Cahyawan ◽  
Imam Akbar
Keyword(s):  
Voice Recognition ◽  
Driving Safety ◽  
Bicycle Helmet ◽  
Turn Signal ◽  
Voice Command ◽  
The Voice ◽  
Indicator Signal

Bicycles are not equipped with the turn signal. For driving safety, a bicycle helmet with a turn signal is designed with voice rrecognition. It is using the Arduino Nano as a controller to control the ON and OFF of turn signal lights with voice commands. This device uses a Voice Recognition sensor and microphone that placed on a bicycle helmet. When the voice command is mentioned in the microphone, the Voice Recognition sensor will detect the command specified, the sensor will automatically read and send a signal to Arduino, then the turn signal will light up as instructed, the Arduino on the helmet will send an indicator signal via the Bluetooth Module. The device is able to detect sound with a percentage of 80%. The tool can work with a distance of <2 meters with noise <71 db.

Low Reflection Automotive Display for Driving Safety

2019 ◽  
pp. 893
Author(s):  
Qian Li ◽  
Bing Zhang ◽  
Puyu Qi ◽  
Cuicui Liang ◽  
Zhiqiang Wang ◽  
...  
Keyword(s):  
Driving Safety

scholarly journals Application of Artificial Neural Networks to Streamline the Process of Adaptive Cruise Control

2021 ◽  
Vol 13 (8) ◽  
pp. 4572
Author(s):  
Jiří David ◽  
Pavel Brom ◽  
František Starý ◽  
Josef Bradáč ◽  
Vojtěch Dynybyl
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Adaptive Cruise Control ◽  
Control Unit ◽  
Driving Safety ◽  
Model Parameters ◽  
Cruise Control ◽  
Artificial Neural ◽  
Dynamic Obstacle ◽  
Braking Process

This article deals with the use of neural networks for estimation of deceleration model parameters for the adaptive cruise control unit. The article describes the basic functionality of adaptive cruise control and creates a mathematical model of braking, which is one of the basic functions of adaptive cruise control. Furthermore, an analysis of the influences acting in the braking process is performed, the most significant of which are used in the design of deceleration prediction for the adaptive cruise control unit using neural networks. Such a connection using artificial neural networks using modern sensors can be another step towards full vehicle autonomy. The advantage of this approach is the original use of neural networks, which refines the determination of the deceleration value of the vehicle in front of a static or dynamic obstacle, while including a number of influences that affect the braking process and thus increase driving safety.

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