scholarly journals Breathe with Touch: A Tactile Interface for Breathing Assistance System

2015 ◽  
pp. 45-52 ◽  
Author(s):  
Bin Yu ◽  
Loe Feijs ◽  
Mathias Funk ◽  
Jun Hu
Keyword(s):  
Assistance System

A Survey on Geograhical Targeting for Elderly Food Assistance System of Public Officers at Local Agencies

2019 ◽  
Vol 48 (9) ◽  
pp. 1007-1014
Author(s):  
Sung-Min Yook ◽  
Kirang Kim ◽  
Jae Eun Shim ◽  
Ji-Yun Hwang
Keyword(s):  
Food Assistance ◽  
Assistance System ◽  
Local Agencies

Evidence on Food Control System in Charitable Food Assistance System: A Systematic Scoping Review

2018 ◽  
Author(s):  
Sizwe Makhunga ◽  
Tivani P. Mashamba-Thompson ◽  
Mbuzeleni Hlongwa ◽  
Khumbulani W. Hlongwana
Keyword(s):  
Control System ◽  
Scoping Review ◽  
Food Assistance ◽  
Assistance System ◽  
Food Control ◽  
System A

Online Learning Resource Based on One ID Website for All Access (OIAA) as a Student Learning Assistance System

2020 ◽  
Author(s):  
Rizqi Fajar Pradipta ◽  
Frimha Purnamawati ◽  
Mohd Hanafi Mohd Yasin ◽  
Dimas Arif Dewantoro ◽  
Muchamad Irvan ◽  
...  
Keyword(s):  
Online Learning ◽  
Student Learning ◽  
Assistance System ◽  
Learning Resource ◽  
Learning Assistance

scholarly journals Experimental Validation on Intersection Turning Trajectory Prediction Method for Advanced Driver Assistance System Based on Triclothoidal Curve

2021 ◽  
Vol 11 (13) ◽  
pp. 5900
Author(s):  
Yohei Fujinami ◽  
Pongsathorn Raksincharoensak ◽  
Shunsaku Arita ◽  
Rei Kato
Keyword(s):  
Traffic Accidents ◽  
Computational Cost ◽  
Prediction Method ◽  
Smooth Curve ◽  
Assistance System ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Trajectory Prediction ◽  
Left Turn ◽  
Left Hand

Advanced driver assistance systems (ADAS) for crash avoidance, when making a right-turn in left-hand traffic or left-turn in right-hand traffic, are expected to further reduce the number of traffic accidents caused by automobiles. Accurate future trajectory prediction of an ego vehicle for risk prediction is important to activate the assistance system correctly. Our objectives are to propose a trajectory prediction method for ADAS for safe intersection turnings and to evaluate the effectiveness of the proposed prediction method. Our proposed curve generation method is capable of generating a smooth curve without discontinuities in the curvature. By incorporating the curve generation method into the vehicle trajectory prediction, the proposed method could simulate the actual driving path of human drivers at a low computational cost. The curve would be required to define positions, angles, and curvatures at its initial and terminal points. Driving experiments conducted at real city traffic intersections proved that the proposed method could predict the trajectory with a high degree of accuracy for various shapes and sizes of the intersections. This paper also describes a method to determine the terminal conditions of the curve generation method from intersection features. We set a hypothesis where the conditions can be defined individually from intersection geometry. From the hypothesis, a formula to determine the parameter was derived empirically from the driving experiments. Public road driving experiments indicated that the parameters for the trajectory prediction could be appropriately estimated by the obtained empirical formula.

scholarly journals Design and Implementation of Driver Assistance System using Machine Learning

2020 ◽  
Vol 994 ◽  
pp. 012006
Author(s):  
D. S. Bhargava ◽  
N. Shyam ◽  
K. Senthil Kumar ◽  
M. Wasim Raja ◽  
P Sivashankar.
Keyword(s):  
Machine Learning ◽  
Driver Assistance System ◽  
Assistance System ◽  
Driver Assistance ◽  
Design And Implementation
Sign in / Sign up

Export Citation Format

Share Document