scholarly journals Driver Assistance System With a Dual Control Scheme: Effectiveness of Identifying Driver Drowsiness and Preventing Lane Departure Accidents

2016 ◽  
Vol 46 (5) ◽  
pp. 660-671 ◽  
Author(s):  
Yuichi Saito ◽  
Makoto Itoh ◽  
Toshiyuki Inagaki
Keyword(s):  
Driver Assistance System ◽  
Assistance System ◽  
Dual Control ◽  
Driver Assistance ◽  
Driver Drowsiness ◽  
Lane Departure ◽  
Control Scheme

Driver assistance system for lane departure avoidance by steering and differential braking

2010 ◽  
Vol 43 (7) ◽  
pp. 471-476 ◽  
Author(s):  
Nicoleta Minoiu Enache ◽  
Saïd Mammar ◽  
Sébastien Glaser ◽  
Benoit Lusetti
Keyword(s):  
Driver Assistance System ◽  
Assistance System ◽  
Driver Assistance ◽  
Lane Departure

Hardware Simulation of Active Lane Keeping Assist Based on Fuzzy Logic

2017 ◽  
Vol 5 (2) ◽  
pp. 321 ◽  
Author(s):  
Noor Cholis Basjaruddin ◽  
Kuspriyanto Kuspriyanto ◽  
Didin Saefudin ◽  
Alditama Rachman
Keyword(s):  
Fuzzy Logic ◽  
Real Time ◽  
Driver Assistance System ◽  
Assistance System ◽  
Advanced Driver Assistance System ◽  
Toll Roads ◽  
Test Results ◽  
Driver Assistance ◽  
Lane Departure ◽  
Lane Keeping

Accidents due to cross the lane (lane departure crash) of achieving 19% of all accidents. These accidents mostly caused because the driver is not concentrating or dozing. A device that can help the driver so that the vehicle does not exit lanes known as Active Lane Keeping Assist (ALKA). ALKA is a subsystem of Advanced Driver Assistance System (ADAS) and is now used in several brands of cars namely Audi, BMW, and Ford. ALKA useful to help the driver, especially on roads with monotonous situations such as toll roads. This paper presents the development of fuzzy logic-based algorithm for ALKA. A battery toy car is used as a hardware simulation to test the algorithm in real time situation. The test results at three different speeds are slow, medium,   and   high   indicate   that   the   algorithm   can   work according to design.

scholarly journals Driver Drowsiness Detection System

2021 ◽  
Vol 9 (VII) ◽  
pp. 1226-1231
Author(s):  
Kiranmayee V
Keyword(s):  
Visual Information ◽  
Driver Assistance System ◽  
Detection System ◽  
Assistance System ◽  
Advanced Driver Assistance System ◽  
Driver Assistance ◽  
Drowsiness Detection ◽  
Driver Drowsiness ◽  
Drugs And Alcohol ◽  
Eye Closure

Drowsiness of drivers are among the critical reasons for accidents. This can be a relatively smaller number still, as among the multiple causes that can lead to an accident. Drowsiness, in general, is not easy to measure unlike drugs and alcohol, which have tests and indicators that are available easily. In this paper, we are presenting a module for Advanced Driver Assistance System (ADAS) to reduce drowsiness related accidents. The system deals with automatic driver drowsiness detection based on visual information. We propose an algorithm to track, analyze and locate both the drivers eyes and face to measure PERCLOS, a scientifically supported measure of drowsiness asso- ciated with slow eye closure.

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
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