A Brain Wave-Verified Driver Alert System for Vehicle Collision Avoidance

2021 ◽  
Vol 9 (1) ◽  
pp. 105-122
Author(s):  
Azim Eskandarian ◽  
◽  
Ce Zhang ◽  
Keyword(s):  
Driving Simulator ◽  
Visual Stimulation ◽  
Light Emitting Diode ◽  
Warning System ◽  
Alert System ◽  
Brain Wave ◽  
External Stimulation ◽  
Brain Signals ◽  
Vehicle Collision ◽  
Signal Processing Algorithms

Collision alert and avoidance systems (CAS) could help to minimize driver errors. They are instrumental as an advanced driver-assistance system (ADAS) when the vehicle is facing potential hazards. Developing effective ADAS/CAS, which provides alerts to the driver, requires a fundamental understanding of human sensory perception and response capabilities. This research explores the premise that external stimulation can effectively improve drivers’ reaction and response capabilities. Therefore this article proposes a light-emitting diode (LED)-based driver warning system to prevent potential collisions while evaluating novel signal processing algorithms to explore the correlation between driver brain signals and external visual stimulation. When the vehicle approaches emerging obstacles or potential hazards, an LED light box flashes to warn the driver through visual stimulation to avoid the collision through braking. Thirty (30) subjects completed a driving simulator experiment under different near-collision scenarios. The Steady-State Visually Evoked Potentials (SSVEP) of the drivers’ brain signals and their collision mitigation (control performance) data were analyzed to evaluate the LED warning system’s effectiveness. The results show that (1) The proposed modified canonical correlation analysis evaluation (CCA-EVA) algorithm can detect SSVEP responses with 4.68% higher accuracy than the Adaptive Kalman filter; (2) The proposed driver monitoring and alert system produce on average a 52% improvement in time to collision (TTC), 54% improvement in reaction distance (RD), and an overall 26% reduction in collision rate as compared to similar tests without the LED warning.

CONCEPTUAL MODEL OF THE SYSTEM OF SPECIAL TYPES OF TECHNICAL SUPPORT

2018 ◽  
pp. 135-139
Author(s):  
A. N. Mironov ◽  
V. V. Lisitskiy
Keyword(s):  
Conceptual Model ◽  
System Approach ◽  
Warning System ◽  
Theoretical Explanation ◽  
Technical Support ◽  
Technical System ◽  
The Other ◽  
Alert System ◽  
Empirical Basis ◽  
The Creation

In the article on set-theoretic level, developed a conceptual model of the system of special types of technical support for difficult organizational-technical system. The purpose of conceptualizing the creation of a system of interrelated and stemming from one of the other views on certain objects, phenomena, processes associated with the system of special types of technical support. In the development of applied concepts and principles of the methodology of system approach. The empirical basis for the development of the conceptual model has served many fixed factors obtained in the warning system and require formalization and theoretical explanation. The novelty of the model lies in the account of the effect of environment directly on the alert system. Therefore, in the conceptual model of the system of special types of technical support included directly in the conceptual model of the system of special types and conceptual model of the environment. Part of the conceptual model of the environment is included in the conceptual model of the enemy of nature and co-systems.

scholarly journals PENERAPAN LOGIKA FUZZY PADA SISTEM PERINGATAN JARAK AMAN SEPEDA MOTOR BERBASIS MIKROKONTROLER

2020 ◽  
Vol 3 (2) ◽  
pp. 114-119
Author(s):  
Samsudin Samsudin ◽  
Muhammad Ikhsan ◽  
Maya Juliana Ritonga
Keyword(s):  
Fuzzy Logic ◽  
Warning System ◽  
Process Control System ◽  
Road Accident ◽  
Alert System ◽  
Motorcycle Safety ◽  
Electrical Systems ◽  
Safety Distance ◽  
Safety Alert ◽  
The Brain

The research purpose to design a motorcycle safety alert system using ultrasonic sensors to detect objects within reach and using the microcontroller as the brain of the process control system so that it can be used to build electrical systems. The bike's safety-distance warning system uses fuzzy logic in the soft computing category, a method that could process uncertain, inaccurate, and less cost implemented data. With this system, it can help the community to reduce the number of a road accident to generate output in some conditions such as safe, carefully, and dangerous by using alarm warnings that can cause sound and LED as virtual and LCD notifications that can display distances in efficient and effective. Based on the results of the tests being done, the sensor system is accurate at 95.242% at 10 times the test.

Creating by Imagining: Use of Natural and Intuitive BCI in 3D CAD Modeling

2013 ◽  
Author(s):  
Sree Shankar S. ◽  
Anoop Verma ◽  
Rahul Rai
Keyword(s):  
3D Modeling ◽  
Signal Acquisition ◽  
Natural Interaction ◽  
Brain Wave ◽  
3D Cad ◽  
Facial Movements ◽  
Brain Signals ◽  
Computer Aided ◽  
Cad Models ◽  
Google Sketchup

Since its inception, computer aided 3D modeling has primarily relied on the Windows, Icons, Menus, Pointer (WIMP) user interface. WIMP has rarely been able to tap into the natural intuitiveness and imagination of the user which accompanies any design process. Brain-computer interface (BCI) is a novel modality that uses the brain signals of a user to enable natural and intuitive interaction with an external device. The BCI’s potential to become an important modality of natural interaction for 3D modeling is almost limitless and unexplored. In theory, using BCI one can create any 3D model by simply thinking about it. This paper presents a basic framework for using BCI as an interface for computer aided 3D modeling. This framework involves the task of recording and recognizing electroencephalogram (EEG) brain wave patterns and electromyogram (EMG) signals corresponding to facial movements. The recognized EEG/EMG brain signals and associated keystrokes are used to activate/control different commands of a CAD package. Eight sample CAD models are created using the Emotiv EEG head set based BCI interface and Google SketchUp and presented to demonstrate the efficacy of the developed system based on the framework. To further exhibit BCI’s usability, human factor studies have been carried out on subjects from different backgrounds. Based on preliminary results, it is concluded that EEG/EMG based BCI is suitable for computer aided 3D modeling purposes. Issues in signal acquisition, system flexibility, integration with other modalities, and data collection are also discussed.

CO Pollution Warning System for Indoor Parking Area Using FPGA

2013 ◽  
Vol 2 (2) ◽  
pp. 64
Author(s):  
Prima Dewi Purnamasari ◽  
Evan G. Sumbayak ◽  
Vicky Dwi Kurniawan ◽  
RR. Wulan Apriliyanti
Keyword(s):  
Air Pollution ◽  
Response Time ◽  
Embedded System ◽  
User Satisfaction ◽  
Characteristic Curve ◽  
Warning System ◽  
Alert System ◽  
Common Cause ◽  
Scientific Nature ◽  
Potential Area

From some compounds used as parameters in air pollution-such as O3, Particulate Materials, CO, NO2, SO2 and Pb-CO is the most common cause of poisoning accidents. Indoor parking area is one sample of potential area for CO pollution. However, according to the scientific nature of CO-tasteless, colorless, and odorless-people exposed to CO are usually not aware that s/he exposed to dangerous levels of CO. This research aimed to make a prototype of an embedded system that can monitor air pollution, give an effective warning and it should be affordable. The prototype of CO air pollution alert system has been successfully built using FPGA Xilinx Spartan 3E as the major component. Sensor Hanwei MQ7 used in this prototype has been tested in a simulation box using cigarette smoke as CO pollutant and the reading result has met the characteristic curve in the datasheet. The system interface has met user satisfaction with MOS value 4.31 from 5 scales. Based on the response time testing, we conclude that FPGA is suitable to be used in a system that performs fast parallel processing based on logical actions from the input given.

scholarly journals Cannabis-impaired driving and Canadian youth

2020 ◽  
Vol 25 (Supplement_1) ◽  
pp. S21-S25
Author(s):  
Jeff R Brubacher ◽  
Herbert Chan ◽  
John A Staples
Keyword(s):  
Traffic Safety ◽  
Driving Simulator ◽  
Motor Vehicle ◽  
Motor Vehicle Crash ◽  
Cannabis Use ◽  
Crash Risk ◽  
Delayed Information ◽  
Canadian Youth ◽  
Vehicle Collision ◽  
Significant Impairment

Abstract Acute cannabis use results in inattention, delayed information processing, impaired coordination, and slowed reaction time. Driving simulator studies and epidemiologic analyses suggest that cannabis use increases motor vehicle crash risk. How much concern should we have regarding cannabis associated motor vehicle collision risks among younger drivers? This article summarizes why young, inexperienced drivers may be at a particularly high risk of crashing after using cannabis. We describe the epidemiology of cannabis use among younger drivers, why combining cannabis with alcohol causes significant impairment and why cannabis edibles may pose a heightened risk to traffic safety. We provide recommendations for clinicians counselling younger drivers about cannabis use and driving.

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