scholarly journals Detecting Driver Drowsiness with Multi-Sensor Data Fusion Combined with Machine Learning

2021 ◽  
Author(s):  
Hovannes Kulhandjian
Keyword(s):  
Machine Learning ◽  
Data Fusion ◽  
Traffic Safety ◽  
Head Movement ◽  
High Reliability ◽  
Research Work ◽  
Visual Surveillance ◽  
Sensor Data ◽  
Highway Traffic ◽  
National Highway Traffic Safety

In this research work, we develop a drowsy driver detection system through the application of visual and radar sensors combined with machine learning. The system concept was derived from the desire to achieve a high level of driver safety through the prevention of potentially fatal accidents involving drowsy drivers. According to the National Highway Traffic Safety Administration, drowsy driving resulted in 50,000 injuries across 91,000 police-reported accidents, and a death toll of nearly 800 in 2017. The objective of this research work is to provide a working prototype of Advanced Driver Assistance Systems that can be installed in present-day vehicles. By integrating two modes of visual surveillance to examine a biometric expression of drowsiness, a camera and a micro-Doppler radar sensor, our system offers high reliability over 95% in the accuracy of its drowsy driver detection capabilities. The camera is used to monitor the driver’s eyes, mouth and head movement and recognize when a discrepancy occurs in the driver's blinking pattern, yawning incidence, and/or head drop, thereby signaling that the driver may be experiencing fatigue or drowsiness. The micro-Doppler sensor allows the driver's head movement to be captured both during the day and at night. Through data fusion and deep learning, the ability to quickly analyze and classify a driver's behavior under various conditions such as lighting, pose-variation, and facial expression in a real-time monitoring system is achieved.

A Commented Synopsis of the Report of the Committee for the National Tire Efficiency Study4

2007 ◽  
Vol 35 (2) ◽  
pp. 70-93
Author(s):  
Marion G. Pottinger ◽  
Joseph D. Walter ◽  
John D. Eagleburger
Keyword(s):  
Traffic Safety ◽  
Rolling Resistance ◽  
The United States ◽  
Final Report ◽  
Highway Traffic ◽  
National Academy Of Sciences ◽  
Scrap Tire ◽  
National Highway Traffic Safety ◽  
Fuel Savings ◽  
Time Required

Abstract The Congress of the United States petitioned the Transportation Research Board of the National Academy of Sciences to study replacement passenger car tire rolling resistance in 2005 with funding from the National Highway Traffic Safety Administration. The study was initiated to assess the potential for reduction in replacement tire rolling resistance to yield fuel savings. The time required to realize these savings is less than the time required for automotive and light truck fleet replacement. Congress recognized that other factors besides fuel savings had to be considered if the committee’s advice was to be a reasonable guide for public policy. Therefore, the study simultaneously considered the effect of potential rolling resistance reductions in replacement tires on fuel consumption, wear life, scrap tire generation, traffic safety, and consumer spending for tires and fuel. This paper summarizes the committee’s report issued in 2006. The authors, who were members of the multidisciplinary committee, also provide comments regarding technical difficulties encountered in the committee’s work and ideas for alleviating these difficulties in further studies of this kind. The authors’ comments are clearly differentiated so that these comments will not be confused with findings, conclusions, and recommendations developed by the committee and contained in its final report.

Distribution of Speed and Acceleration on a Tread Wear Course

1981 ◽  
Vol 9 (1) ◽  
pp. 19-25 ◽  
Author(s):  
G. S. Ludwig ◽  
F. C. Brenner
Keyword(s):  
Traffic Safety ◽  
Highway Traffic ◽  
National Highway Traffic Safety ◽  
Level Crossings ◽  
Acceleration Level ◽  
Longitudinal Acceleration ◽  
Acceleration Data ◽  
National Highway

Abstract Belted bias and radial Course Monitoring Tires were run over the National Highway Traffic Safety Administration tread wear course at San Angelo on a vehicle instrumented to measure lateral and longitudinal accelerations, speed, and number of wheel rotations. The data were recorded as histograms. The distribution of speed, the distributions of lateral and longitudinal acceleration, and the number of acceleration level crossings are given. Acceleration data for segments of the course are also given.

The Analysis and Assessment of Test Method for Flammability of an Automotive Headlining Fabric

2011 ◽  
Vol 332-334 ◽  
pp. 1162-1166
Author(s):  
Zhuo Zhang ◽  
Ying Qing Liu ◽  
Zhong Hai Ren ◽  
Jia Zhuang Ma ◽  
Hu Shui Ye
Keyword(s):  
Traffic Safety ◽  
Test Method ◽  
Highway Traffic ◽  
National Highway Traffic Safety ◽  
Test Standards ◽  
Foreign Countries ◽  
Mandatory Standard ◽  
Testing Standards ◽  
Interior Textile ◽  
Automotive Interior

The flammability is one of the most important features about safety for automotive interior material. This paper summarized the testing standards for flammability performed testing on a type of interior textile material made by one of domestic manufacturers, in accordance with the Chart 571.302 Standard No. 302 of the National Highway Traffic Safety Administration of U.S. The complete introduction of national mandatory standard of China in flammability of interior material was introduced and domestic test standards of flammability with those of foreign countries all over world were compared. Finally, this paper proposed possible and would-be necessary parameters based on comprehensiveness of this kind of test due to safer requirement in future.

scholarly journals 40. MVCs: Five Most Frequent Sources of Injury

1996 ◽  
Vol 11 (S2) ◽  
pp. S41-S41
Author(s):  
John E. Gough ◽  
Richard C. Hunt
Keyword(s):  
Traffic Safety ◽  
Motor Vehicles ◽  
Highway Traffic ◽  
Sampling System ◽  
National Highway Traffic Safety ◽  
National Highway

Purpose: To determine the most frequent sources of injuries from the interior of motor vehicles involved in crashes.Methods: We searched the National Highway Traffic Safety Administration's National Accident Sampling System to determine the most frequent sources of injuries. This database includes sources of injuries resulting from crashes from January 1, 1991 to December 31, 1992.

Use Cases for Assessing, Testing, and Validating the Human Machine Interface of Automated Driving Systems

2018 ◽  
Vol 62 (1) ◽  
pp. 1873-1877 ◽  
Author(s):  
Frederik Naujoks ◽  
Sebastian Hergeth ◽  
Katharina Wiedemann ◽  
Nadja Schömig ◽  
Andreas Keinath
Keyword(s):  
Traffic Safety ◽  
Test Methods ◽  
Use Cases ◽  
Highway Traffic ◽  
Automated Vehicles ◽  
Automated Driving ◽  
National Highway Traffic Safety ◽  
Minimum Requirements ◽  
Machine Interface ◽  
Increasing Demand

Reflecting the increasing demand for harmonization of human machine interfaces (HMI) of automated vehicles, different taxonomies of use cases for investigating automated driving systems (ADS) have been proposed. Existing taxonomies tend to serve specific purposes such as categorizing transitions between automation modes; however, they cannot be generalized to different systems or combinations of systems. In particular, there is no exhaustive set of use cases that allows entities to assess and validate the HMI of a given ADS that takes into account all possible system modes and transitions. The present paper describes a newly developed framework based on combinatorics of SAE (Society of Automotive Engineers) automation levels that incorporates a comprehensive taxonomy of use cases required for the assessment and validation of ADS HMIs. This forms a much-needed basis for test methods required to verify whether an HMI meets minimum requirements such as those outlined in the National Highway Traffic Safety Administration’s Federal Automated Vehicles policy.

scholarly journals An Efficient SMOTE-Based Deep Learning Model for Heart Attack Prediction

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Muhammad Waqar ◽  
Hassan Dawood ◽  
Hussain Dawood ◽  
Nadeem Majeed ◽  
Ameen Banjar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Heart Attack ◽  
High Reliability ◽  
Learning Algorithms ◽  
Research Work ◽  
Machine Learning Algorithms ◽  
Feature Engineering ◽  
Unequal Distribution ◽  
The Given

Cardiac disease treatments are often being subjected to the acquisition and analysis of vast quantity of digital cardiac data. These data can be utilized for various beneficial purposes. These data’s utilization becomes more important when we are dealing with critical diseases like a heart attack where patient life is often at stake. Machine learning and deep learning are two famous techniques that are helping in making the raw data useful. Some of the biggest problems that arise from the usage of the aforementioned techniques are massive resource utilization, extensive data preprocessing, need for features engineering, and ensuring reliability in classification results. The proposed research work presents a cost-effective solution to predict heart attack with high accuracy and reliability. It uses a UCI dataset to predict the heart attack via various machine learning algorithms without the involvement of any feature engineering. Moreover, the given dataset has an unequal distribution of positive and negative classes which can reduce performance. The proposed work uses a synthetic minority oversampling technique (SMOTE) to handle given imbalance data. The proposed system discarded the need of feature engineering for the classification of the given dataset. This led to an efficient solution as feature engineering often proves to be a costly process. The results show that among all machine learning algorithms, SMOTE-based artificial neural network when tuned properly outperformed all other models and many existing systems. The high reliability of the proposed system ensures that it can be effectively used in the prediction of the heart attack.

scholarly journals Forensic Engineering Applications of the G-DaTA?V™ System of Equations to Real-World Collisions

2015 ◽  
Vol 32 (2) ◽  
Author(s):  
Jerry S. Ogden
Keyword(s):  
Traffic Safety ◽  
Real World ◽  
System Of Equations ◽  
Collision Dynamics ◽  
Highway Traffic ◽  
Sampling System ◽  
Velocity Change ◽  
National Highway Traffic Safety ◽  
Light Trucks ◽  
Total Velocity

Analysis of vehicle deformation from impacts largely relies upon A and B stiffness coefficients for vehicle structures in order to approximate the velocity change and accelerations produced by an impact. While frontal impact stiffness factors for passenger vehicles, light trucks, vans, and sport utility vehicles are relatively prevalent for modern vehicles, stiffness factors for rear and side structures, as well as heavy vehicles, buses, recreational vehicles, trailers, motorcycles, and even objects, are essentially non-existent. This paper presents the application of the Generalized Deformation and Total Velocity Change Analysis to real-world collision events (G-DaTA?V™ System of Equations) as developed by this author. The focus of this paper addresses the relative precision and accuracy of the G-DaTA?V™ System of Equations for determining the total velocity change for oblique and/or offset vehicle-to-vehicle collisions involving light trucks and sport utility vehicles, which are largely under-represented with modern vehicle A and B stiffness values for side and rear surfaces. The previous paper presented by this author to the Academy addressed the relative accuracy and precision of the G-DaTA?V™ System of Equations as they relate to a first validation using the RICSAC-staged collision database. As a secondary and more comprehensive validation process, the G-DaTA?V™ System of Equations will be applied to real-world collision data obtained through the National Automotive Sampling System (NASS), which provides the National Highway Traffic Safety Administration (NHTSA) with a comprehensive compilation of real-world collision events representing a broad-based collection of collision configurations from across the country. This data represents a reusable source of information that was collected using standardized field techniques implemented by NASS-trained field technicians. Through using a “core set of crash data components,” NASS has demonstrated its utility and applicability to a vast array of statistical and analytical studies regarding traffic safety and vehicle collision dynamics.

Sign in / Sign up

Export Citation Format

Share Document