Study on Driver's Unsafe Gaze Behavior Detection Technology

A system for detecting and evaluating drivers gaze behavior was proposed. A system for recognizing the drivers unsafe gaze behavior was established using multi-level information and fusion decision method as well. The driving environment and condition is complex as well as the gaze behavior characteristics, and given that, a solution consists of patten classification and the multi-information decision-level fusion were put forward to estimate the different kind model of the driver's gaze behavior. In order to test the proposed strategies,the real time driver's gaze behavior detection system was established. The T characteristic curve proposed through the abnormal behavior parameters of the transverse width between the eyes and the vertical distance between mouth and the midpoint of two eyes, combined with the driver's eyelid closure and the proportion and location characteristics of iris - sclera were studied to get the characterization of the drivers gaze status information. The simulation results indicate that the adaptability and accuracy as well as the intelligent level is significantly improved by using the pattern classification and decision-making technology through multi-source information fusion.

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Study of Driver's Gaze Behavior Recognition

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.488-489.1011 ◽

2014 ◽

Vol 488-489 ◽

pp. 1011-1014 ◽

Cited By ~ 1

Author(s):

Peng Wang ◽

Zhi Qiang Liu

Keyword(s):

Feature Fusion ◽

Characteristic Curve ◽

Support Vector ◽

Gaze Behavior ◽

Eyelid Closure ◽

The Gaze ◽

Behavior Characteristics ◽

Level Fusion ◽

Theory Technique

The reliability and accuracy of driver gaze behavior detection was improved by the multi-dimensional feature fusion method. In view of the effects of the complexity of the driving environment, a variety of working conditions and the diversity of gaze behavior characteristics,with support vector machine (SVM) theory technique, the multi-dimensional feature decision-level fusion was proposed to estimate the different kind model of the driver's gaze behavior. The results show that the T characteristic curve proposed by the gaze behavior parameters of the transverse width between the eyes and the vertical distance between mouth and the midpoint of two eyes, combined with the driver's eyelid closure and the proportion and location characteristics of iris-sclera were studied to get the characterization of the driver gaze status. The simulation results indicate that the adaptability and accuracy as well as the intelligence level of the bad fixation characterization information screening are significantly improved by using the pattern classification and decision technology of multi-dimensional feature fusion.

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GPS-Spoofing Attack Detection Technology for UAVs Based on Kullback–Leibler Divergence

Drones ◽

10.3390/drones6010008 ◽

2021 ◽

Vol 6 (1) ◽

pp. 8

Author(s):

Elena Basan ◽

Alexandr Basan ◽

Alexey Nekrasov ◽

Colin Fidge ◽

Nikita Sushkin ◽

...

Keyword(s):

Cyber Security ◽

Detection System ◽

Attack Detection ◽

Abnormal Behavior ◽

Mathematical Methods ◽

Training Time ◽

Detection Technology ◽

Normal Behavior ◽

Leibler Divergence ◽

Aerial Vehicle

Here, we developed a method for detecting cyber security attacks aimed at spoofing the Global Positioning System (GPS) signal of an Unmanned Aerial Vehicle (UAV). Most methods for detecting UAV anomalies indicative of an attack use machine learning or other such methods that compare normal behavior with abnormal behavior. Such approaches require large amounts of data and significant “training” time to prepare and implement the system. Instead, we consider a new approach based on other mathematical methods for detecting UAV anomalies without the need to first collect a large amount of data and describe normal behavior patterns. Doing so can simplify the process of creating an anomaly detection system, which can further facilitate easier implementation of intrusion detection systems in UAVs. This article presents issues related to ensuring the information security of UAVs. Development of the GPS spoofing detection method for UAVs is then described, based on a preliminary study that made it possible to form a mathematical apparatus for solving the problem. We then explain the necessary analysis of parameters and methods of data normalization, and the analysis of the Kullback—Leibler divergence measure needed to detect anomalies in UAV systems.

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