Driver Behavior Model Based on Ontology for Intelligent Transportation Systems

2015 ◽  
Author(s):  
Susel Fernandez ◽  
Takayuki Ito
Keyword(s):  
Intelligent Transportation Systems ◽  
Driver Behavior ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Behavior Model ◽  
Model Based

Methodology for Capturing Driver Eye Glance Behavior during In-Vehicle Secondary Tasks

2005 ◽  
Vol 1937 (1) ◽  
pp. 61-65 ◽  
Author(s):  
David L. Smith ◽  
James Chang ◽  
Richard Glassco ◽  
James Foley ◽  
Daniel Cohen
Keyword(s):  
Eye Movements ◽  
Visual Attention ◽  
Intelligent Transportation Systems ◽  
Driver Behavior ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Video Data ◽  
Visual Task ◽  
Secondary Tasks

Driving is a visual task; any driver behavior that takes the driver's visual attention from the driving environment is likely to increase the risk of a crash. Many new intelligent transportation systems telematics applications are now being installed in cars, and some of these may result in substantial degradation of visual attention. Accurately capturing driver eye glance behavior during the evaluation of these devices is critical to assessing the safety impact of engaging in secondary tasks while driving. The development of a methodology is reported; it reliably determines on-road and off-road eye glance duration by using video data collected during highway driving evaluation trials while drivers were engaged with secondary tasks. Human raters determined glance location and duration by detailed coding of the driver's eye movements on the videos. The raters agreed most of the time, but sufficient disagreement suggested that using the consensus of multiple raters significantly improved the reliability of the resulting eye glance data.

scholarly journals Driver Rating: a mobile application to evaluate driver behavior

2021 ◽  
Vol 2 (2) ◽  
pp. 1147-1160
Author(s):  
Nielson S. Trindade ◽  
Artur H. Kronbauer ◽  
Helder G. Aragão ◽  
Jorge Campos
Keyword(s):  
Carbon Dioxide ◽  
Urban Environment ◽  
Fuel Consumption ◽  
Intelligent Transportation Systems ◽  
Mobile Application ◽  
Driver Behavior ◽  
Carbon Dioxide Emission ◽  
Intelligent Transportation ◽  
Transportation Systems

The combination of data from sensors embedded in vehicles and smartphones promises to generate great innovations in intelligent transportation systems. This article presents Driver Rating, a mobile application to evaluate the behavior of drivers based on the data gathered from vehicles´ and smartphones´ sensors. The Driver Rating application analyzes five variables (fuel consumption, carbon dioxide emission, speed, longitudinal acceleration, and transverse acceleration) to evaluate driver´s behaviors while driving. To test the Driver Rating application and identify its potentialities, an experiment was carried out on an urban environment, showing promising results regarding the classification of drivers’ behavior.

Model for Situation Awareness and Driving: Application to Analysis and Research for Intelligent Transportation Systems

2001 ◽  
Vol 1779 (1) ◽  
pp. 26-32 ◽  
Author(s):  
Michael L. Matthews ◽  
David J. Bryant ◽  
Robert D. G. Webb ◽  
Joanne L. Harbluk
Keyword(s):  
Situation Awareness ◽  
Intelligent Transportation Systems ◽  
Nuclear Power ◽  
Driver Behavior ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Nuclear Power Generation ◽  
Strategic Decisions ◽  
Driver Performance ◽  
Underlying Processes

The concept of situation awareness (SA)—applied broadly over the last decade to human factors issues in aviation, nuclear power generation, and military combat systems—has only recently been introduced to the analysis of driver behavior. In a driving context, SA involves spatial, temporal, goal, and system awareness. These aspects of SA have been integrated into a goal-oriented model of driver behavior that encompasses strategic, tactical, and operational goals of driving. Maintenance of appropriate SA for each type of goal is based on three underlying processes: perception, comprehension of disparate information, and projection and prediction. The model can be used as a basis for understanding the possible impact of new generations of intelligent transportation systems (ITSs) on driver performance. The model allows ITSs to be analyzed for how they are likely to enhance or impair a driver’s performance in pursuit of each type of driving goal. The model may provide a way to determine how an ITS supports or interferes with the required SA to meet a driving goal (e.g., an onboard navigation system that assists strategic decisions).

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