The quantitative measurement of driver behavior has been central to much of the systematic research underlying highway safety issues during the past forty years. It has contributed to the way in which we design roads, vehicles, training programs, signs and markings, and intelligent transportation systems. Yet the methods we use to conduct driver behavior experiments may result in a disconnect w...

A method for detecting drivers’ intentions is essential to facilitate operating mode transitions between driver and driver assistance systems. We propose a driver behavior recognition method using Hidden Markov Models (HMMs) to characterize and detect driving maneuvers and place it in the framework of a cognitive model of human behavior. HMM-based steering behavior models for emergency and norm...

Driver heterogeneity in travel behavior has repeatedly been cited in the literature as a limitation that needs to be addressed. In this work, driver heterogeneity is addressed from four different perspectives. First, driver heterogeneity is addressed by models of driver perceptions of travel conditions: travel distance, time, and speed. Second, it is addressed from the perspective of driver lea...

Nowadays, AI has many applications in everyday human activities such as exercise, eating, sleeping, and automobile driving. Tech companies can apply to identify individual behaviors (e.g., walking, driving), analyze them, offer personalized feedback help individuals make improvements accordingly. While offering is more beneficial for drivers, most smart driver systems the current market do not ...

Drivers' behavior is one of the most important factors in traffic safety. Understanding of this issue and its effective factors can be helpful to reduce the influences of human factors on traffic accidents. The objective of this study is to apply Manchester driver behavior questionnaire (DBQ) to a group of drivers who have overtaken on two-lane rural roads and to analyze their behavior. Also, t...

YU, ZHANG. An Empirical Assessment of the Role of Driver Motivation and Emotion State, and Driving Conditions in Perceived Safety Margins. (Under the direction of Dr. David B. Kaber). Models of motivation in driver behavior have been developed to predict driver performance under different conditions. Among existing models, Näätänen and Summala’s multi-dimensional threshold model (1976) was sele...

Intelligent Driver Model (IDM) is a well-known microscopic model of traffic flow within the traffic engineering societies. While it is a powerful technique for modeling traffic flows, the Intelligent Driver Model lacks the potential of accommodating the notion of drivers’ heterogeneous behavior whenever they are on roads. Concerning the above mentioned, this paper takes the lane to recognize th...