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...

Background: Human factors have the most significant effects on the occurrence of traffic accidents among the three factors of human, road, and vehicle. The driver is a significant traffic safety concern. Interventions that enhance driver’s safety are required. Risky behaviors of drivers on the road influence physical healthy and personality. The aim of this study is to report the protocol...

There is an increasing body of research investigating relationships between personality factors and crash involvement. This research estimates that personality factors account for about 10 to 20% of the variance in crashes. However, there are a number of limitations associated with this research. Most studies are cross-sectional or retrospective in design, are based on self-reported crashes or ...

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 ...

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...