Learning from human driver’s strategies for undertaking complex traffic scenarios has the potential to improve decision-making methods designing ADAS systems, as well design self-driving rules automated vehicles. This paper proposes a human-like algorithm built up drivers experiential naturalistic driving. The approach of this work consists exploring two main techniques. Firstly, use “think alo...

In this work, the design and analysis of new Level Shifter with Gate Driver for Li-Ion battery charger is proposed for high speed and low area in 180nm CMOS technology. The new proposed level shifter is used to raise the voltage level and significantly reduces transfer delay 1.3ns (transfer delay of conventional level shifter) to 0.15ns with the same input signal. Also, the level shifter with g...

Successful modeling and simulation of driver behavior is important for the current industrial thrust of computer-based vehicle development. The main contribution of this paper is the development of an adaptive lateral preview human driver model. This driver model template has a few parameters that can be adjusted to simulate steering actions of human drivers with different driving styles. In ot...

Uncovering driver genes is crucial for understanding heterogeneity in cancer. L1-type regularization approaches have been widely used for uncovering cancer driver genes based on genome-scale data. Although the existing methods have been widely applied in the field of bioinformatics, they possess several drawbacks: subset size limitations, erroneous estimation results, multicollinearity, and hea...

We present in this report a new car-following theory that can reproduce both the so-called capacity drop and traffic hysteresis, two prominent features of multiphase vehicular traffic flow. This is achieved through the introduction of a single variable, driver response time, that depends on both vehicle spacing and traffic motion. By specifying different functional forms of response time, one c...

This research is focused on driver behavior in traffic, especially during car-following situations and safety critical events. Driving behavior is considered as a human decision process in this research which provides opportunities for an artificial driver agent simulator to learn according to naturalistic driving data. This thesis presents two mechine learning methodologies that can be applied...