Identifying Modes of Intent from Driver Behaviors in Dynamic Environments

In light of growing attention of intelligent vehicle systems, we propose developing a driver model that uses a hybrid system formulation to capture the intent of the driver. This model hopes to capture human driving behavior in a way that can be utilized by semi- and fully autonomous systems in heterogeneous environments. We consider a discrete set of high level goals or intent modes, that is designed to encompass the decision making process of the human. A driver model is derived using a dataset of lane changes collected in a realistic driving simulator, in which the driver actively labels data to give us insight into her intent. By building the labeled dataset, we are able to utilize classification tools to build the driver model using features of based on her perception of the environment, and achieve high accuracy in identifying driver intent. Multiple algorithms are presented and compared on the dataset, and a comparison of the varying behaviors between drivers is drawn. Using this modeling methodology, we present a model that can be used to assess driver behaviors and to develop human-inspired safety metrics that can be utilized in intelligent vehicular systems.Comment: Submitted to ITSC 201

How do robots take two parts apart

This research is a natural progression of efforts which begun with the introduction of a new research paradigm in machine perception, called Active Perception. There it was stated that Active Perception is a problem of intelligent control strategies applied to data acquisition processes which will depend on the current state of the data interpretation, including recognition. The disassembly/assembly problem is treated as an Active Perception problem, and a method for autonomous disassembly based on this framework is presented

Loss-enabled sub-Poissonian light generation in a bimodal nanocavity

We propose an implementation of a source of strongly sub-Poissonian light in a system consisting of a quantum dot coupled to both modes of a lossy bimodal optical cavity. When one mode of the cavity is resonantly driven with coherent light, the system will act as an efficient photon number filter, and the transmitted light will have a strongly sub-Poissonian character. In addition to numerical simulations demonstrating this effect, we present a physical explanation of the underlying mechanism. In particular, we show that the effect results from an interference between the coherent light transmitted through the resonant cavity and the super-Poissonian light generated by photon-induced tunneling. Peculiarly, this effect vanishes in the absence of the cavity loss

Assembly via disassembly: A case in machine perceptual development

First results in the effort of learning about representations of objects is presented. The questions attempted to be answered are: What is innate and what must be derived from the environment. The problem is casted in the framework of disassembly of an object into two parts