With the semiconductor industry evolving into the deep sub-micron (DSM) era, crosstalk noise becomes a critical issue that needs to be handled efficiently and accurately. Modern designs like System-on-Chips have millions of noise-prone wires that need to be analyzed. Analysis using circuit-level simulation is not feasible. Efficient static noise analysis, which statically estimate noise based o...

Traffic flow on straight roads is the most common traffic phenomenon in urban road traffic networks. In this paper, a realistic cellular automaton (CA) model is proposed to investigate driver behavior on urban straight roads based on our field observations. Two types of driver behavior, free and car-following, are simulated. Free driving behavior is modeled by a novel five-stage speeding model ...

This paper describes our first step for advances in humanmachine interactive systems for in-vehicle environments of the UTDrive project. UTDrive is part of an on-going international collaboration to collect and research rich multi-modal data recorded for modeling behavior while the driver is interacting with speech-activated systems or performing other secondary tasks. A simultaneous second goa...

reviews in many countries indicate that drivers often talk on mobile (cell) phones. studies have been done on possible effects of concurrent mobile phone use on driving performance. previous studies have found that phone talking could impairs performance on simulated or instrumented driving tasks, using such measures as reaction time, following distance, and situational awareness. most of these...

The study and modeling of driver’s gaze dynamics is important because, if and how the driver is monitoring the driving environment is vital for driver assistance in manual mode, for take-over requests in highly automated mode and for semantic perception of the surround in fully autonomous mode. We developed a machine vision based framework to classify driver’s gaze into context rich zones of in...

This paper presents a driver performance map of braking and steering in response to three driving scenarios that lead to rear-end crashes. This map encompasses low risk, conflict, near-crash, and crash imminent driving states that correspond to advisory warning, crash imminent warning, and crash mitigation functionalities for intelligent vehicle rear-end crash countermeasures. Specifically, thi...

This paper examines the role of the human driver as the primary control element within the traditional driver-vehicle system. Lateral and longitudinal control tasks such as path-following, obstacle avoidance, and headway control are examples of steering and braking activities performed by the human driver. Physical limitations as well as various attributes that make the human driver unique and ...

Knowledge of the current and future driving context could facilitate the interaction between human driver and advanced driver assistance systems. A driver's intended actions (the future context) can be inferred from a number of sources, including the driver's current control actions, their visual scanning behavior, and the traffic environment surrounding them. In an approach similar to hidden M...

The Problem. The driving task involves a host of relevant cognitive, perceptual, and motor abilities. Cognitive architectures—frameworks for modeling human cognition and behavior—have evolved to integrate known theories of human cognition into unified theories that account for a wide range of cognitive phenomena. Role of Driving Simulators. Models of driver behavior based on cognitive architect...