Variations on a theme: Topic modeling of naturalistic driving data.

بررسی تغییرات ریتم آلفا به منظور ردیابی خستگی ذهنی راننده در روی شبیه ساز رانندگی

Introduction: Driver fatigue is one of the major causes of accidents in roads. It is suggested that driver fatigue and drowsiness accounted for more than 30% of road accidents. Therefore, it is important to use features for real-time detection of driver mental fatigue to minimize transportation fatalities. The purpose of this study was to explore the EEG alpha power variations in sleep dep...

Modeling 100-Car Safety Events: A Case-Based Approach for Analyzing Naturalistic Driving Data Final Report

Understanding safety critical interactions between bicycles and motor vehicles in Europe by means of Naturalistic Driving techniques

Although prevention of bicycle–motorized-vehicle-crashes will result in significant improvements in bicycle mortality rates, little is known about the actual safety critical events leading up to these crashes. Recently, the understanding of critical events has been advanced by the use of Naturalistic driving techniques. This technique gathers data about driving in ‘natural conditions’ by means ...

Crashes and crash-surrogate events: exploratory modeling with naturalistic driving data.

There is a need to extend and refine the use of crash surrogates to enhance safety analyses. This is particularly true given opportunities for data collection presented by naturalistic driving studies. This paper connects the original research on traffic conflicts to the contemporary literature concerning crash surrogates using the crash-to-surrogate ratio, π. A conceptual structure is develope...

PROCEEDINGS of the Seventh International Driving Symposium on Human Factors in Driver Assessment, Training, and Vehicle Design A COHORT-BASED DATA STRUCTURE DESIGN FOR ANALYZING CRASH RISK USING NATURALISTIC DRIVING DATA

Although naturalistic driving studies (NDS) have become more prevalent in recent years, many challenges remain in analyzing the data. One challenge is inclusion of exposure in modeling crash risk. While this is a potential strength of NDS, comparatively few studies have emphasized exposure-based analyses. A second challenge is the formulation of analysis methods that include driver attributes, ...