In 2012, the seven university consortium, including Michigan Tech, was awarded the first National University Rail Transportation Center (NURail) by the USDOT Research and Innovative Technology Administration (RITA). The University of lllinois at Urbana-Champaign (UIUC) leads the consortium.
The primary objective of the NURail Center is to improve and expand rail education, research, workforce development, and technology transfer in the U.S.
According to the USDOT Federal Railroad Administration, highway-rail grade crossing and trespasser fatalities still account for 96 percent of all rail-related deaths. A review of accident causes reveals that one of the main accident factors is human driving behavior.
One of the potential approaches to improve drivers’ behavior at crossings is to systematically examine their actions and use the outcomes to develop alternative methods or approaches to decrease the probability of missing or ignoring warnings. The Naturalistic Driving Study (NDS) approach, using in-vehicle video and other sensors to directly observe drivers during normal driving activities, is a technique that shows promise toward improved understanding of driver actions in the crossings.
This two-year project is divided into two phases with an overall objective to investigate driver behavior at highway-rail grade crossings using two distinct, but complimentary techniques. Phase I will use data collected under the Strategic Highway Research Program (SHRP) Naturalistic Driving Study to look at how normal drivers react at crossings in every day driving situations. Phase II will use the understanding developed in the first phase to create scenarios that resemble environments similar to those found in the NDS for use in our driver simulator environment. The research will look for two basic results. First, we will develop and use the organized NDS crossing database to examine behavioral trends at the crossings. Second, we will compare driver behavior in the simulator with that found in the NDS data to determine the level of correlation between the two environments. Our hypothesis is that a strong correlation would allow us to outline how to use the simulator environment in the future to predict driver response to a variety of crossing parameters.
Sponsor: Michigan Tech Transportation Institute (MTTI)
PI: Myounghoon (Philart) Jeon
In the proposed initiative, the PI aims to provide a phased path for an innovative research and educational program at Michigan Tech focused on the driving domain. The proposed effort will eventually lead to a sustainable, officially recognized Driving Research Center under the Michigan Tech Transportation Insitutute MTTI). To this end, the PI plans to (1) initiate a collaborative driving research project with like-minded domain experts; (2) expand the scope across the Michigan Tech campus and build an ENGIN (Exploring Next Generation IN-vehicle INterfaces) consortium that can identify and develop additional driving-related research projects together; (3) make continuous efforts to secure external funding for driving-related projects; and (4) develop a more systematic driving education for undergraduates and graduates and K-12 outreach program by lining-up and integrating related courses across departments, hosting regular seminars with invited external speakers, expanding current and developing new outreach programs, and organizing international workshops and conferences.
Coordinating the initiative is Steven Landry, PhD student in Applied Cognitive Science and Human Factors Graduate Program.
Michigan Tech will redesign and expand sound samples from Phase I. The sound variables, which will be manipulated for the experiment, include frequency, number of tones, repetition rate, duration, speed and amplitude. Equos will then conduct an experiment for mapping between sound profiles and perceptual dimensions. Michigan Tech will also design sound samples for further functionalities (e.g., school zone, awakening sound).