Assessment and Prediction of Behavioral Simulator Fidelity Round Table Discussion Moderator Erwin R. Boer.

Slides:



Advertisements
Similar presentations
USING VALUES AND POLICY TO PRIORITIZE INDICATORS OF SUSTAINABILITY Deborah J. Shields USDA Forest Service - Research.
Advertisements

GEOSS ADC Architecture Workshop Session 3c Test Facility for GEOSS Registration Paul Smits EC-JRC
Chia Wei Ensar Becic Christopher Edwards HumanFIRST Program Department of Engineering University of Minnesota.
Autonomic Scaling of Cloud Computing Resources
Cognitive Systems, ICANN panel, Q1 What is machine intelligence, as beyond pattern matching, classification and prediction. What is machine intelligence,
Esri International User Conference | San Diego, CA Technical Workshops | Kevin M. Johnston Shitij Mehta ****************** An Introduction to Dynamic Simulation.
© 2009 The MITRE Corporation. All rights Reserved. Evolutionary Strategies for the Development of a SOA-Enabled USMC Enterprise Mohamed Hussein, Ph.D.
Assessing and Managing Risk
Cognitive Issues in Virtual Reality Wickens, C.D., and Baker, P., Cognitive issues in virtual environments, in Virtual Environments and Advanced Interface.
1 Challenge the future The Dutch Automated Vehicle Initiative: Challenges for automated driving Dr. R.(Raymond) G. Hoogendoorn Assistant Professor Delft.
13 1 Chapter 13 The Data Warehouse Database Systems: Design, Implementation, and Management, Seventh Edition, Rob and Coronel.
Report on Intrusion Detection and Data Fusion By Ganesh Godavari.
Guidelines for Traffic Control at Surface Mines
HRM A – G. Grote ETHZ, WS 06/07 Human Resource Management (HRM) What? …the functional area of an organization that is responsible for all aspects of hiring.
Korea Univ. Division Information Management Engineering UI Lab. Korea Univ. Division Information Management Engineering UI Lab. Human Interface PERCEPTUAL-MOTOR.
A Multi-Agent System for Visualization Simulated User Behaviour B. de Vries, J. Dijkstra.
Location Planner A Decision Support System for the planning of shopping centers.
Professional Growth= Teacher Growth
USE OF VISUAL OCCLUSION TO ASSESS IN-VEHICLE HMI Dean P. Chiang Dynamic Research, Inc., Torrance, CA 22 May 2003 ITS America Annual Meeting, Minneapolis.
 Road Safety the European Union Policy Carla Hess European Commission, Directorate General for Mobility & Transport Road.
Session 8 Early Risk Communication Campaign Planning Session 8 Slide Deck Slide 8-1.
UML - Development Process 1 Software Development Process Using UML (2)
Modeling Driver Behavior in a Cognitive Architecture
Outline Introduction Methodhology Domains associated with teacher training in technology integration Domains, knowledges and teaching competencies for.
Chapter © 2012 Pearson Education, Inc. Publishing as Prentice Hall.
Conducting Situated Learning in a Collaborative Virtual Environment Yongwu Miao Niels Pinkwart Ulrich Hoppe.
1. 2 Abstract - Two experimental paradigms : - EEG-based system that is able to detect high mental workload in drivers operating under real traffic condition.
9/14/2012ISC329 Isabelle Bichindaritz1 Database System Life Cycle.
Break-out Group # D Research Issues in Multimodal Interaction.
Development of Indicators for Integrated System Validation Leena Norros & Maaria Nuutinen & Paula Savioja VTT Industrial Systems: Work, Organisation and.
BUSINESS SENSITIVE 1 Annual Meeting of the AASHTO Subcommittee on Design Human Factors Guidelines (HFG) for Road Systems (NCHRP 17-41) July 22, 2009 John.
Report on Intrusion Detection and Data Fusion By Ganesh Godavari.
Do Now… Take a Handout(s) and then answer the following questions: –Name the elements of the New Jersey Road test. –Explain the Early Bird Steps of the.
The roots of innovation Future and Emerging Technologies (FET) Future and Emerging Technologies (FET) The roots of innovation Proactive initiative on:
Railway Safety Commission An Coimisiún Sábháilteachta Iarnróid The Management of Third Party Generated Risk in Ireland International Railway Safety Conference.
Online Construction of Analytical Prediction Models for Physical Environments: Application to Traffic Scene Modeling Anurag Umbarkar, Shreyas K Rajagopal.
Research Project #6 Develop Better Data on Accident Precursors or Leading Indicators.
Assessing and Managing Risk
IntelliDrive Safety Workshop July 20, 2010 Stephanie C. Binder National Highway Traffic Safety Administration US Department of Transportation Human Factors.
L. M. Camarinha-Matos © L. M. Camarinha-Matos WP5 – STATUS OVERVIEW WP5 meeting – Paris, June 2004 Luis M. Camarinha-Matos
The geometry of the system consisting of the hyperbolic mirror and the CCD camera is shown to the right. The points on the mirror surface can be expressed.
GNET BRAINSTORMING. GNET INTRODUCTION.
Copyright © 2015 McGraw-Hill Education. All rights reserved
HFE 760 Virtual Environments Winter 2000 Jennie J. Gallimore
New ELICIT Software Platform Capabilities and Campaign 13 th ICCRTS, I-079, June 2008 Mary Ruddy Mark Nissen
 ROAD SAFETY: the European Union Policy European Commission, Directorate General for Mobility & Transport «Road Safety.
Do Now… –Explain the Early Bird Road Steps of the GDL licensing process.
Functionality of objects through observation and Interaction Ruzena Bajcsy based on Luca Bogoni’s Ph.D thesis April 2016.
Programme for Simulation Innovation Hamish Jamson Erwin Boer Sunjoo Advani Spencer Salter University of Leeds Driving Simulator Institute for Transport.
National Taiwan Normal A System to Detect Complex Motion of Nearby Vehicles on Freeways C. Y. Fang Department of Information.
Part III: The Future: Scenarios, Conclusions, and Recommendations [of HSI Methods in System Development] Frank E. Ritter 26 feb 08 1.
Stages of Research and Development
Driving, Mobility and Laws
The Management of Third Party Generated Risk in Ireland
Chapter 13 Managing Risk in Leisure Programs Russell & Jamieson.
SEEV Model of Visual Attention Allocation
Location Prediction and Spatial Data Mining (S. Shekhar)
Professor S K Dubey,VSM Amity School of Business
Round Table Discussion on Ergonomics Competencies
A Unifying View on Instance Selection
Situation Awareness through Agent Based
Symbolic cognitive architectures
Hypotheses and Objectives Experimentation and Transition
RSM GC Advisory – Energy Management System (ISO50001)
Software Engineering I
Market-based Dynamic Task Allocation in Mobile Surveillance Systems
Chapter 13 The Data Warehouse
Social Practice of the language: Describe and share information
In service monitoring Near miss logging Continuous improvement
Presentation transcript:

Assessment and Prediction of Behavioral Simulator Fidelity Round Table Discussion Moderator Erwin R. Boer

Raise awareness of issues Establish framework for attack Establish what we mean by success GOAL

Behavioral Simulator Fidelity Do we know enough, do we understand enough? How to assess it? –How to characterize driver behavior? How to predict it? –How to quantify simulator characteristics? –How to characterize experimental scenarios and protocol? –How to map simulator characteristics onto behavioral characteristics?

Driver Behavior What is a representative subset of driving tasks? Performance – safety –Safety margins Boundaries (CDFs) of operation in perceptual space –Decision criteria Boundaries (CDFs) of maneuver initiation in perceptual space Effort – workload –Monitoring activity Eye tracker (attention tracker) –Control activity Manipulator movements –Mental activity Predictable from monitoring and control activity (secondary task paradigms)

Simulator Characteristics Is the dimensionality tamable? How do we know effect of each factor? Can we assign low-dimensionality quality vectors to each aspect? Visual system –Visual rendering Motion system –Motion rendering Sound system –Sound rendering Control loader –Feel rendering Vehicle dynamics –Response rendering Environmental dynamics –Disturbance rendering Cab configuration –View rendering Room Configuration –Contextual rendering

Experimental Scenario and Protocol What is our language? Is a low-dimensionality vector of deviations from target feasible? (IHRA Simulator Scenario Workshop, 1:15 Thursday Oct 9) Driving sub-tasks –Imposed / assumed goals Environmental context –Road configuration –Dynamic distribution of other traffic –Spatio-temporal disturbance characterization Experimental procedure –Introduction –Training

Mapping Sim-Vector & Exp-Vector onto Beh-Vector Do we want neural nets? Do we want to employ data mining? Do we believe in fuzzy, Bayesian, or evidencial rules? Do we build a perceptual-mind-motor theory from first principles? Mapping function to performance and effort –Does this provide enough constructive insight? Integration of factors into perceptual-motor driver model –Do we know of a driver model that lends itself to this? Mapping function to coefficients in a driver model. –Are we getting swamped in a high dimensional marchland without knowing what to anchor us on?

Commonality Any effort towards and implementation of standardized characterization aids future efforts to develop behavioral assessment models. Do we feel that current studies are described well enough to conduct cross study research from the literature? Are we satisfied with the adopted paradigms for assessing driver’s perception (and control) is simulators?