BeaCON - A Research Framework Towards an Optimal Navigation

In an optimally integrated HMS (Human Machine System), human must understand the machine as well as the machine must understand the human user. Same principle applies for car NS (Navigation System) which is a human-in-the-loop system. An ideally integrated NS knows how, when and what navigation information must be provided for the user and create minimal interruption for the primary task. To do the same, NS must hold the behavioral models of the user for providing the guidance information in an effective way. A research framework which uses these principles, is needed to create such models as well as for conducting further analysis for the research problem of “Giving the driver adequate navigation information with minimal interruption”. Until now no such research framework exists and because of that further analysis of the mentioned research problem cannot be conducted. In this paper we present the research framework BeaCON: Behavior-and Context-Based Optimal Navigation that enables detailed analysis of this research problem.

[1]  Morten Fjeld,et al.  Mixed Reality: A Survey , 2009, Human Machine Interaction.

[2]  Fumio Mizoguchi,et al.  Classifying Cognitive Load and Driving Situation with Machine Learning , 2014 .

[3]  Kai-Florian Richter,et al.  How does navigation system behavior influence human behavior? , 2019, Cognitive Research: Principles and Implications.

[4]  Paul N. Bennett,et al.  Guidelines for Human-AI Interaction , 2019, CHI.

[5]  Erwin R. Boer,et al.  Development of a steering entropy method for evaluating driver workload , 1999 .

[6]  Madjid Tavana,et al.  Autonomous vehicles: challenges, opportunities, and future implications for transportation policies , 2016, Journal of Modern Transportation.

[7]  Isaac Skog,et al.  In-Car Positioning and Navigation Technologies—A Survey , 2009, IEEE Transactions on Intelligent Transportation Systems.

[8]  Tom Gross,et al.  Towards Optimum Integration of Human and Car Navigation System , 2019, 2019 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT).

[9]  Carlos Guestrin,et al.  "Why Should I Trust You?": Explaining the Predictions of Any Classifier , 2016, ArXiv.