Is Now A Good Time?: An Empirical Study of Vehicle-Driver Communication Timing

Advances in automotive sensing systems and speech interfaces provide new opportunities for smarter driving assistants or infotainment systems. For both safety and consumer satisfaction reasons, any new system which interacts with drivers must do so at appropriate times. We asked 63 drivers, ''Is now a good time?'' to receive non-driving information during a 50-minute drive. We analyzed 2,734 responses and synchronized automotive and video data, and show that while the chances of choosing a good time can be determined with better success using easily accessible automotive data, certain nuances in the problem require a richer understanding of the driver and environment states in order to achieve higher performance. We illustrate several of these nuances with quantitative and qualitative analyses to contribute to the understanding of how to design a system that might simultaneously minimize the risk of interacting at a bad time while maximizing the window of allowable interruption.

[1]  Christoph Mayser,et al.  REDUCING DRIVERS' MENTAL WORKLOAD BY MEANS OF AN ADAPTIVE MAN-MACHINE INTERFACE , 2003 .

[2]  Bryan Reimer,et al.  MIT Autonomous Vehicle Technology Study: Large-Scale Deep Learning Based Analysis of Driver Behavior and Interaction with Automation , 2017 .

[3]  Anne T. McCartt,et al.  National Reported Patterns of Driver Cell Phone Use in the United States , 2010, Traffic injury prevention.

[4]  Jerome Boudy,et al.  "REAL TIME" ANALYSIS OF THE DRIVING SITUATION IN ORDER TO MANAGE ON-BOARD INFORMATION , 2002 .

[5]  Wendy Ju,et al.  Fast & Furious: Detecting Stress with a Car Steering Wheel , 2018, CHI.

[6]  Stewart Worrall,et al.  An Unsupervised Approach for Inferring Driver Behavior From Naturalistic Driving Data , 2015, IEEE Transactions on Intelligent Transportation Systems.

[7]  Victor-Emil Neagoe,et al.  Drunkenness diagnosis using a Neural Network-based approach for analysis of facial images in the thermal infrared spectrum , 2017, 2017 E-Health and Bioengineering Conference (EHB).

[8]  Hema Swetha Koppula,et al.  Brain4Cars: Car That Knows Before You Do via Sensory-Fusion Deep Learning Architecture , 2016, ArXiv.

[9]  Dot Hs,et al.  The 100-Car Naturalistic Driving Study Phase II - Results of the 100-Car Field Experiment , 2006 .

[10]  D. Strayer,et al.  Passenger and Cell-Phone Conversations in Simulated Driving , 2004, Journal of experimental psychology. Applied.

[11]  Roberto Montanari,et al.  COMUNICAR: INTEGRATED ON-VEHICLE HUMAN MACHINE INTERFACE DESIGNED TO AVOID DRIVER INFORMATION OVERLOAD , 2002 .

[12]  Reid G. Simmons,et al.  Smartphone Interruptibility Using Density-Weighted Uncertainty Sampling with Reinforcement Learning , 2011, 2011 10th International Conference on Machine Learning and Applications and Workshops.

[13]  Clifford Nass,et al.  Improving automotive safety by pairing driver emotion and car voice emotion , 2005, CHI Extended Abstracts.

[14]  Toshihiro Wakita,et al.  Voice Information System Adapted to Driver's Mental Workload , 2002 .

[15]  Christopher D. Wickens,et al.  Examining the Impact of Cell Phone Conversations on Driving Using Meta-Analytic Techniques , 2006, Hum. Factors.

[16]  David Crundall,et al.  Regulating Conversation During Driving: A Problem for Mobile Telephones? , 2005 .

[17]  Gustav Markkula,et al.  TOWARDS THE NEXT GENERATION INTELLIGENT DRIVER INFORMATION SYSTEM (IDIS): THE VOLVO CAR INTERACTION MANAGER CONCEPT , 2006 .

[18]  Daniel C. McFarlane,et al.  Comparison of Four Primary Methods for Coordinating the Interruption of People in Human-Computer Interaction , 2002, Hum. Comput. Interact..

[19]  Andrew L. Kun,et al.  Estimating cognitive load using remote eye tracking in a driving simulator , 2010, ETRA.

[20]  James Fogarty,et al.  Examining task engagement in sensor-based statistical models of human interruptibility , 2005, CHI.

[21]  Zhang Hua,et al.  Speech recognition interface design for in-vehicle system , 2010, AutomotiveUI.

[22]  Fuliang Weng,et al.  Developing a Conversational In-Car Dialog System , 2005 .

[23]  Roel Vertegaal,et al.  Towards a Physiological Model of User Interruptability , 2007, INTERACT.

[24]  Christopher G. Atkeson,et al.  Predicting human interruptibility with sensors: a Wizard of Oz feasibility study , 2003, CHI '03.

[25]  Paul Green,et al.  Development and Evaluation of Automotive Speech Interfaces: Useful Information from the Human Factors and the Related Literature , 2013 .

[26]  Marco Botta,et al.  Real-Time Detection System of Driver Distraction Using Machine Learning , 2013, IEEE Transactions on Intelligent Transportation Systems.

[27]  Paul Green,et al.  Safety and Usability of Speech Interfaces for In-Vehicle Tasks while Driving: A Brief Literature Review , 2006 .

[28]  Mark Billinghurst,et al.  A user study of auditory versus visual interfaces for use while driving , 2008, Int. J. Hum. Comput. Stud..

[29]  Alessandro De Gloria,et al.  Towards the Automotive HMI of the Future: Overview of the AIDE-Integrated Project Results , 2010, IEEE Transactions on Intelligent Transportation Systems.

[30]  Shuyan Hu,et al.  Driver drowsiness detection with eyelid related parameters by Support Vector Machine , 2009, Expert Syst. Appl..

[31]  Anind K. Dey,et al.  Sensors Know When to Interrupt You in the Car: Detecting Driver Interruptibility Through Monitoring of Peripheral Interactions , 2015, CHI.

[32]  James Fogarty,et al.  Biases in human estimation of interruptibility: effects and implications for practice , 2007, CHI.

[33]  André Berton,et al.  Towards a flexible UI model for automotive human-machine interaction , 2009, AutomotiveUI.

[34]  Firas Lethaus,et al.  A comparison of selected simple supervised learning algorithms to predict driver intent based on gaze data , 2013, Neurocomputing.

[35]  Tim Paek,et al.  The effect of speech interface accuracy on driving performance , 2007, INTERSPEECH.

[36]  Davide Anguita,et al.  Human Activity Recognition on Smartphones Using a Multiclass Hardware-Friendly Support Vector Machine , 2012, IWAAL.

[37]  D. Norman,et al.  Psychological Issues in Support of Multiple Activities , 1986 .

[38]  Mary Czerwinski,et al.  Instant Messaging: Effects of Relevance and Timing , 2000 .

[39]  Alex Pentland,et al.  Graphical models for driver behavior recognition in a SmartCar , 2000, Proceedings of the IEEE Intelligent Vehicles Symposium 2000 (Cat. No.00TH8511).

[40]  Thomas A. Dingus,et al.  The 100-Car Naturalistic Driving Study Phase II – Results of the 100-Car Field Experiment , 2006 .

[41]  Christopher G. Atkeson,et al.  Predicting human interruptibility with sensors , 2005, TCHI.

[42]  Andreas Christmann,et al.  Support vector machines , 2008, Data Mining and Knowledge Discovery Handbook.

[43]  Mohan M. Trivedi,et al.  On the Roles of Eye Gaze and Head Dynamics in Predicting Driver's Intent to Change Lanes , 2009, IEEE Transactions on Intelligent Transportation Systems.