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Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research

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Modeling Driving Performance Using In-Vehicle Speech Data From a Naturalistic Driving Study.

Jonny Kuo¹, Judith L Charlton², Sjaan Koppel³

¹Monash University, Melbourne, AustraliaHuman Factors North, Inc., Toronto, CanadaMonash University, Melbourne, Australia jonny.kuo@monash.edu.

|May 28, 2016

Summary

This summary is machine-generated.

Automated speech analysis reveals that while child presence alone doesn't alter driving, speech activity with multiple children significantly impacts driving performance variability. This highlights the complex interplay between in-vehicle communication and road safety.

Keywords:

child passengers driver distraction naturalistic driving study speaker diarization speech activity detection

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Area of Science:

Human Factors and Ergonomics
Transportation Safety
Speech Technology

Background:

Parent drivers often engage in child-related secondary behaviors, with unknown effects on driving.
Automated speech processing of naturalistic driving study (NDS) audio can analyze driver-child interactions and their impact on driving.

Purpose of the Study:

Develop and apply automated speech processing for in-vehicle data.
Examine how child passenger presence affects driving performance.
Model the relationship between speech data and driving behavior.

Main Methods:

Applied speech activity detection and speaker diarization to NDS audio data.
Utilized multilevel models to assess speech activity and child presence effects.
Analyzed data from 42 families in a Melbourne-based NDS.

Main Results:

Speech activity correlated significantly with velocity and steering angle variability.
Child passenger presence alone did not alter driving performance.
Speech activity with two child passengers showed the highest driving performance variability.

Conclusions:

In-vehicle speech effects on driving with child passengers are complex and heterogeneous.
Automated processing of observational data, including speech, is crucial for large-scale NDS.
Speech processing algorithms unlock new insights from existing NDS data for future research.