Are Mobile In-Car Communication Systems

Feasible?

A Usability Study

Authors:Patrick Tchankue, Janet Wesson and Dieter Vogts

SAICSIT '12, October 1–3, 2012, Centurion, Tshwane, South Africa

• Introduction

• In-Car Communication Systems (ICCS)

• Speech User Interfaces (SUI)

• Design and Implementation

• User Study

• Results

• Conclusion & Future work

Agenda

• Driver distraction can be caused by the use of mobile phone

• Sending text messages affects the driver the most

• Text messaging increases the crash risk by a factor of 23

• Mobile speech user interface (SUI) are one of the proposed solutions

• But the usability of mobile SUI has not been widely investigated

Introduction

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• ICCS are a subset of In-Vehicle Infotainment Systems (IVIS):

– Sending/Receiving text messages

– Making/Answering calls

• Limited access in developing countries:

– Limited to several car models

– Involve extra costs: e.g. Ford’s SYNC: $395

• Examples:

– DriveSafe.ly, VoiceTalk, S Voice, Siri

In-Car Communication Systems (ICCS)

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• Speech UI or Voice UIs:

– Allows interaction with systems through speech

– Widely used in multitasking situations (e.g. Car, Kitchen)

– Guarantee eyes-free and hands-free interactions

Speech User Interface (SUI)

Text-To-Speech

Speech recognition Language understanding

Dialogue manager

Language generation

Database

Typical architecture for voice-activated application

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Proposed Design (MIMIC)

Input Module

Automatic Speech Recognition

Natural Language Understanding

Input pre-processingand

Data fusion

Sensors

GPS

Web services

Dialogue Module

Context-aware module

Dialogue manager

Output Module

Text-To-Speech(TTS)

Natural Language Generation

1

2

3

4

67

89

5

Peer’s phone

Frames

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• Frame approach:

– CALL(number),

– SMS(number, content),

– REDIAL, REPEAT and CANCEL

• Prompts user (SMS)

• Grounding to handle uncertainty

• Confirmation before execution

Dialogue Module

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• Speech engine:

– Google’s cloud-based speech recogniser

– RecognitionListener’s library fully hands-free

• Natural Language Understanding:

– Commands, keywords, homophones and synonyms

• Text To Speech:

– Native Android text-to-speech

– Female voice, normal pitch and rate

– Samsung Galaxy SII

Implementation

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• Selection of participants:– 10 participants: students from 18 to 28 year old

– 95 % of usability issues can be found (Nielsen & Landauer, 1993)

• Apparatus and Procedure:– Lane Change Test

– Android phone

– Steering wheel and pedals

• Metrics:– Workload

– Time-on-task in seconds

– Error on task

– Task completion

– Success rate.

User Study

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Task List

ID Task Description

T01

Please call the contact Maria

- Say “Call Maria”

- Answer “Yes”

T02

Send the text message “I am running a few minutes late” to John:

- Say “SMS John”

- Say ”three” to choose the third option

- Say “Yes” to send the selected text message

T03

Redial the previous outgoing call:

- Say “Redial”

- Answer “Yes”

T04

Please call Diana

- Say “Phone Diana”

- Answer “Yes”

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ID Task Description

T05

Send the text message “I will call you when I get there” to Peter:

- Say “Text Peter”

- Say ”One” to choose the first option

- Say “Yes” to send the selected text message

T06

Send the text message “I can’t talk right now, I am driving” to Janet:

- Say “SMS Janet”

- Say “Two” to choose the second option

- Say “Yes” to confirm the message

T07

Call a number :

- Say “Call 074 456 1245”

- Say “Yes” to confirm the number

Number of Errors

0.250.33

3.00

0.25 0.50

0.75

2.38

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

4.00

4.50

5.00

T01 T02 T03 T04 T05 T06 T07

Means of errors for each task (n=10)

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Time-on-task Results

0

50

100

150

200

250

300

T01 T02 T03 T04 T05 T06 T07

Time (seconds) spent on each task (n=10)

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Task Completion and Success Rate

ID Completion Success

T01 10 9

T02 10 9

T03 6 6

T04 10 10

T05 10 9

T06 10 10

T07 7 6

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Workload NASA TLX

Means of the user workload (n=10)

1.50

2.50

2.00

3.503.50

3.00

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

4.00

4.50

5.00

Mental demand Physical demand Temporal demand Frustration Effort Good performance

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User Satisfaction Results

Means of user satisfaction (System Usability Scale) (n = 10)

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3.50

4.00

4.00

4.00

3.00

2.00

2.00

2.00

2.00

1.00

0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50 5.00

Willingness to use frequently

Ease of use

Integration

Ease of learning

Confidence

Complexity

Assistance needed

Inconsistency

Cumbersome

Lot of learning needed

Self-reported metrics on the SUI

2

1 1

3

0

1

2

3

4

5

6

Poor recognition (confusion) Voice not recognised Clear utterance (TTS) Turn taking

Means of self-reported metrics on the SUI (n=10)

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Conclusion and Future work

• Mobile SUI can only be useful if there are few usability issues and recognition rate is high.

• The SUI of MIMIC was effective in sending text messages and making calls

• The user dictation was a source of errors

• Future work:

– Implementation of the context-aware module

– Weather, GPS and sensor information will be used to determine possible distraction level (from 1 to 5)

Thank You

Contact:Emails: Patrick.TchankueSielinou@nmmu.ac.za

Janet.Wesson@nmmu.ac.zaDieter.Vogts@nmmu.ac.za

Phone Numbers: 041 504 2323, 041 504 2088