A comparison of artificial driving sounds for automated vehicles

Tools

Beattie, David and Baillie, Lynne and Halvey, Martin; (2015) A comparison of artificial driving sounds for automated vehicles. In: UbiComp '15. ACM, JPN, pp. 451-462. ISBN 978-1-4503-3574-4 (https://doi.org/10.1145/2750858.2807519)

[thumbnail of Beattie-etal-ACM-UbiComp-2015-A-comparison-of-artificial-driving-sounds-for-automated]
Preview
 Text. Filename: Beattie_etal_ACM_UbiComp_2015_A_comparison_of_artificial_driving_sounds_for_automated.pdf
Accepted Author Manuscript
Download (988kB)| Preview

Abstract

As automated vehicles currently do not provide sufficient feedback relating to the primary driving task, drivers have no assurance that an automated vehicle has understood and can cope with upcoming traffic situations [16]. To address this we conducted two user evaluations to investigate auditory displays in automated vehicles using different types of sound cues related to the primary driving sounds: acceleration, deceleration/braking, gear changing and indicating. Our first study compared earcons, speech and auditory icons with existing vehicle sounds. Our findings suggested that earcons were an effective alternative to existing vehicle sounds for presenting information related to the primary driving task. Based on these findings a second study was conducted to further investigate earcons modulated by different sonic parameters to present primary driving sounds. We discovered that earcons containing naturally mapped sonic parameters such as pitch and timbre were as effective as existing sounds in a simulated automated vehicle.

ORCID iDs

Beattie, David, Baillie, Lynne and Halvey, Martin ORCID logoORCID: https://orcid.org/0000-0001-6387-8679;
CORE (COnnecting REpositories)