Identification of Adaptive Driving Style Preference through Implicit Inputs in SAE L2 Vehicles

Publication Date

11-7-2022

Document Type

Conference Proceeding

Publication Title

ACM International Conference Proceeding Series

DOI

10.1145/3536221.3556637

First Page

468

Last Page

475

Abstract

A key factor to optimal acceptance and comfort of automated vehicle features is the driving style. Mismatches between the automated and the driver preferred driving styles can make users take over more frequently or even disable the automation features. This work proposes identification of user driving style preference with multimodal signals, so the vehicle could match user preference in a continuous and automatic way. We conducted a driving simulator study with 36 participants and collected extensive multimodal data including behavioral, physiological, and situational data. This includes eye gaze, steering grip force, driving maneuvers, brake and throttle pedal inputs as well as foot distance from pedals, pupil diameter, galvanic skin response, heart rate, and situational drive context. Then, we built machine learning models to identify preferred driving styles, and confirmed that all modalities are important for the identification of user preference. This work paves the road for implicit adaptive driving styles on automated vehicles.

Keywords

adaptive driving style, automated vehicle, gaze detection, multimodal data

Department

Industrial and Systems Engineering

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