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

TL;DR

This study develops machine learning models using multimodal signals to automatically identify driver preferences for driving styles in SAE L2 vehicles, aiming to improve comfort and acceptance of automated driving features.

Contribution

It introduces a multimodal data collection and machine learning approach to continuously and automatically identify driver driving style preferences in automated vehicles.

Findings

All collected modalities are important for identifying user preferences.

The models successfully distinguish driver preferences based on multimodal data.

The approach enables implicit adaptation of driving styles in automated vehicles.

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.