Driver Heterogeneity in Willingness to Give Control to Conditional Automation

TL;DR

This study investigates driver willingness to relinquish control in conditionally automated vehicles using virtual reality experiments, accounting for individual differences and locus of control, revealing key factors influencing control sharing.

Contribution

It introduces a mixed logit and latent class modeling approach to analyze heterogeneity in driver control willingness, identifying internalizers and externalizers based on locus of control.

Findings

Drivers are more willing to give control during high-attention tasks like nighttime driving.

Internalizers show greater heterogeneity in control willingness than externalizers.

Control sharing increases when drivers perform non-driving tasks or face demanding conditions.

Abstract

The driver's willingness to give (WTG) control in conditionally automated driving is assessed in a virtual reality based driving-rig, through their choice to give away driving control and through the extent to which automated driving is adopted in a mixed-traffic environment. Within- and across-class unobserved heterogeneity and locus of control variations are taken into account. The choice of giving away control is modelled using the mixed logit (MIXL) and mixed latent class (LCML) model. The significant latent segments of the locus of control are developed into internalizers and externalizers by the latent class model (LCM) based on the taste heterogeneity identified from the MIXL model. Results suggest that drivers choose to "giveAway" control of the vehicle when greater concentration/attentiveness is required (e.g., in the nighttime) or when they are interested in performing a non-driving-related task (NDRT). In addition, it is observed that internalizers demonstrate more heterogeneity compared to externalizers in terms of WTG.