Actual Achieved Gain and Optimal Perceived Gain: Modeling Human Take-over Decisions Towards Automated Vehicles' Suggestions

TL;DR

This paper introduces metrics to evaluate human decision quality in autonomous vehicle take-overs, analyzing how decision accuracy varies with ADS reliability and decision time, and exploring interventions to enhance decision-making.

Contribution

It proposes the Actual Achieved Gain and Optimal Perceived Gain metrics, providing a novel framework for assessing and improving human-Autonomous Driving System collaboration.

Findings

AAG converges to OPG with sufficient decision time.

Limited decision time results in more intuitive, less optimal choices.

Interventions like voice and multi-modal alarms can improve decision quality.

Abstract

Driver decision quality in take-overs is critical for effective human-Autonomous Driving System (ADS) collaboration. However, current research lacks detailed analysis of its variations. This paper introduces two metrics--Actual Achieved Gain (AAG) and Optimal Perceived Gain (OPG)--to assess decision quality, with OPG representing optimal decisions and AAG reflecting actual outcomes. Both are calculated as weighted averages of perceived gains and losses, influenced by ADS accuracy. Study 1 (N=315) used a 21-point Thurstone scale to measure perceived gains and losses-key components of AAG and OPG-across typical tasks: route selection, overtaking, and collision avoidance. Studies 2 (N=54) and 3 (N=54) modeled decision quality under varying ADS accuracy and decision time. Results show with sufficient time (>3.5s), AAG converges towards OPG, indicating rational decision-making, while limited time leads to intuitive and deterministic choices. Study 3 also linked AAG-OPG deviations to irrational behaviors. An intervention study (N=8) and a pilot (N=4) employing voice alarms and multi-modal alarms based on these deviations demonstrated AAG's potential to improve decision quality.