Improving Driver Satisfaction with a Driving Function Learning from Implicit Human Feedback -- a Test Group Study

TL;DR

This study introduces an adaptive algorithm for personalizing a predictive longitudinal driving function based on driver feedback, significantly increasing satisfaction and reducing interventions in a simulated driving environment.

Contribution

The paper presents a novel iterative algorithm that adjusts a driving function using implicit driver feedback from interventions, enabling personalized and improved driver assistance.

Findings

Significant increase in driver satisfaction

Reduction in driver interventions

Positive participant feedback on system improvements

Abstract

During the use of advanced driver assistance systems, drivers frequently intervene into the active driving function and adjust the system's behavior to their personal wishes. These active driver-initiated takeovers contain feedback about deviations in the driving function's behavior from the drivers' personal preferences. This feedback should be utilized to optimize and personalize the driving function's behavior. In this work, the adjustment of the speed profile of a Predictive Longitudinal Driving Function (PLDF) on a pre-defined route is highlighted. An algorithm is introduced which iteratively adjusts the PLDF's speed profile by taking into account both the original speed profile of the PLDF and the driver demonstration. This approach allows for personalization in a traded control scenario during active use of the PLDF. The applicability of the proposed algorithm is tested in a driving simulator-based test group study with 43 participants. The study finds a significant increase in driver satisfaction and a significant reduction in the intervention frequency when using the proposed adaptive PLDF. Additionally, feedback by the participants was gathered to identify further optimization potentials of the proposed system.