Assessing Energy-Related Situation Awareness Using Self-Controlled Occlusion During Electric Vehicle Driving Scenes

Abstract

Optimal eco-driving in electric vehicles (EVs) can be challenging due to volatile, bidirectional energy flows and the difficulty of directly sensing energy flows. The present research investigates energy-related situation awareness (Energy Dynamics Awareness, EDA) as a pilot study. EDA is a theoretical concept that helps to describe and understand how visual energy feedback displays inform energy-efficient vehicle control decisions. We compared three methods (estimation tasks, subjective EDA rating scale, and gaze behavior metric) to assess EDA under two different workload conditions, using a video-based online study displaying EV driving scenes (N = 29). We developed a novel approach to collect gaze behavior indicators using self-controlled (i.e., manually directed) occlusion through keyboard input. Participants were asked to estimate and compare the energy consumed in EV driving scenes while performing a parallel visuospatial n-back task to induce cognitive load. Based on our findings, the n-back task successfully induced cognitive load and self-directed occlusion showed to be a promising method for energy display evaluation studies. The performance of the consumption estimation task and display fixations were influenced by cognitive workload, which has important implications for ecodriving interface design. As the subjective and performance-related measures of EDA did not correlate, the results contribute to the discussion on the divergence between subjective and objective measures of situation awareness. This pilot study encourages further research with a larger sample and adapted methods.

Keywords: Electric Vehicles, Situation Awareness, Ecodriving, Self-Controlled Occlusion, Workload, Instantaneous Consumption Display

DOI: 10.54941/ahfe1005219