A novel EEG-driven multidimensional modelling of the Driver Performance Envelope for a user-centred human-vehicle interaction

Abstract

The concept of Driver Performance Envelope (DPE), conceptualized from the theory of Human Performance Envelope developed in aviation, defines the boundary within which the driver can perform effectively. This study proposes a novel multidimensional formulation of the DPE based on EEG-derived neurometrics, aimed at providing a time-resolved estimation of the driver’s readiness to safely operate the vehicle. Fifteen participants completed five simulated manual driving scenarios designed to elicit different levels of difficulty, stress and thus overall performance, by manipulating context (highway vs. urban), traffic density, weather conditions (sun, rain, fog), time pressure, and the presence of a secondary task. EEG was continuously recorded to extract individual neurometrics of workload, vigilance, stress, and fatigue. These dimensions were integrated into a DPE index through a novel processing pipeline combining Principal Component Analysis and Mutual Information. Questionnaires were administered to validate the experimental design. Results showed that the proposed EEG-driven DPE successfully discriminated driving conditions characterised by different hypothesised performance envelopes. The DPE index significantly differentiated (p < .05) scenarios associated with high, intermediate, and—at least one comparison—low expected performance capacity. The robustness of the metric was supported by its comparison with an EEG baseline derived from a resting-state task, confirming its reliability and coherence across conditions. Beyond its methodological innovation, the proposed framework supports the development user-centred human–vehicle interaction strategies. Overall, this work demonstrates the feasibility and value of an EEG-driven multidimensional DPE as a key enabler for next-generation, human-centred mobility, contributing to safer and socially sustainable driving systems.

Keywords: Performance, Simulated Driving, EEG, Driver Assessment, Safety, Human Factors, Mutual Information, Principal Component Analysis, Human-vehicle Interaction

DOI: 10.54941/ahfe1007860