Defining and Evaluation Method for External Human-Machine Interfaces

2026-04-14

AI Summary

This study addresses the absence of a standardized evaluation framework for external human–machine interfaces (eHMIs) in autonomous vehicles, which hinders systematic comparison and design optimization. To bridge this gap, the authors propose the first structured, multidimensional assessment framework encompassing seven dimensions—standardization difficulty, cost-effectiveness, accessibility, comprehensibility, communication multidimensionality, layout placement, and readability—comprising 223 specific metrics. The framework’s validity is demonstrated through a questionnaire-based quantitative evaluation comparing four existing eHMI designs against a motion-only baseline. Results indicate that combining intentional vehicle motion with well-placed textual displays yields the highest performance. The study also identifies critical research gaps, particularly concerning readability and user learning efficiency, thereby establishing an objective benchmark for future eHMI development and evaluation.

Technology Category

• Humans and AI: Intelligent User Interfaces
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Intelligent Robots: Human-Robot Interaction

Application Category

• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

As the number of fatalities involving Autonomous Vehicles increase, the need for a universal method of communicating between vehicles and other agents on the road has also increased. Over the past decade, numerous proposals of external Human-Machine Interfaces (eHMIs) have been brought forward with the purpose of bridging this communication gap, with none yet to be determined as the ideal one. This work proposes a universal evaluation method conformed of 223 questions to objectively evaluate and compare different proposals and arrive at a conclusion. The questionnaire is divided into 7 categories that evaluate different aspects of any given proposal that uses eHMIs: ease of standardization, cost effectiveness, accessibility, ease of understanding, multifacetedness in communication, positioning, and readability. In order to test the method it was used on four existing proposals, plus a baseline using only kinematic motions, in order to both exemplify the application of the evaluation method and offer a baseline score for future comparison. The result of this testing suggests that the ideal method of machine-human communication is a combination of intentionally-designed vehicle kinematics and distributed well-placed text-based displays, but it also reveals knowledge gaps in the readability of eHMIs and the speed at which different observers may learn their meaning. This paper proposes future work related to these uncertainties, along with future testing with the proposed method.

Problem

• Autonomous Vehicles
• external Human-Machine Interfaces
• communication gap
• road safety
• eHMI evaluation

Innovation

• external Human-Machine Interface
• evaluation framework
• autonomous vehicles
• communication standardization
• kinematic cues