An extension of the human-factors methodological toolbox for human-AV interaction design research : Deliverable 1.4 in the EC ITN project SHAPE-IT

Rapport, 2023

Early state researchers (ESRs) of the SHAPE-IT project have committed to exploring innovative methods to ensure driving safety during interactions between human and Automated Vehicles (AVs). In this deliverable, insights of ESRs span a broad spectrum of methodologies, from experimental methods, including psychophysiological measures, Virtual Reality/Augmented Reality (VR/AR) applications, and transparency assessments, to human-AV interaction models, with vehicle-pedestrian model and vehicle-cyclist model, and lastly the long-term effects.

New types of interactions between humans and AVs need to be evaluated during the systems’ development to ensure that requirements of safety, acceptance, and efficiency are met before they are introduced to the market. Since innovative concepts require great cost and effort for their realization, it is necessary to ascertain whether the expected effects will be achieved. Many of the systems’ ergonomic requirements can be considered using experimental methods based on theoretical knowledge.

This proposal outlines different aspects for empirical investigations related to the interaction between human and AV. It is important to mention that different human roles need to be considered inside (passenger or driver) and outside/around (VRU) the AV. The research aspects range from cognitive processes (perception and decision), via motion behavior, to learning and behavioral adaptation. This requires that dedicated methods with clear, consistent definitions be refined or developed.

One example is the usage of virtual reality to investigate the complex interaction processes between AVs and VRUs in a safe and controllable setting as an alternative to field trials. Also, different AV communication strategies can be implemented in VR quicker and with reduced effort compared to hardware setups or experimental cars.

Further methods are physiological measurements, different types of driving simulation and long-term behavioral study approaches.

In their combination the different methods represent a toolbox of methodological approaches to analyze and evaluate different aspects of automated driving realizations.

This deliverable presents a collection of recommended experimental approaches that address complex questions using advanced measurement equipment and statistical approaches, and their successful application within the SHAPE-IT project.