Blending Participatory Design and Artificial Awareness for Trustworthy Autonomous Vehicles

TL;DR

This paper presents a data-driven model of human drivers for autonomous vehicles, integrating participatory design and artificial awareness to enhance trustworthiness and safe collaboration in multi-agent systems.

Contribution

It introduces a large-scale user study on human-AV interaction and develops Markov chain models of human drivers based on the data, emphasizing transparency effects.

Findings

AV transparency influences user behavior

Demographics affect driver responses

Models show environment impacts transition probabilities

Abstract

Current robotic agents, such as autonomous vehicles (AVs) and drones, need to deal with uncertain real-world environments with appropriate situational awareness (SA), risk awareness, coordination, and decision-making. The SymAware project strives to address this issue by designing an architecture for artificial awareness in multi-agent systems, enabling safe collaboration of autonomous vehicles and drones. However, these agents will also need to interact with human users (drivers, pedestrians, drone operators), which in turn requires an understanding of how to model the human in the interaction scenario, and how to foster trust and transparency between the agent and the human. In this work, we aim to create a data-driven model of a human driver to be integrated into our SA architecture, grounding our research in the principles of trustworthy human-agent interaction. To collect the data necessary for creating the model, we conducted a large-scale user-centered study on human-AV interaction, in which we investigate the interaction between the AV's transparency and the users' behavior. The contributions of this paper are twofold: First, we illustrate in detail our human-AV study and its findings, and second we present the resulting Markov chain models of the human driver computed from the study's data. Our results show that depending on the AV's transparency, the scenario's environment, and the users' demographics, we can obtain significant differences in the model's transitions.