Opportunities and Challenges of Operating Semi-Autonomous Vehicles: A Layered Vulnerability Perspective

TL;DR

This paper explores the layered vulnerabilities faced by human operators of semi-autonomous vehicles, revealing how psychological, operational, and social factors interact to influence safety and trust in supervision.

Contribution

It applies Luna's layered vulnerability framework to semi-autonomous vehicle operators, highlighting the dynamic and situational nature of their vulnerabilities through qualitative analysis.

Findings

Vulnerability arises from the convergence of psychological, operational, and social layers.

Situational factors cause fluctuating supervisory demands and risk management capacity.

Design and regulation should address multiple vulnerability layers simultaneously.

Abstract

This study examines how vulnerability is produced for human operators of Tesla's Full Self-Driving (FSD), a Level 2 semi-autonomous vehicle (SAV) system, by applying Florencia Luna's layered vulnerability framework. While existing road safety models conceptualize vulnerability as a fixed attribute of external road users, emerging evidence suggests that semi-autonomous vehicle operators themselves experience dynamic and situational vulnerability as they supervise automated systems that they do not fully control. To investigate this phenomenon, we conducted semi-structured interviews with 17 active FSD users, analyzing their accounts through a combined deductive-inductive coding process aligned with Luna's framework. Findings reveal three interacting layers of operator vulnerability, namely psychological, operational, and social. Vulnerability emerged not from any single layer but from how these layers converged in specific situations, creating fluctuating supervisory demands and uneven capacity to recognize and manage risk. The findings extend debates on contextual trust calibration, automation complacency, and meaningful human control by demonstrating how factors commonly treated as liabilities such as trust or informal learning, can both increase and mitigate vulnerability depending on context. This analysis determines the need for design and regulatory interventions that address psychological, operational, and social conditions together rather than in isolation, and highlights how responsibility is implicitly shifted onto individual operators within inadequately supported supervisory regimes.