Liability Design for Autonomous Vehicles and Human-Driven Vehicles: A Hierarchical Game-Theoretic Approach
Xuan Di, Xu Chen, Eric Talley

TL;DR
This paper develops a hierarchical game-theoretic model to analyze the interactions between autonomous vehicles, human drivers, and policymakers, aiming to optimize liability rules and improve road safety amid technological uncertainty.
Contribution
It introduces a unified game-theoretic framework capturing complex interactions and provides insights into liability design and behavioral effects in mixed traffic environments.
Findings
Human drivers may develop moral hazard under perceived safety improvements.
Optimal liability rules can enhance social welfare in autonomous vehicle deployment.
The model offers analytical tools for policymakers to regulate AVs effectively.
Abstract
Autonomous vehicles (AVs) are inevitably entering our lives with potential benefits for improved traffic safety, mobility, and accessibility. However, AVs' benefits also introduce a serious potential challenge, in the form of complex interactions with human-driven vehicles (HVs). The emergence of AVs introduces uncertainty in the behavior of human actors and in the impact of the AV manufacturer on autonomous driving design. This paper thus aims to investigate how AVs affect road safety and to design socially optimal liability rules for AVs and human drivers. A unified game is developed, including a Nash game between human drivers, a Stackelberg game between the AV manufacturer and HVs, and a Stackelberg game between the law maker and other users. We also establish the existence and uniqueness of the equilibrium of the game. The game is then simulated with numerical examples to…
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