Considering Perspectives for Automated Driving Ethics: Collective Risk in Vehicular Motion Planning

2026-02-26

AI Summary

This study addresses the ethical limitations of conventional autonomous driving motion planning approaches, which typically minimize risk solely from the ego vehicle’s perspective while neglecting the risk perceptions of other road users. To overcome this shortcoming, the authors propose a novel motion planning framework that dynamically switches the risk assessment reference among multiple traffic participants, thereby incorporating multi-agent risk perspectives into the decision-making process for the first time. By minimizing collective risk rather than individual risk, the method achieves a synergistic optimization of ethical fairness and operational efficiency. Built upon a multi-perspective risk modeling architecture, the approach enables real-time adaptation of driving strategies to enhance behavioral predictability. Experimental results demonstrate that the proposed strategy effectively reduces overall traffic risk, improves societal acceptance, and, in certain scenarios, increases traffic throughput.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Multiagent Systems: Multiagent Planning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems

Abstract

Recent automated vehicle (AV) motion planning strategies evolve around minimizing risk in road traffic. However, they exclusively consider risk from the AV's perspective and, as such, do not address the ethicality of its decisions for other road users. We argue that this does not reduce the risk of each road user, as risk may be different from the perspective of each road user. Indeed, minimizing the risk from the AV's perspective may not imply that the risk from the perspective of other road users is also being minimized; in fact, it may even increase. To test this hypothesis, we propose an AV motion planning strategy that supports switching risk minimization strategies between all road user perspectives. We find that the risk from the perspective of other road users can generally be considered different to the risk from the AV's perspective. Taking a collective risk perspective, i.e., balancing the risks of all road users, we observe an AV that minimizes overall traffic risk the best, while putting itself at slightly higher risk for the benefit of others, which is consistent with human driving behavior. In addition, adopting a collective risk minimization strategy can also be beneficial to the AV's travel efficiency by acting assertively when other road users maintain a low risk estimate of the AV. Yet, the AV drives conservatively when its planned actions are less predictable to other road users, i.e., associated with high risk. We argue that such behavior is a form of self-reflection and a natural prerequisite for socially acceptable AV behavior. We conclude that to facilitate ethicality in road traffic that includes AVs, the risk-perspective of each road user must be considered in the decision-making of AVs.

Problem

• automated driving ethics
• collective risk
• motion planning
• risk perspective
• road user

Innovation

• collective risk
• motion planning
• ethical decision-making
• perspective-awareness
• autonomous driving