In this paper, we argue that solutions to normative challenges associated with autonomous driving, such as real-world trolley cases or distributions of risk in mundane driving situations, face the problem of reasonable pluralism: Reasonable pluralism refers to the fact that there exists a plurality of reasonable yet incompatible comprehensive moral doctrines within liberal democracies. The corresponding problem is that a politically acceptable solution cannot refer to only one of these comprehensive doctrines. Yet a politically adequate solution to the normative challenges (...) of autonomous driving need not come at the expense of an ethical solution, if it is based on moral beliefs that are (1) shared in an overlapping consensus and (2) systematized through public reason. Therefore, we argue that a Rawlsian justificatory framework is able to adequately address the normative challenges of autonomous driving and elaborate on how such a framework might be employed for this purpose. (shrink)

In 2017, the German ethics commission for automated and connected driving released 20 ethical guidelines for autonomous vehicles. It is now up to the research and industrial sectors to enhance the development of autonomous vehicles based on such guidelines. In the current state of the art, we find studies on how ethical theories can be integrated. To the best of the authors’ knowledge, no framework for motion planning has yet been published which allows for the true implementation of any practical (...) ethical policies. This paper makes four contributions: Firstly, we briefly present the state of the art based on recent works concerning unavoidable accidents of autonomous vehicles and identify further need for research. While most of the research focuses on decision strategies in moral dilemmas or crash optimization, we aim to develop an ethical trajectory planning for all situations on public roads. Secondly, we discuss several ethical theories and argue for the adoption of the theory “ethics of risk.” Thirdly, we propose a new framework for trajectory planning, with uncertainties and an assessment of risks. In this framework, we transform ethical specifications into mathematical equations and thus create the basis for the programming of an ethical trajectory. We present a risk cost function for trajectory planning that considers minimization of the overall risk, priority for the worst-off and equal treatment of people. Finally, we build a connection between the widely discussed trolley problem and our proposed framework. (shrink)

Dilemma situations involving the choice of which human life to save in the case of unavoidable accidents are expected to arise only rarely in the context of autonomous vehicles. Nonetheless, the scientific community has devoted significant attention to finding appropriate and acceptable automated decisions in the event that AVs or drivers of AVs were indeed to face such situations. Awad and colleagues, in their now famous paper “The Moral Machine Experiment”, used a “multilingual online ‘serious game’ for collecting large-scale data (...) on how citizens would want AVs to solve moral dilemmas in the context of unavoidable accidents.” Awad and colleagues undoubtedly collected an impressive and philosophically useful data set of armchair intuitions. However, we argue that applying their findings to the development of “global, socially acceptable principles for machine learning” would violate basic tenets of human rights law and fundamental principles of human dignity. To make its arguments, our paper cites principles of tort law, relevant case law, provisions from the Universal Declaration of Human Rights, and rules from the German Ethics Code for Autonomous and Connected Driving. (shrink)