As Autonomous vehicles (AVs) are entering shared roads, the challenge of designing and implementing a completely autonomous vehicle is still open. Aside from technological issues regarding how to manage the complexity of the environment, AVs raise difficult legal issues and ethical dilemmas, especially in unavoidable accident scenarios. In this context, a vast speculation depicting moral dilemmas has developed in recent years. A new perspective was proposed: an “Ethical Knob” (EK), enabling passengers to ethically customise their AVs, namely, to choose between different settings corresponding to different moral approaches or principles. In this contribution we explore how an AV can automatically learn to determine the value of its “Ethical Knob” in order to achieve a trade-off between the ethical preferences of passengers and social values, learning from experienced instances of collision. To this end, we propose a novel approach based on a genetic algorithm to optimize a population of neural networks. We report a detailed description of simulation experiments as well as possible applications.
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