When AI takes the wheel: AI-defined vehicles principles and pitfalls

As introduced by Asimov in "I, Robot", intelligent machines are characterized as systems capable of performing tasks that traditionally require human intelligence, such as autonomous decision-making and driving. In this context, modern road vehicles can increasingly be understood as robotic systems endowed with progressively sophisticated functionalities, operational flexibility, and, crucially, the capacity to learn and evolve autonomously over time. Building on this perspective, AI-defined vehicles (AIDVs) are emerging in both the automotive industry and the research community as a next stage in vehicle evolution, where interaction capabilities, adaptability, sustainability, and ethical governance are embedded as core design principles rather than treated as auxiliary features. This work aims to introduce this new class of vehicles and provide an analysis of their defining principles, capabilities, and challenges. This article contributes a first conceptualization of AIDVs, outlines their defining principles, and distinguishes them from existing vehicle classes. Then, it identifies the risks introduced by adaptive AI and proposes a preliminary roadmap for their integration into Intelligent Transportation Systems (ITS).