Autonomous Driving Systems (ADS) represent one of the most complex realizations of artificial intelligence in real-world, safety-critical environments. Their development sits at the intersection of perception, cognition, decision-making, learning, and ethical accountability. This article presents an extensive, theoretically grounded investigation into autonomous driving intelligence by synthesizing contemporary engineering research with foundational theories of machine intelligence and cognition. Drawing strictly from the provided references, the study bridges modern deep reinforcement learning frameworks, motion planning, anomaly detection, perception systems, and infrastructure support with classical intellectual foundations such as the Turing Test, early neural models, and theories of learning and behavior.

The abstract notion of machine intelligence, historically framed through philosophical inquiry and behavioral criteria, has evolved into a measurable, operational phenomenon in autonomous vehicles. Yet, this evolution raises unresolved questions regarding safety, generalization, explainability, and the very definition of “intelligence” when embodied in machines navigating dynamic human environments. By integrating surveys on deep reinforcement learning for driving, motion planning paradigms, perception systems, and safety analyses with classical theories from Turing, Hebb, McCulloch, and Pitts, this article argues that autonomous driving is not merely a technical achievement but a practical testbed for machine intelligence itself.

The methodology of this work is qualitative and analytical, involving deep interpretive synthesis rather than empirical experimentation. Each referenced study is examined in detail, not as isolated contributions but as interdependent components of a broader intelligence architecture. Results are presented as conceptual findings: the emergence of hierarchical intelligence, the tension between learning and control, the role of anomaly detection as a form of machine self-awareness, and the implications of human-like evaluation criteria in assessing autonomous systems.

The discussion critically evaluates current limitations in ADS, including brittleness, data dependence, and ethical opacity, while proposing future research directions grounded in lifelong learning, hybrid symbolic–neural reasoning, and intelligence evaluation beyond task performance. The article concludes that autonomous driving systems represent the most concrete instantiation of the question “Can machines think?” in applied form, transforming philosophical debates into engineering imperatives.