(D) Reinforcement Learning — Parking a car involves trial-and-error decision-making where the AI learns by receiving rewards for correct actions and penalties for mistakes, which is the core of Reinforcement Learning.
Source: Chapter 2, Section 2.2 – Reinforcement Learning
The textbook explicitly lists "Parking a car" as an example of Reinforcement Learning and states it "enables the computer to make a series of decisions that maximize a reward metric without human intervention." Supervised learning needs labelled data; unsupervised finds patterns; transfer learning is not covered as a main ML category here. Reinforcement Learning fits best because parking requires sequential decisions optimised through reward/penalty feedback.