A micromouse’s rational agent is made to make decisions based on sensory input and predetermined goals in order to efficiently navigate a maze. Actuators are used to regulate movement, a decision-making algorithm is used, and sensors are used to sense the surroundings. Sensors that identify barriers and obstructions, such infrared or ultrasonic sensors, give the decision-making system access to real-time data. Based on the configuration of the maze and the mouse’s present position, this algorithm determines the best course and instructs the actuators to follow it.
A autonomous car can be created using the same technological concepts, but on a bigger scale and with more advanced sensors and algorithms. A autonomous automobile uses GPS, cameras, LiDAR, and radar to sense its surroundings. It then uses sophisticated machine learning algorithms to interpret this information and make driving judgements.
Although the ideas are similar, an autonomous vehicle is not the same as a micromouse in terms of scale and complexity. Nonetheless, the principles apply to both cases since the basic concepts of observation of the environment, decision-making, and actuation do not change.