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How does artificial intelligence help an automated vehicle make driving decisions?
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At the top of the machine is a stack of punched cards that would be fed into the loom to control the weaving pattern. This method of automatically issuing machine instructions was employed by computers well into the 20th century.
Artificial intelligence (AI) and machine learning are foundational to autonomous vehicle systems, enabling them to make driving decisions without requiring specific, pre-programmed instructions for every potential scenario encountered on the road.12
The process functions through the following mechanisms:
- Data Processing: Vehicles utilize remote-sensing technologies—including radar, GPS, cameras, and lidar—to monitor their surroundings and construct a 3-D map of the environment, which includes road signs, traffic lights, pedestrians, and other vehicles.2
- Algorithmic Improvement: Through machine learning, vehicles are trained on complex data sets. This training allows the system to refine its underlying algorithms, effectively learning how to navigate diverse road conditions over time.12
- Decision Making: AI processes the continuous stream of data from sensors to make real-time adjustments to steering, acceleration, braking, and cruising speed.2
While these systems are highly advanced, current technology still faces significant challenges, such as the need for "common sense" to handle complex interactions with other road users safely.1