A foundational aspect of driving – for both human-piloted and autonomous vehicles – is understanding the rules of the road. In ideal scenarios, vehicles and other road users obey all relevant rules without conflict. For example, a driver will commonly take the right of way at a green light, or avoid a lane change across solid lane markings.
In real-world driving conditions, however, conflicts frequently arise between rules, particularly in urban environments. A vehicle may be required to stop at a green light in order to avoid a jaywalking pedestrian, or a double-parked truck may necessitate a detour across solid lane markings. In such scenarios, “common practice” rules are followed, while other rules are deprioritized, to ensure safe and efficient traffic flow.
When rules conflict – that is, when safety, common practice, cultural norms, or ethical decisions take priority over standard road rules – how should autonomous vehicles behave? The answer: Just as a safe human driver would.
To achieve consistent driving behavior in such instances, Aptiv’s approach, called Structured AI, is a rigorous system using rich data collection and machine learning to encode logical descriptions of driving rules and preferences. A critical element of Structured AI is Aptiv’s Rulebooks, which prioritizes these rules, common practices, and all manners of driving preferences to ensure the safety and comfort take priority above all else.
Such a system allows an AV to determine the safest possible action, as it is it is impossible to satisfy all rules of the road due to the sometimes unpredictable actions of other road users. An AV Rulebook can vary from country to country, enabling developers to scale AVs globally without re-writing (or re-training) decision-making systems.
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SOURCE: Aptiv
