Overview

This report explores the adaptability gap between humans and artificial intelligence (AI), focusing on why autonomous vehicles (AVs) still fail to match human drivers in real-world conditions. While AI excels in data processing and perception, humans possess a computational self, an intrinsic self-awareness that enables rapid, context-sensitive adaptation. This uniquely human capability remains beyond the reach of machines

The Computational Self and the Foundation of Human Adaptability

The promise of autonomous vehicles (AVs) is predicated on the assumption that they will eventually surpass human drivers in safety and efficiency by eliminating human error, which is the final failure in the causal chain for the vast majority of vehicular crashes.

According to the National Highway Traffic Safety Administration’s (NHTSA) National Motor Vehicle Crash Causation Survey (NMVCCS), driver error is the final failure in the chain of events leading to approximately 94% of motor vehicle crashes (Singh, 2015).

If AVs could perfectly see and perceive the road, and never got tired, drunk, or distracted, they might prevent about one-third (34%) of all crashes. But here’s the catch, roughly two-thirds (66%) of crashes would still remain, because they stem from decision-making, planning, or predicting errors, not from perception problems.

This distinction between what machines can see and what they can understand highlights a deeper limitation,perception alone doesn’t equal adaptability.