Computational law-enabled autonomous and semi-autonomous vehicles (terrestrial, aerial and marine) (ACLVs) with “unsupervised learning” capabilities can drive the construct of a smart transportation-centric ontology (TCO).

The ACLVs’ unsupervised learning capabilities are driven by artificial neural networks focused on machine and deep learning (Transformative Computing). As the ACLV is routinely exposed in the course of its operation to a virtually infinite data set, its opportunity to learn is similarly boundless. This operational environment strengthens the ACLV’s analytical prowess, concomitantly constructing the TCO, injecting it with relevant operational variables (Learnings) that can be cross-referenced in real time to already-present permitted operational parameters (POP), which are represented by relevant laws and regulations. Once the Learnings are vetted, that is, they are not incompatible with POP, they mature into a new POP.

The “smart” characteristic is attributed to the TCO’s self-regulatory capability. To remain consistently operationally relevant, the TCO is designed to self-regulate through an autonomous, internal data governance structure. This system takes the form of a “data scoring” feature, one that acts in the same way as a rating system which continuously analyzes Learnings and POP relevancy. Any instance in which the TCO determines that a given Learning or POP fails to meet the necessary criteria, it is flagged for review. This action can result in its removal or modification. Initially, this action would be decided by humans, but as the TCO becomes more sophisticated, more powerful by virtue of, for example, quantum computing, the entire process could be, indeed should be, managed autonomously.

Ultimately, the smart TCO will serve to govern the optimal operation of the ACLV. This means the ACLV is incapable of deviating from a secure operational pattern; i.e., a behavior that meets operator intention, is predictable and POP compliant. Once it is secured with a blockchain, the TCO’s integrity (think FIPS 199) is assured on a continuous basis, which in return keeps operational confidence, which is critical for its continued existence.