Abstract

A system and method for coordinated autonomous vehicle fleet training and assessment across vehicle domains is disclosed. The system comprises a networked fleet of autonomous vehicles operating as self-directed agents with built-in self-preservation, each vehicle configured to receive semantic scenario constraints, interpret and fill gaps using autonomous driving intelligence, independently detect collision risk, and coordinate evasive maneuvers with other vehicles without blindly trusting central commands. A training orchestration engine transmits semantic constraints at multiple specificity levels without specifying complete trajectories. A distributed safety protocol layer implements a three-level detection hierarchy enabling any vehicle or system level to trigger coordinated safety responses. An adaptive difficulty system selects behavior modes along a continuous spectrum from defensive instruction to adversarial training. An assessment module accumulates all actor inputs into a unified session record. A repeatability engine enables objective performance comparison through scenario re-invocation and behavioral replay with multi-ghost overlay. The system supports human operator training, machine learning model co-training, and cross-domain application across automotive, maritime, aerial, and unmanned vehicle domains. Applications include driver education, security training, autonomous fleet development, and operator certification.

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

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