Hierarchical Intelligence for Self-Aware Autonomous Mobility
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Solution Overview
Problem
Conventional autonomous systems rely heavily on human intervention and are not fully autonomous, lacking the ability to operate independently with advanced autonomy.
Innovation Solution
The implementation of a Hierarchical Intelligence Model (HIM) that utilizes vision and image processing to aggregate intelligence from reflexive, imperative, adaptive, and cognitive elements, enabling autonomous systems like self-driving vehicles to operate at SAE automation levels 4 or 5, with decision support for collision avoidance and combat response through machine learning and algorithmic logic.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional autonomous systems use human-in-the-loop control, then system reliability is improved through human oversight, but extent of automation deteriorates as systems cannot operate completely autonomously
Solution Approach 1:
The system segments autonomous operation into multiple intelligence levels (reflexive, imperative, adaptive, cognitive) that can function independently. Each level handles specific aspects of autonomous decision-making, allowing the system to operate completely autonomously while maintaining reliability through distributed intelligence rather than centralized human control.
Solution Approach 2:
The autonomous system performs self-monitoring, self-diagnosis, and self-correction across all intelligence levels. The cognitive intelligence level provides self-awareness and self-regulation, enabling the system to maintain reliability through self-service mechanisms without requiring human intervention.
2Adaptability or versatility
If autonomous systems process complex environmental data for advanced autonomy, then decision-making capability is improved, but device complexity increases
Solution Approach 1:
The system divides complex decision-making into four distinct intelligence levels (reflexive, imperative, adaptive, cognitive), each processing specific types of data and making decisions appropriate to their capability. This segmentation allows the system to handle complex environmental data effectively while managing device complexity through modular architecture.
Solution Approach 2:
The system adds the dimension of hierarchical intelligence levels to the processing architecture. Instead of a flat complex processing structure, data flows through multiple dimensional layers of intelligence, with each level adding value and reducing complexity for the next level, ultimately enabling advanced decision-making without proportional increases in overall system complexity.
Data Source
AI summary
Embodiments may provide techniques for operating autonomous systems with improved autonomy so as to operate largely or completely, autonomously. For example, in an embodiment, a self-aware mobile system may comprise a vehicle, vessel, or aircraft comprising: at least one communication device configured to transmit and receive data so as communicate with at least one autonomous sensor platform, and at least one computer system configured to receive data from the at least one autonomous sensor platform and, using the received data, to generate data to implement autonomous movement corresponding to SAE automation level 4 or level 5 using processing in accordance with a Hierarchical Intelligence Model and to generate data to communicate with a human regarding operations of the vehicle, vessel, or aircraft, and at least one autonomous sensor platform comprising: at least one communication device configured to transmit and receive data so as communicate with the vehicle, vessel, or aircraft.