Autonomous Conveyance Control Updates for Rare Hazard Mitigation
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Solution Overview
Problem
Traditional approaches to autonomous device control systems lack the capability for real-time, shared learning across identical vehicles or machines, limiting their ability to adapt quickly to rare or unforeseen situations, and do not effectively incorporate safety requirements that arise from unpredictable events.
Innovation Solution
A networked system that enables real-time or near real-time sharing of learning updates across autonomous control devices, utilizing a centralized infrastructure to generate, evaluate, and distribute updates, and incorporates neural matrices for processing and disseminating safety-critical programming to handle rare events, with co-processing services for urgent hazards beyond hardware capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional control systems are used with individual learning only, then each device operates independently with its own learned experiences, but shared learning across multiple devices cannot be achieved in real-time
Solution Approach 1:
The patent combines individual device learning with centralized infrastructure learning to create a shared learning system. The centralized infrastructure aggregates learning from multiple devices and distributes updates network-wide, merging previously separate learning processes into a unified system that achieves real-time knowledge sharing across all devices.
Solution Approach 2:
The centralized infrastructure acts as an intermediary between individual devices and the network. It receives learning data from devices, processes and validates it, then distributes updated learning to all devices. This intermediary enables efficient shared learning without requiring direct peer-to-peer communication between all devices.
2Productivity
If traditional software update processes are used, then learning updates can be delivered to devices, but the process is slow and not suitable for rare or unforeseen situations
Solution Approach 1:
The system performs preliminary validation and testing of learning updates at the centralized infrastructure before distributing them to devices. By pre-processing and verifying updates centrally, the system ensures safety and reliability while enabling rapid deployment to all devices simultaneously, eliminating the need for slow individual device updates.
Solution Approach 2:
The learning update process operates continuously rather than in discrete batches. The centralized infrastructure constantly receives learning from devices, processes it, and distributes updates in real-time, ensuring continuous improvement of safety performance across the entire device fleet without interruption to operations.
3Reliability
If control programming is designed to handle rare and unforeseen situations, then safety is improved, but the complexity of predicting and programming all possible hazards increases
Solution Approach 1:
The system uses self-service by allowing devices to contribute their own learning experiences to the centralized infrastructure. Rather than requiring engineers to predict and program all possible hazards, the system automatically collects real-world data from devices, processes it centrally, and generates updates that handle rare and unforeseen situations that actually occur in operation.
Solution Approach 2:
The centralized infrastructure implements continuous feedback loops where device performance data and learned experiences are constantly monitored, analyzed, and used to generate safety updates. This feedback mechanism enables the system to adapt to new hazards and situations dynamically, improving safety without requiring upfront programming of all possible scenarios.
Data Source
AI summary
Disclosed subject matter identifies, characterizes, and mitigates previously unforeseen safety hazards that are likely to be encountered by autonomous conveyances—finding these hazards, assessing their potential safety impact, modifying the design to mitigate them should they occur, disseminating updated design programming to all units, including those under construction or those already in the field, and including those hazard mitigations of high severity that exceed the maximum capabilities of the controller as manufactured. These hazards can include rare, infrequent and unforeseen hazards by monitoring conveyances already in the field, gathering data from autonomous conveyances, such as those using a design being updated, and data obtained from those using other autonomous designs in the field. By obtaining data from non-autonomous conveyances, as supplied by their drivers and operators, reporting real-time via a smartphone application, categories of rare, infrequent or unforeseen hazards can be integrated into modified designs.