Vehicle State Prediction via Network Node Encoding
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Solution Overview
Problem
Existing vehicle state prediction systems face a trade-off between data volume and prediction accuracy, where a large number of combinations of vehicle situations, driver commands, and in-vehicle device notices lead to excessive data, compromising prediction reliability.
Innovation Solution
A vehicle state prediction system that encodes vehicle states using time-series information, defines symbols as nodes, and generates a network structure by accumulating node and link appearances, allowing for efficient data management and reduced calculation load while predicting future vehicle states with high reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all combinations of vehicle situations, driver commands, and in-vehicle device notices are recorded in the management selection table, then prediction accuracy of driver management is improved, but data volume becomes excessively large
Solution Approach 1:
The patent combines multiple vehicle signals (engine state, transmission state, brake state, steering state, sensor states) into a unified vehicle state representation. By merging these diverse signals into a consolidated state model, the system reduces the overall data volume while preserving the essential information needed for accurate prediction of driver management behavior.
2Quantity of substance
If the number of combinations in the management selection table is limited to reduce data volume, then data storage requirements are reduced, but prediction accuracy of driver management deteriorates
Solution Approach 1:
The patent extracts and identifies the most critical vehicle signals that have the greatest impact on driver management behavior. By selecting only the essential signals (engine state, transmission state, brake state, steering state, and relevant sensor states) and excluding redundant information, the system achieves accurate predictions with reduced data volume.
Solution Approach 2:
The patent transforms the representation of vehicle states by encoding multiple signal parameters into a unified state model. This parameter transformation allows the system to capture complex vehicle conditions using a compact representation, maintaining prediction accuracy while minimizing data storage requirements.
3Reliability
If comprehensive vehicle signal combinations are used to ensure prediction reliability, then calculation load increases, but if signals are limited to reduce calculation load, then prediction reliability decreases
Solution Approach 1:
The patent segments the vehicle state prediction process into distinct components: identifying critical signals, encoding vehicle states, storing state transitions, and predicting future states. This segmentation allows each component to be optimized independently, reducing overall calculation load while maintaining prediction reliability through systematic processing of essential information.
Data Source
AI summary
A state predicting circuitry predicts a route showing a future change in the vehicle state from among a plurality of routes from a first node to a second node. The first node corresponds to the current vehicle state. The second node corresponds to the vehicle state after having transitioned a predetermined number of times from the first node. The state predicting circuitry predicts a route in which at least one of an accumulated value of the node that exists in the routes and an accumulated value of the link that exists in the routes is greatest, from among the plurality of routes.