Adaptable vehicle monitoring system
Inventors
Ryan, Jason Christopher • Duda, Jessica Edmonds • Musto, Andrew
Assignees
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Publication Number
US-11151810-B2
Publication Date
2021-10-19
Expiration Date
Abstract
An adaptable vehicle monitoring system is disclosed. The system includes a core platform having a state monitoring subsystem and a feedback subsystem. The core platform interconnects a perception subsystem, a knowledge acquisition subsystem, and a user interface. The perception subsystem is configured to acquire current vehicle state data from instruments of a vehicle. The knowledge acquisition subsystem includes a context awareness subsystem configured to determine a current vehicle context. The state monitoring subsystem is configured to derive a current vehicle state based at least in part on the vehicle state data and vehicle context. The knowledge acquisition subsystem further includes a database subsystem configured to store the current vehicle state data, current vehicle context, and current vehicle state. The trend monitoring subsystem is configured to analyze the one or more stored vehicle state data, stored vehicle contexts, and stored vehicle states to identify one or more trends. The feedback subsystem is configured to prepare operator and deliver operator feedback via the user interface based at least in part on a comparison between the current vehicle state and/or current trend and an expected vehicle state and/or previously identified trends.
Core Innovation
An adaptable vehicle monitoring system provides operator feedback to operate a vehicle. The system includes a context awareness subsystem configured to determine a current vehicle context that reflects an operational mode of the vehicle, and a state monitoring subsystem configured to derive a current vehicle state based at least in part on current vehicle state data. The system correlates the current vehicle state data with the current vehicle context as a function of at least one parameter of the vehicle.
The state monitoring subsystem analyzes, via one or more processors, the current vehicle state data and the current vehicle context using one or more machine-learning techniques to identify one or more trends. Based on the identified trends and one or more stored vehicle states, the system derives an expected vehicle state. The system prepares operator feedback based at least in part on a comparison between the current vehicle state and the expected vehicle state.
The system generates the prepared operator feedback via a user interface configured to provide the operator feedback to the operator. The disclosed approach supports analysis using stored vehicle states and trends, enabling expected vehicle state derivation from stored information and trend identification using machine-learning.
Claims Coverage
The document includes two independent claims (system and method), each centered on seven core inventive capabilities: context awareness, state derivation from state data, correlation with context as a function of a vehicle parameter, machine-learning trend identification, expected vehicle state derivation from stored states and trends, comparison-based operator feedback preparation, and presentation via a user interface.
Context awareness subsystem for operational mode
A context awareness subsystem configured to determine a current vehicle context that reflects an operational mode of the vehicle.
State monitoring subsystem deriving current vehicle state
A state monitoring subsystem configured to derive a current vehicle state based at least in part on current vehicle state data.
Correlation of state data with context as a function of a vehicle parameter
The state monitoring subsystem is operatively coupled with a knowledge acquisition subsystem configured to correlate the current vehicle state data with the current vehicle context as a function of at least one parameter of the vehicle.
Machine-learning trend identification from state data and context
The state monitoring subsystem is configured to analyze, via one or more processors, the current vehicle state data and the current vehicle context using one or more machine-learning techniques to identify one or more trends.
Expected vehicle state derivation from stored vehicle states and trends
The expected vehicle state is derived based at least in part on one or more stored vehicle states and the one or more trends.
Comparison-based operator feedback preparation
A feedback subsystem configured to prepare operator feedback based at least in part on a comparison between the current vehicle state and an expected vehicle state.
User interface providing operator feedback
A user interface configured to provide the prepared operator feedback to the operator.
Determining current vehicle context reflecting operational mode
Determining a current vehicle context, wherein the current vehicle context reflects an operational mode of the vehicle.
Deriving current vehicle state from vehicle state data
Deriving a current vehicle state based at least in part on current vehicle state data.
Correlating vehicle state data with current vehicle context using a vehicle parameter
Correlating the vehicle state data with the current vehicle context as a function of at least one parameter of the vehicle.
Analyzing state data and context using machine-learning to identify trends
Analyzing the current vehicle state data and the current vehicle context using one or more machine-learning techniques to identify one or more trends.
Preparing operator feedback from comparison to expected vehicle state derived from stored states and trends
Preparing operator feedback based at least in part on a comparison between the current vehicle state and an expected vehicle state, wherein the expected vehicle state is derived based at least in part on one or more stored vehicle states and the one or more trends.
Generating operator feedback via user interface
Generating the prepared operator feedback via a user interface.
Across the independent claims, the inventive core is an adaptable vehicle monitoring approach that determines current vehicle context from operational mode, derives current vehicle state from current vehicle state data, correlates that data with the context using at least one vehicle parameter, uses machine-learning to identify trends, derives an expected vehicle state from stored vehicle states and the trends, prepares operator feedback via comparison, and provides the feedback through a user interface.
Stated Advantages
Provides operator feedback based on comparison between current vehicle state and an expected vehicle state derived from stored vehicle states and machine-learning-identified trends.
Documented Applications
Operator feedback for operating a vehicle, including deriving expected vehicle state and generating operator feedback via a user interface based on comparison of current and expected vehicle states.
Vehicle monitoring for adaptable deployment that supports analysis using stored vehicle states and identified trends, including trend analysis that can be performed with online and off-line analysis [procedural detail omitted for safety].
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