Vehicle Control Directives Tuned to Driving Style and Environment
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Solution Overview
Problem
Existing vehicle control systems rely on standardized driving scenarios for generating control directives, which may not account for specific driving styles or unique features of particular driving environments, leading to suboptimal vehicle performance.
Innovation Solution
A vehicle dynamics model and machine learning optimization program are used to input operation data, allowing for the generation of tailored control directives that improve vehicle operation in specific driving environments, with the ability to update and refine these directives based on real-world data from multiple vehicles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If standardized driving scenarios are used for generating control directives, then device complexity is reduced and ease of manufacture is improved, but adaptability to specific driving styles and environments deteriorates
Solution Approach 1:
The system enables vehicles to automatically generate their own customized control directives by collecting and analyzing their own operation data, without requiring manual calibration or external intervention. The vehicle self-adjusts its control parameters based on its unique driving patterns and environmental conditions
Solution Approach 2:
The system continuously collects operation data from vehicle sensors, compares actual performance with predicted performance from the dynamics model, and uses optimization programs to generate updated control directives. This closed-loop feedback mechanism enables continuous adaptation to changing driving conditions and styles
2Manufacturing precision
If standardized control directives are used for all vehicles, then ease of operation is improved and device complexity is reduced, but manufacturing precision and performance optimization deteriorate
Solution Approach 1:
The system tailors control directives to each specific vehicle and its unique operating conditions, rather than applying uniform control parameters across all vehicles. Each vehicle receives customized control instructions optimized for its specific driving style, environment, and performance characteristics
Solution Approach 2:
The system dynamically adjusts control parameters based on analyzed operation data and optimization results. Control directives are continuously refined by modifying parameters such as acceleration profiles, braking patterns, and gear selection based on what works best for each specific vehicle and driving scenario
3Measurement precision
If vehicle operation relies on standardized data acquisition, then device complexity is reduced, but measurement precision and timeliness of accurate data deteriorate
Solution Approach 1:
The system divides the data acquisition and processing function into multiple independent components: sensor data collection, data transmission to external servers, dynamics model prediction, optimization program execution, and control directive generation. This modular segmentation enables high measurement precision through specialized components while managing overall system complexity through clear separation of functions
Data Source
AI summary
Operation data from one or more vehicle subsystems are input to a vehicle dynamics model. Predicted operation data of the one or more vehicle subsystems are output from the vehicle dynamics model. The operation data and the predicted operation data are input to an optimization program that is programmed to output control directives for the one or more vehicle subsystems. One or more vehicle subsystems are operated according to the output control directives.