Predictive Vehicle Control for Real-Time Operational Restrictions
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Solution Overview
Problem
Existing vehicle systems lack effective real-time monitoring and control mechanisms to predict and adapt operational modes based on sensor data and vehicle status, leading to potential component failures and safety risks during travel.
Innovation Solution
A vehicle control system that utilizes a predictive model to analyze sensor data and vehicle data to determine a predictive vehicle status, enabling adaptive operational mode changes, such as speed adjustments or operational restrictions, to ensure safe and efficient travel.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time monitoring and predictive analysis systems are implemented, then vehicle safety and reliability are improved, but device complexity and computational requirements increase
Solution Approach 1:
The system performs preliminary predictive analysis of vehicle status using sensor data and predictive models before actual failures or safety issues occur. The control circuit continuously evaluates sensor data against predictive models to anticipate potential problems, allowing preventive operational mode changes before critical failures happen, thereby improving safety without requiring overly complex real-time intervention systems.
Solution Approach 2:
The patent introduces a control circuit as an intermediary component that bridges sensor data collection and operational decision-making. This control circuit processes sensor data through predictive models and determines operational mode changes, serving as a manageable intermediate layer that improves reliability while keeping the overall system architecture tractable and not excessively complex.
2Reliability
If operational mode changes are implemented based on predictive status, then component failure prevention is improved, but response time and operational flexibility may be reduced
Solution Approach 1:
The system dynamically adjusts operational modes based on real-time predictive status evaluation. The control circuit continuously monitors sensor data and automatically transitions between operational modes as conditions change, enabling adaptive response that prevents component failures while maintaining operational flexibility. The system can ramp down operational intensity gradually or switch modes based on predicted component status, optimizing both prevention and response time.
3Measurement precision
If continuous sensor data collection and predictive modeling are performed, then vehicle status accuracy is improved, but energy consumption and computational load increase
Solution Approach 1:
The predictive models serve multiple functions: they evaluate current vehicle status, predict future component conditions, and determine appropriate operational mode changes. This multi-functionality allows the system to achieve high measurement precision through a single computational framework rather than requiring separate specialized systems for each function, thereby reducing overall computational energy requirements while maintaining accurate vehicle status assessment.
Data Source
AI summary
A control system is provided to receive a plurality of vehicle data from sensors in a vehicle communication network. The control system is disposed on a vehicle in a vehicle system and evaluates a plurality of components of the vehicle system with a predictive model associated with the sensor data. The control system may determine a vehicle status based on the predictive model and modify an operational plan or impose an operational restriction through a propulsion system of the vehicle.