Vehicle Motion Data Filtering for Skid-Resilient Control
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Solution Overview
Problem
Existing vehicular measurement systems often include inaccurate data due to factors like wheel slippage and skidding, leading to incorrect vehicle operation and potential safety hazards.
Innovation Solution
A method and system that utilize a motion data model to filter out or de-emphasize potentially erroneous measurements by comparing sensor data against expected values, discarding or adjusting data that do not conform to the model, and incorporating alternative data sources for accurate vehicle control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple data sources (sensors, telematics, ECU) are integrated to improve data comprehensiveness, then the quality and accuracy of vehicle operation recommendations improve, but the system complexity and data processing burden increase
Solution Approach 1:
The system segments data processing by creating separate modules for different data sources (sensor data module, telematics data module, ECU data module) and processes them through distinct pipelines before integration. This allows each data type to be handled with specialized processing logic, improving accuracy while managing complexity through modular architecture.
Solution Approach 2:
The server acts as an intermediary that receives, validates, and processes data from multiple heterogeneous sources before generating recommendations. The server mediates between diverse data formats and the vehicle client, standardizing data processing and reducing the complexity burden on individual components.
2Speed
If real-time data processing is implemented to improve response speed, then vehicle operation recommendations are provided more quickly, but the computational load and energy consumption increase
Solution Approach 1:
The system performs preliminary data validation, filtering, and preprocessing at the data source level and at the server before transmission. By preparing data in advance and pre-processing it server-side, the system reduces the computational load during real-time operation, enabling fast response with lower energy consumption during critical decision-making periods.
Solution Approach 2:
The system processes data at different levels of detail based on operational needs. Not all data requires full real-time processing - the system applies partial processing to less critical data streams while maintaining full real-time processing for safety-critical parameters, optimizing the balance between response speed and energy consumption.
Data Source
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AI summary
Methods, apparatuses, and computer-readable media are described. In one example, a method of controlling a vehicle comprises: receiving, using one or more sensors, a first set of measurements of a set of physical attributes of the vehicle in a motion; determining, based on a motion data model that defines a set of relationships among the set of physical attributes of the vehicle in the motion and based on the first set of measurements, a set of expected measurements of the set of physical attributes; determining whether to use an entirety of the first set of measurements to control an operation of the vehicle based on comparing the first set of measurements and the set of expected measurements; and responsive to determining not to use the entirety of the first set of measurements, controlling the operation of the vehicle based on a second set of measurements.