Predictive eHorizon Forecast Table for Fluctuating Computing Power
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Solution Overview
Problem
Driver assistance systems face challenges in providing an accurate eHorizon with sufficient computing power, as the available processor power fluctuates, leading to inefficiencies in data processing and potential disruptions in vehicle control systems.
Innovation Solution
A method that distributes the calculation of eHorizon data across multiple computing units, utilizing a forecast table to store predictive data, allowing for efficient data retrieval and adaptation based on current vehicle position, even with varying computing power, and incorporates look-ahead tables to buffer data for situations with limited resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If constant computing power is allocated to the eHorizon provider, then the eHorizon data can be provided with sufficient accuracy, but the computing resources are wasted during periods of low demand
Solution Approach 1:
The system dynamically adjusts the computing power allocated to the eHorizon provider based on real-time demand conditions. During periods of low computational demand, the provider operates with reduced resources by retrieving pre-calculated data from the forecast table, while during high-demand periods, full computing power is available to recalculate and update the forecast table, thus optimizing the balance between accuracy and resource consumption.
Solution Approach 2:
The system performs preliminary calculations by pre-calculating and storing eHorizon data in a forecast table during periods when computing resources are abundant. This allows the system to retrieve pre-computed data during periods of low demand without sacrificing accuracy, thereby avoiding continuous high-power consumption while maintaining data quality.
2Reliability
If maximum computing power is made available to the eHorizon provider, then sufficient information can be provided even during route changes, but this level of computing power is not required during most operating time
Solution Approach 1:
The system performs preliminary calculations by pre-computing eHorizon data for various route scenarios and storing them in the forecast table. During normal operation, the provider retrieves pre-calculated data rather than performing real-time computations, ensuring reliable information availability while dramatically improving computing resource utilization efficiency.
Solution Approach 2:
The system creates copies of eHorizon data for different possible route scenarios and stores them in the forecast table. When the vehicle follows a pre-calculated path, the system retrieves the corresponding copied data without requiring maximum computing power, thus maintaining reliability while improving productivity.
3Loss of information
If the eHorizon provider calculates data in real-time, then the most current information is provided, but the system cannot handle fluctuations in computing power availability
Solution Approach 1:
The system pre-calculates and stores eHorizon data in the forecast table for various possible vehicle positions and route scenarios. When the vehicle is at a position with pre-calculated data, the system retrieves this information without real-time computation, providing current data while tolerating fluctuations in computing power availability.
Solution Approach 2:
The forecast table acts as an intermediary between real-time vehicle position data and eHorizon information. It stores pre-calculated data that can be quickly retrieved based on current vehicle position, eliminating the need for continuous real-time calculations while maintaining data currency and adapting to computing power fluctuations.
4Loss of information
If the forecast table stores data for all possible paths, then complete information is available, but the memory requirements become excessive
Solution Approach 1:
The system extracts and stores only the most relevant path information in the forecast table based on probability assessments. Instead of storing data for all possible paths, it identifies and retains information for the most likely routes, thus maintaining sufficient path information completeness while significantly reducing memory storage requirements.
Solution Approach 2:
The system applies different storage qualities to different path data in the forecast table. High-probability paths are stored with complete detail, while low-probability paths are either stored with reduced detail or not stored at all. This local differentiation of data quality allows the system to maintain information completeness for critical paths while minimizing overall memory usage.
Data Source
AI summary
The invention relates to a method for optimizing the provision of a predictive eHorizon in a driver assistance system. A method for providing a predictive eHorizon in a driver assistance system is provided, wherein a horizon provider in a driver assistance system makes information about an expected route course available to an assistance application. The horizon provider creates a planning table comprising information about the expected route course and/or comprising data to be provided as a function of the route course, associated with an expected position of the vehicle in the planning table. The planning table data to be provided by the assistance system application are taken at least partially from the planning table as a function of the current position of the vehicle.