Vehicle Collision Warning Using Recursive Risk Fitting
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Solution Overview
Problem
Existing camera-based collision warning systems for vehicles require significant computational power and complexity, particularly when using Kalman filters, which can lead to delays and inefficiencies in determining the time to collision, making them unsuitable for embedded systems in motor vehicles.
Innovation Solution
A method that determines the current degree of collision risk using a fitting function based on current and previous measured values, without a Kalman filter, employing regression analysis and weighting factors to adapt to different road situations and prioritize recent measurements, ensuring precise risk estimation with minimal computational effort.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a Kalman filter is used to filter measured values for time to collision determination, then measurement precision is improved, but device complexity and computational power requirements increase significantly
Solution Approach 1:
The patent extracts only the essential filtering functionality needed for time to collision determination, implementing a simplified filter that processes only the necessary measured values (distance, speed, acceleration) without the full complexity of a Kalman filter. This extraction approach maintains measurement precision while significantly reducing algorithmic complexity and computational requirements for embedded systems.
Solution Approach 2:
The patent changes the parameters and structure of the filtering algorithm from a complex Kalman filter to a simpler recursive filter that uses weighted averages of recent measurements. By modifying the filter parameters (weighting factors, measurement windows) and structural complexity, the system achieves adequate measurement precision with substantially reduced computational burden suitable for embedded automotive systems.
2Measurement precision
If a Kalman filter is used to filter measured values, then measurement precision is improved, but processing time increases causing delays
Solution Approach 1:
The patent performs preliminary actions by pre-calculating and storing weighting factors and filter parameters that are needed for real-time processing. By preparing filtering constants and measurement windows in advance, the system reduces real-time computational requirements and processing delays, while still achieving precise time to collision determination through the simplified filter structure.
Solution Approach 2:
The patent substitutes the complex mechanical/computational structure of a Kalman filter with a simpler mathematical model based on weighted averages and recursive calculations. This substitution replaces the intricate matrix operations and state estimation mechanics of Kalman filtering with straightforward arithmetic operations that can be executed rapidly in embedded systems, eliminating processing delays while maintaining adequate precision.
3Measurement precision
If computational power is increased to handle complex filtering algorithms, then measurement precision is improved, but use of energy increases
Solution Approach 1:
The patent employs a disposable, lightweight filtering approach that does not require sustained high computational power. By using a simple recursive filter with pre-calculated parameters and fixed measurement windows, the system achieves adequate measurement precision with minimal energy consumption, avoiding the need for powerful but energy-intensive processing hardware in embedded automotive systems.
Data Source
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AI summary
The invention relates to a method for warning the driver of a motor vehicle of an imminent collision with a target object located in an environmental region of the motor vehicle and approaching the motor vehicle, wherein a temporal sequence of images of the environmental region is provided by means of a camera of the motor vehicle, wherein a measured value (14; TTC1 to TTCx) for a degree of risk (TTC) with respect to the collision is respectively determined from at least a subset of the images based on the respective image by means of an image processing device, and wherein the warning of the driver is effected depending on the measured values (14; TTC1 to TTCx), wherein based on the respectively current measured value (TTC1) as well as based on a plurality of previous measured values (TTC1 to TTCx), a fitting function (15) is determined, which satisfied a predetermined optimization criterion with respect to the measured values (TTC1 to TTCx), and wherein the current degree of risk (TTC) is determined based on the fitting function (15) for warning the driver.