Image Processing Filter Selection for Rapid Vehicle Speed Convergence
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Solution Overview
Problem
Existing image processing systems face significant errors in distance and speed calculations immediately after object detection, particularly for distant or oncoming vehicles, due to unknown initial conditions, leading to delayed filter convergence and inaccurate vehicle control.
Innovation Solution
An image processing device employing a parallel filter process with multiple filters, each with different initial speed values, to quickly converge on an accurate filter result by selecting the filter with the smallest accumulated observation residual value.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single filter process is used for distance and speed measurement, then the device complexity is low, but the measurement precision is poor immediately after object detection
Solution Approach 1:
The filter processing is segmented into multiple parallel filter processes (first filter process and second filter process), each handling different aspects of the measurement. This segmentation allows the system to improve measurement precision by selecting appropriate filter results based on observation residuals, while managing complexity through structured division of filtering tasks.
Solution Approach 2:
The system changes filter parameters by performing multiple filter processes with different initial values and configurations. By calculating observation residuals for each filter process and selecting the one with the smallest residual, the system adapts parameter selection dynamically, improving measurement precision without permanently increasing system complexity.
2Reliability
If the filter process is started immediately with arbitrary initial values, then the ease of operation is high, but the reliability is poor due to large errors in initial measurements
Solution Approach 1:
The system performs preliminary actions by executing multiple filter processes in parallel from the start, each with different initial values. This preliminary parallel processing ensures that reliable measurements are obtained quickly without requiring complex preliminary analysis or manual intervention for initial value selection.
Solution Approach 2:
The system implements feedback by calculating observation residuals for each filter process and using this feedback to select the most reliable filter result. This feedback mechanism automatically improves reliability by selecting the filter with the smallest residual, eliminating the need for manual initial value tuning while ensuring measurement accuracy.
3Measurement precision
If multiple filter processes are performed in parallel, then the measurement precision improves quickly, but the use of energy increases
Solution Approach 1:
Multiple filter processes are performed in parallel as a preliminary action immediately after object detection. This approach achieves quick measurement precision convergence without requiring prolonged single-filter processing, thereby reducing total energy consumption despite the parallel execution overhead.
Solution Approach 2:
The system changes processing parameters by dynamically selecting which filter process results to use based on observation residuals. This parameter selection strategy avoids the need to continuously run all filter processes indefinitely, reducing energy consumption while maintaining high measurement precision through selective result usage.
4Speed
If a filter process is performed with fixed initial values, then the ease of operation is high, but the speed of filter error convergence is slow for distant or oncoming vehicles
Solution Approach 1:
The filter processing is segmented into multiple parallel processes with different initial values, allowing the system to achieve faster error convergence by selecting the appropriate segmented filter result based on observation residuals, rather than relying on a single slow-converging filter.
Solution Approach 2:
The system changes filter parameters by using multiple initial values in parallel filter processes and dynamically selecting the optimal one based on observation residuals. This parameter diversification accelerates error convergence speed without requiring complex adaptive parameter adjustment mechanisms.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Reduces speed errors immediately after object detection, enabling precise vehicle control and enhancing the performance of advanced driver-assistance systems like ACC and AEB.
Implementation Method 1
a filter processing unit that performs a filter process on the distance calculated by the distance calculation unit and the speed calculated by the speed calculation unit. The filter processing unit performs a plurality of filter processes for a predetermined period of time and then selects one of the plurality of filter processes.
Data Source
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AI summary
An image processing device includes: a distance calculation unit that calculates a distance to a detection target searched from an acquired image; a speed calculation unit that calculates a speed of the detection target; and a filter processing unit that performs a filter process on the calculated distance and speed. Here, the filter processing unit performs a plurality of filter processes for a predetermined period of time and then selects one of the plurality of filter processes.