Vehicle Camera Optical Flow with Adaptive Frame Time Difference
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Solution Overview
Problem
Conventional methods for determining optical flow in vehicle cameras use a constant time difference, which can lead to loss of flow data in scenes with high dynamics, limiting the accuracy of self-motion estimation and scene understanding.
Innovation Solution
The method dynamically adjusts the time difference between camera images based on specific input signals, such as automatic emergency braking or pitch rate, to prevent loss of optical flow and optimize data accuracy in varying driving situations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a constant time difference is used for optical flow calculation, then the computational process is simple, but optical flow is lost in highly dynamic scenes
Solution Approach 1:
The patent applies dynamics by transitioning from a static constant time difference to a dynamic time difference selection mechanism. The system now adapts the time difference between consecutive images based on detected driving situations (e.g., emergency braking, high pitch rate) to maintain optical flow availability in highly dynamic scenes while keeping it constant in stable conditions.
Solution Approach 2:
The patent changes the parameter of time difference from a fixed constant to a variable parameter that is adjusted based on input signals from sensors (acceleration, pitch rate, roll rate). This allows the system to optimize optical flow calculation by selecting appropriate time differences for different driving conditions.
2Measurement precision
If a long time difference is used, then self-motion estimation accuracy is improved, but optical flow is lost in highly dynamic situations
Solution Approach 1:
The system dynamically adjusts the time difference parameter based on the current driving situation. In stable driving conditions, a longer time difference is used to improve self-motion estimation accuracy. In highly dynamic situations (detected via acceleration, pitch rate, or roll rate thresholds), the system switches to shorter time differences to prevent optical flow loss, thus maintaining both accuracy and reliability adaptively.
Solution Approach 2:
The time difference parameter is changed from a fixed long value to a variable that is selected from multiple possible values based on real-time sensor data. This allows the system to optimize the balance between measurement precision and reliability by adjusting the parameter according to scene dynamics.
3Reliability
If a short time difference is used, then optical flow is maintained in dynamic scenes, but self-motion estimation accuracy is reduced
Solution Approach 1:
The system uses a dynamic time difference selection strategy that switches between short and long time differences based on driving conditions. Short time differences are used during high-dynamic events (emergency braking, sharp pitching/rolling) to maintain optical flow availability, while long time differences are used during stable driving to maximize self-motion estimation accuracy.
Solution Approach 2:
The time difference parameter is adjusted based on input signals from vehicle sensors. When dynamic thresholds are exceeded (indicating high-dynamic scenes), the parameter is changed to a shorter value to preserve optical flow. When thresholds are not exceeded, the parameter is set to a longer value to improve estimation accuracy.
4Productivity
If the frame rate is dynamically changed, then computational effort is optimized, but the system complexity increases
Solution Approach 1:
The system dynamically changes the frame rate based on the detected driving situation and the selected time difference. This allows the system to optimize computational effort by adjusting the frame rate to match the actual dynamics of the scene, processing images at higher rates during dynamic events and lower rates during stable conditions.
Solution Approach 2:
The frame rate parameter is changed dynamically based on the time difference selection and sensor input signals. This adaptive parameter change allows the system to balance computational efficiency with the need for accurate optical flow calculation in varying driving conditions.
Data Source
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AI summary
The invention relates to a method for determining an optical flow on the basis of an image sequence captured by a camera (104) of a vehicle (100). At least one input signal (108), which represents a vehicle environment detected by using at least one sensor (110) of the vehicle (100) and/or a driving situation of the vehicle (100) and/or a result of a prior determination of the optical flow, and an image signal (106) representing the image sequence are received. A time difference value is determined by using the input signal (108). At least two individual images of the image sequence that are offset to each other by the time difference value are selected by using the image signal (106). Finally, corresponding pixels are detected in the individual images in order to determine the optical flow by using the corresponding pixels.