Camera-Based Lane Cut-In Prediction Without World-Frame Conversion
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Solution Overview
Problem
Conventional methods for predicting a target vehicle's behavior in advanced driving assist systems (ADAS) and autonomous driving (AD) often rely on transforming vision data to a world frame, which can be inaccurate due to calibration errors and incorrect geometry assumptions, leading to suboptimal collision avoidance in scenarios like lane cut-in.
Innovation Solution
The system uses pixel measurements from camera images to directly predict a target vehicle's time to line crossing into an occupied lane without sensor fusion or conversion to a world frame, calculating a normalized target vehicle offset and arrival rate to improve prediction accuracy and reduce lag in path planning and motion control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If vision data is transformed to a world frame using conventional methods, then the system can process target vehicle behavior prediction, but measurement accuracy deteriorates due to calibration errors and incorrect geometry assumptions
Solution Approach 1:
The patent extracts and eliminates the problematic transformation step to a world frame. By working directly with pixel measurements from the camera image frame, the system removes the source of calibration errors and geometry assumptions, thereby improving both measurement precision and reliability simultaneously
Solution Approach 2:
Instead of transforming image data to a world frame (conventional approach), the patent inverts the approach by keeping all calculations in the image frame using pixel measurements. This inversion eliminates the transformation errors while maintaining the ability to predict target vehicle behavior accurately
2Measurement precision
If pixel measurements are used directly without sensor fusion or world frame conversion, then measurement accuracy improves, but device complexity reduces
Solution Approach 1:
The patent extracts only the essential pixel measurements needed for prediction (target vehicle position, lane markings) and eliminates unnecessary sensor fusion and coordinate transformation components. This simplification reduces system complexity while maintaining high prediction accuracy through direct pixel-based calculations
Solution Approach 2:
The patent inverts the conventional complex pipeline by working directly in pixel space without transformation. This approach reduces computational complexity and device requirements while improving accuracy by avoiding error-prone transformation steps
3Productivity
If conventional transformation methods are used, then path planning can be performed, but response time increases due to lag
Solution Approach 1:
The patent extracts and removes the time-consuming world frame transformation step from the processing pipeline. By calculating predictions directly from pixel measurements, the system significantly reduces computation time and eliminates lag, enabling faster response for path planning and collision avoidance
Solution Approach 2:
The patent skips the intermediate transformation step to a world frame and rushes directly from pixel measurements to prediction results. This shortcut eliminates unnecessary computational delays, improving response speed and reducing lag time for critical path planning decisions
Data Source
AI summary
An apparatus includes at least one camera configured to capture a series of image frames for traffic lanes in front of an ego vehicle, where each of the series of image frames is captured at a different one of a plurality of times. A target object detection and tracking controller is configured to process each of the image frames using pixel measurements extracted from the respective image frame to determine, from the pixel measurements, a predicted time to line crossing for a target vehicle detected in the respective image frame at a time corresponding to capture of the respective image frame.