Vehicle Traversal Polygon Prediction via Dynamic State Segmentation
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Solution Overview
Problem
Current systems lack an efficient method to predict the area traversed by a moving vehicle over a given distance, particularly for real-time collision avoidance and path planning in autonomous vehicles, as they fail to accurately account for dynamic vehicle states and configurations.
Innovation Solution
A computer-implemented method that receives dynamic parameters such as speed, yaw rate, and initial position to generate sequences of vehicle configuration states, which are then used to create one-sided polylines for the vehicle's sides, merged to form a closed polygon representing the predicted area traversed by the vehicle.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sequences of vehicle configuration states are generated to predict traversal area, then prediction accuracy is improved, but computational complexity increases
Solution Approach 1:
The vehicle configuration sequence is segmented into left-side and right-side polylines, which are processed independently and then merged. This segmentation reduces the complexity of handling the entire configuration sequence as a single complex object, while maintaining prediction accuracy through detailed side-by-side comparison of corresponding polylines.
Solution Approach 2:
The left-side polyline sequence and right-side polyline sequence are merged into single representative polylines by comparing corresponding polylines and determining outer boundaries. This merging consolidates multiple configuration states into compact representations that preserve the essential traversal area information without requiring full sequence storage and processing.
2Reliability
If dynamic vehicle parameters are incorporated into prediction, then prediction reliability is improved, but processing time increases
Solution Approach 1:
The method performs preliminary segmentation of the vehicle configuration sequence into left and right side polylines before merging operations. This preliminary organization of data structures enables more efficient processing during the merging phase, reducing overall computation time while incorporating all necessary dynamic parameters for reliable prediction.
Solution Approach 2:
The patent dynamically adjusts the prediction model by incorporating real-time vehicle parameters (speed, yaw rate, acceleration) and adaptively generating configuration sequences based on current vehicle state. This dynamic approach ensures prediction reliability reflects actual vehicle behavior while optimizing processing efficiency through state-dependent sequence generation.
Data Source
AI summary
Computer-implemented methods and systems are provided for calculating a polygon that estimates the area to be traversed by a moving ground vehicle by merging polygons representing static poses of the vehicle at different times.