VRU Path Prediction Using Joint Angle Probability Correction
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Solution Overview
Problem
Existing methods fail to accurately predict the movement path of vulnerable road users (VRUs) based on VRU path maps, lacking correction of path probabilities and angle relationships, which hinders precise navigation and safety in wireless communication systems.
Innovation Solution
A method and apparatus that utilize a VRU path map to correct path probabilities and angles, determining specific joints and applying probability correction values based on distance and angle differences, and transmitting messages with updated path information to predict VRU movements accurately.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If VRU path maps are used for movement path prediction, then navigation capability is provided, but prediction accuracy is insufficient due to lack of probability correction
Solution Approach 1:
The patent applies parameter changes by correcting the probability values associated with different movement paths. The system calculates correction values based on the current VRU position relative to joint points and uses these to adjust the original path probabilities from the path map, thereby improving prediction accuracy while maintaining reliability.
Solution Approach 2:
The system implements feedback by continuously updating path probabilities based on the actual VRU position and heading direction. The correction mechanism uses real-time position information to adjust the movement path prediction, creating a feedback loop that improves accuracy over time.
2Measurement precision
If path probability correction is applied based on angle and position relationships, then prediction accuracy improves, but computational complexity increases
Solution Approach 1:
The patent segments the movement path into discrete segments between joint points. By dividing the continuous path into manageable segments with associated probabilities, the system can process and correct each segment independently, reducing overall computational complexity while maintaining accuracy.
Solution Approach 2:
The correction mechanism applies local quality by calculating position and angle relationships only for the specific joint points relevant to the current VRU location. Rather than processing the entire path map uniformly, the system focuses computational resources on the local area around the current position, improving efficiency.
Data Source
AI summary
A device receives a VRU path map including joint information; predicts the movement path of a VRU device; and transmits a first message including information about the movement path of the VRU, wherein: the joint information includes location information about joints, and information about the angle and probability of each of the plurality of VRU paths branched at the joints; the VRU device determines a specific joint from among the joints on the basis of the location of the VRU and a progress direction measured by a sensor, and predicts the movement path of the VRU by correcting the probability of each of the VRU paths on the basis of the difference in angle between the progress angle and the angle of each of the VRU paths branched at the specific joint.