State Estimation for Dynamic Objects Using Detection Reliability
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Solution Overview
Problem
Existing autonomous driving technologies face challenges in accurately predicting the motion states of dynamic objects around vehicles, particularly due to irregular motion, occlusion, and low detection reliability, which can lead to prediction errors, identity switching, and distorted trajectory updates.
Innovation Solution
A method for estimating the state of a target object in an image sequence using previous state estimation data, current target detection data, and detection reliability, which involves predicting current state prediction data, determining current state estimation data by updating the prediction data with target detection data and Kalman gain, and managing measurement noise and prediction noise based on detection reliability and matching states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a Kalman filter with linear motion hypothesis is used for motion state prediction, then the prediction process is simple and computationally efficient, but prediction errors occur for irregular motion patterns
Solution Approach 1:
The patent applies dynamics by transitioning from a static linear motion hypothesis to a dynamic adaptive model that adjusts its behavior based on motion characteristics. The system dynamically switches between constant velocity and constant acceleration models based on detected motion patterns, allowing the prediction process to adapt to both regular and irregular motion while maintaining computational efficiency through model selection rather than complex calculations.
Solution Approach 2:
The patent changes the parameters of the motion model based on detected motion characteristics. By monitoring acceleration and velocity parameters, the system switches between different motion models (constant velocity vs. constant acceleration), effectively changing the mathematical parameters to match the actual motion pattern and improving prediction accuracy without requiring a completely complex system.
2Ease of manufacture
If all detection targets are treated as having the same trajectory, then the processing is uniform and simple, but prediction and update processes become distorted for objects with different motion characteristics
Solution Approach 1:
The patent applies local quality by assigning different processing characteristics to different detection targets based on their motion properties. Instead of uniform processing, the system identifies objects with different motion patterns (e.g., high acceleration vs. low acceleration) and applies appropriate prediction models locally to each group, improving trajectory prediction accuracy while maintaining manageable processing complexity through categorization.
3Ease of manufacture
If detection reliability is not considered in the update process, then the update operation is simple, but incorrect trajectory updates or identity switching occur
Solution Approach 1:
The patent implements feedback by continuously monitoring the consistency between predicted and detected trajectories and using this information to adjust the update process. The system calculates consistency metrics that feed back into the update operation, allowing it to weigh detected information differently based on reliability and prevent incorrect updates or identity switching while maintaining a relatively simple update framework.
Data Source
AI summary
A method of estimating a state includes: predicting current state prediction data of a target object by using previous state estimation data of a previous image frame of an image sequence in which the target object is represented, the previous image frame previous to a current image frame; acquiring current target detection data of the target object for the current image frame of the image sequence; and determining current state estimation data of the target object of the current image frame by updating the current state prediction data by using the current target detection data and by using a detection reliability of the current target detection data.