Learned State Covariances for Autonomous Vehicle Sensor Fusion
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Solution Overview
Problem
Existing object detection and tracking systems rely heavily on human estimation and trial-and-error methods for determining observation covariances, which limits flexibility and accuracy, especially in autonomous vehicle applications where precise sensor data fusion is crucial.
Innovation Solution
A learned covariance model using machine learning techniques to generate observation covariance matrices for Kalman filters, improving the accuracy of object detection and tracking by providing specific covariance values tailored to each observation, reducing reliance on human experience and enhancing data fusion from diverse sensor modalities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human estimation and trial-and-error methods are used to determine observation covariances, then the process is simple to implement, but the accuracy and precision of object detection and tracking are limited
Solution Approach 1:
The patent replaces the manual trial-and-error method (mechanical process) with a machine learning model that automatically learns optimal observation covariances from sensor data. The neural network processes sensor inputs and outputs covariance values, eliminating the need for engineer iteration and significantly improving accuracy while maintaining ease of use through automated learning.
Solution Approach 2:
The patent changes the parameter determination approach from fixed engineer-selected values to dynamic learned values. The observation covariance parameters are no longer static but are continuously adjusted based on sensor data characteristics, allowing the system to adapt to different sensing conditions and improve measurement precision across varied scenarios.
2Adaptability or versatility
If fixed observation covariance values are used in Kalman filters, then the system is easy to operate, but the adaptability to different sensing conditions is poor
Solution Approach 1:
The patent transforms the static observation covariance values into dynamic, adaptive parameters. The machine learning model continuously processes sensor data and generates covariance values that adapt to current sensing conditions, such as varying sensor noise characteristics or environmental factors, enabling the system to respond dynamically to different operational scenarios.
Solution Approach 2:
The system performs self-configuration through the machine learning model, which automatically determines appropriate observation covariances without requiring manual intervention. The model learns from sensor data and autonomously adjusts parameters, eliminating the need for engineers to manually tune covariance values for different conditions while maintaining ease of operation.
3Productivity
If trial-and-error methods are used to estimate covariances, then the development process is simple, but the time required for system development and tuning is excessive
Solution Approach 1:
The patent performs preliminary learning during the training phase, where the machine learning model is pre-trained on sensor data to learn optimal covariance determination. This preliminary action eliminates the need for time-consuming trial-and-error tuning during deployment, as the model has already acquired the knowledge needed to quickly adapt to different conditions without requiring extensive manual adjustment.
Solution Approach 2:
The patent replaces the iterative manual tuning process with an automated machine learning approach. The neural network learns covariance patterns from training data and automatically applies this knowledge during operation, dramatically reducing the time required for system development and eliminating the repetitive trial-and-error cycles that previously consumed significant engineering time.
Data Source
AI summary
Techniques are disclosed for a covariance model that may generate observation covariances based on observation data of object detections. Techniques may include determining observation data for an object detection of an object represented in sensor data, determining that track data of a track is associated with the object, and inputting the observation data associated with the object detection into a machine-learned model configured to output a covariance (a covariance model). The covariance model may output one or more observation covariance values for the observation data. In some examples, the techniques may include determining updated track data based on the track data, the one or more observation covariance values, and the observation data.