Vehicle Object Identification With Uncertainty-Aware Continuous Learning
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Solution Overview
Problem
Autonomous driving systems face challenges in reliably identifying and adapting to data shifts and out-of-distribution scenarios, leading to uncertainty in predictions and potential safety risks due to the lack of robust uncertainty estimation and continuous learning frameworks.
Innovation Solution
Implementing a probabilistic deep neural network with a continuous learning framework that uses uncertainty metrics to annotate and retrain data, allowing for efficient identification of uncertain predictions and updating the model to maintain accuracy and prevent catastrophic forgetting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a traditional deep neural network is used for object identification, then the model can make predictions quickly, but the model becomes overconfident and cannot reliably estimate uncertainty in predictions
Solution Approach 1:
The patent transforms the deterministic parameters of traditional DNNs into probabilistic parameters by modeling weights and biases as probability distributions rather than fixed values. This allows the network to output uncertainty estimates alongside predictions, directly addressing the reliability issue while maintaining the same network architecture complexity
Solution Approach 2:
The patent adds a probabilistic dimension to the traditional deterministic DNN output. Instead of only predicting object classes and positions, the model now operates in an extended space that includes uncertainty metrics and confidence intervals, providing richer information without fundamentally changing the network structure
2Adaptability or versatility
If the probabilistic model is continuously retrained with new data, then the model adapts to data shifts and improves accuracy, but the model may forget previously learned information (catastrophic forgetting)
Solution Approach 1:
The patent implements a feedback mechanism where the model's uncertainty predictions are used to selectively trigger retraining. When high uncertainty is detected for certain input types, the system retrieves relevant training examples and retrainsthe model, creating a closed-loop system that adapts only when necessary and preserves previously learned information
Solution Approach 2:
The patent pre-computes and stores uncertainty metrics for different input distributions during the training phase. This preliminary action allows the model to quickly assess whether new data represents a data shift without immediate retraining, preventing catastrophic forgetting by avoiding unnecessary updates
3Measurement precision
If manual annotation is used for all uncertain predictions, then the data quality improves, but the process becomes time-consuming and expensive
Solution Approach 1:
The patent applies partial annotation by using automated methods for the majority of predictions and reserving manual annotation only for cases where the model exhibits high uncertainty. This selective approach achieves sufficient data quality while dramatically reducing the time and cost compared to annotating all predictions manually
Solution Approach 2:
The model performs self-annotation by generating its own uncertainty metrics and identifying which predictions require human review. This self-service capability filters out confidently predicted cases that don't need annotation, allowing the system to focus human resources only on ambiguous cases
Data Source
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AI summary
Vehicle navigation control systems in autonomous driving rely on accurate predictions of objects within the vicinity of the vehicle to appropriately control the vehicle safely through its surrounding environment. Accordingly this disclosure provides methods and devices which implement mechanisms for obtaining contextual variables of the vehicle's environment for use in determining the accuracy of predictions of objects within the vehicle's environment.