Autonomous Driving Perception With Embedded Uncertainty Estimation
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Solution Overview
Problem
Conventional autonomous driving perception models fail to incorporate uncertainty estimation, leading to potential safety and reliability issues due to ignoring the inherent uncertainty in perception tasks, which can result in unsafe decisions in complex and dynamic environments.
Innovation Solution
Incorporate uncertainty estimation into deep-neural-network-based autonomous driving perception frameworks by augmenting each prediction head with a confidence dimension, generating uncertainty-weighted predictions through interpolation with ground truth, and using a regularization loss to penalize low-confidence predictions, with dynamic adjustment of hyperparameters to manage uncertainty-related loss.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If uncertainty estimation is incorporated into the perception model, then safety and reliability are improved, but device complexity increases
Solution Approach 1:
The perception model is segmented into multiple independent prediction heads, each responsible for specific perception tasks (e.g., object detection, lane detection, traffic light detection). Each prediction head independently generates uncertainty estimates for its predictions, allowing the system to obtain reliability information for different perception tasks without creating a monolithic complex model. This modular approach enables targeted uncertainty estimation where needed while keeping individual model components manageable.
Solution Approach 2:
The patent adds a confidence dimension to the existing prediction outputs by introducing additional neural network layers that generate uncertainty estimates alongside the primary predictions. This dimensional extension allows the model to output both the predicted values (e.g., object locations, velocities) and their associated confidence scores simultaneously, providing reliability information without fundamentally redesigning the core prediction architecture. The confidence dimension operates parallel to the main prediction pathway, minimizing interference with existing model complexity.
2Measurement precision
If uncertainty-weighted predictions are generated using interpolation with ground truth, then measurement precision is improved, but loss computation complexity increases
Solution Approach 1:
The patent dynamically changes the weighting parameter in the loss computation based on the predicted confidence scores. For high-confidence predictions, the model uses standard loss computation with full weight on prediction accuracy. For low-confidence predictions, the confidence score automatically adjusts the weighting toward ground truth values through interpolation. This parameter adaptation allows the loss function to adaptively prioritize accuracy where the model is confident while relying more on ground truth guidance where uncertainty is high, improving overall measurement precision without requiring complex manual tuning.
3Reliability
If regularization loss term is added to penalize low-confidence predictions, then reliability is improved, but training time increases
Solution Approach 1:
The regularization loss term is continuously applied throughout the training process alongside the standard prediction loss, creating a composite loss function that simultaneously optimizes for both accuracy and confidence. Rather than applying regularization as a separate post-processing step or requiring multiple training passes, the confidence penalty is integrated into each training iteration, allowing the model to learn reliable confidence calibration continuously. This continuous integration ensures that reliability improvement happens throughout training without requiring additional time-consuming separate training phases.
Data Source
AI summary
Embodiments of this disclosure can provide a system and method for training a perception model to perform an autonomous driving task. During operation, the system can obtain labeled training data comprising images captured by multiple cameras mounted at different locations on a vehicle, and the perception model can generate, in parallel, a prediction output associated with the task and a confidence score based on the labeled training data. The confidence score can indicate a level of uncertainty associated with the prediction output. The system can generate an uncertainty-weighted prediction based on ground truth indicated by the labeled training data, the prediction output, and the confidence score; compute a loss function based on the uncertainty-weighted prediction; and update the perception model based on the loss function.