Anomaly Estimation Neural Networks for Real-Time Behavior Prediction
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Solution Overview
Problem
Autonomous vehicles face challenges in accurately predicting the behavior of nearby objects due to limitations in computational resources and time constraints, leading to potential inaccuracies in behavior prediction models, which can be penalized by conventional error calculation methods that rely on actual future outcomes.
Innovation Solution
Implementing an anomaly estimation model that compares predicted probability distributions from an on-board behavior prediction model with a more resource-intensive off-board model to generate a prediction error, allowing the system to adjust its decisions based on the reliability of the on-board model's predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If an on-board behavior prediction model with limited computational resources is used, then the system can operate in real-time on the autonomous vehicle, but the prediction accuracy is reduced
Solution Approach 1:
An anomaly estimation model is introduced as an intermediary component that assesses the reliability of predictions from the on-board behavior prediction model. This mediator model processes the same sensor inputs and generates an anomaly score indicating whether the on-board model's prediction is likely to be accurate, thereby compensating for the limited accuracy of the resource-constrained on-board model while maintaining real-time operation
Solution Approach 2:
The system creates a simplified copy of the behavior prediction functionality through the anomaly estimation model, which uses the same input data but produces a reliability assessment rather than a full behavior prediction. This copying approach allows the system to evaluate prediction quality without requiring the full computational resources of a more accurate model
2Measurement precision
If a more accurate behavior prediction model is used, then the prediction accuracy is improved, but the computational resources and time required increase
Solution Approach 1:
Instead of deploying a full-accuracy model that consumes excessive resources, the system performs partial action by using a lightweight on-board model for real-time predictions and supplementing it with selective anomaly estimation. The anomaly estimation model processes only the necessary features to assess reliability, consuming far fewer resources than a complete high-accuracy model while still improving effective prediction accuracy
3Measurement precision
If conventional error calculation methods are used to evaluate behavior prediction models, then the model performance can be measured, but the evaluation is penalized by reliance on actual future outcomes which are unavailable in real-time
Solution Approach 1:
The anomaly estimation model performs preliminary assessment of prediction reliability using only current and historical sensor data, before the actual future outcome is known. By predicting whether an anomaly is likely to occur based on patterns in the input data, the system enables performance evaluation and adaptive decision-making without waiting for ground truth verification
Solution Approach 2:
The system implements feedback by using the anomaly estimation results to adjust the autonomous vehicle's behavior. When the anomaly estimation model indicates low confidence in the on-board model's prediction, the system can switch to more conservative behaviors or alternative strategies, creating a closed-loop feedback mechanism that improves overall system performance without requiring immediate ground truth feedback
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for anomaly estimation for behavior predictions using a neural network. One of the methods includes receiving data characterizing a scene that includes an agent in an environment. A behavior prediction input generated from the data is processed using a behavior prediction model. The behavior prediction model is configured to process the behavior prediction input to generate a predicted probability distribution over a plurality of possible behaviors for the agent. An anomaly estimation input generated from the data is processed using an anomaly estimation model. The anomaly estimation model is configured to process the anomaly estimation input to generate a prediction error for the predicted probability distribution. The prediction error indicates an error between the predicted probability distribution generated by the behavior prediction model and another predicted probability distribution generated by another behavior prediction model.