Autonomous Vehicle Anomaly Detection Using Unsupervised Time-Series Models
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Solution Overview
Problem
Existing anomaly detection systems for autonomous vehicles, particularly delivery drones, struggle to identify complex and unknown failure modes due to their reliance on predefined rules and statistical thresholds, which are inadequate for the diverse and evolving operational scenarios of these vehicles.
Innovation Solution
An unsupervised machine learning model is trained on large volumes of time series data to detect anomalies by learning normal flight dynamics, using a novel algorithm for robust least trimmed squares estimation, allowing it to identify deviations from expected behavior without prior labeling of normal and anomalous data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If predefined rules and statistical thresholds are used for anomaly detection, then the system is simple to implement, but it cannot identify complex and unknown failure modes
Solution Approach 1:
The patent replaces traditional mechanical rule-based anomaly detection systems with a machine learning model that learns patterns from data. The model substitutes predefined statistical thresholds with adaptive neural network-based anomaly scoring, enabling detection of complex and unknown failure modes while maintaining system simplicity through automated learning.
Solution Approach 2:
The patent changes the detection parameters from fixed statistical thresholds to dynamic anomaly scores generated by a trained machine learning model. The model learns normal flight dynamics patterns and computes deviation scores, allowing the system to adapt to diverse operational scenarios without manual parameter adjustment.
2Measurement precision
If machine learning models are trained on large volumes of time series data, then anomaly detection accuracy improves, but computational resources and training time increase
Solution Approach 1:
The patent performs preliminary training of the machine learning model on historical flight data before deployment. By pre-training the model offline on large datasets, the system achieves high detection accuracy while minimizing real-time computational burden during actual flight monitoring, as the heavy lifting is done in advance.
Solution Approach 2:
The patent extracts and stores optimized model weights and anomaly thresholds from the training phase into a separate configuration file. This extraction allows the trained model to be deployed efficiently without requiring the training dataset or training computations during runtime, separating the training burden from operational execution.
3Adaptability or versatility
If unsupervised learning is used to detect unknown failure modes, then the system can identify novel anomalies, but it requires large amounts of unlabeled data
Solution Approach 1:
The patent implements unsupervised learning where the machine learning model learns normal flight dynamics patterns autonomously from unlabeled historical data without requiring manual annotation. The model self-adjusts its parameters to capture typical operational patterns, enabling detection of novel anomalies while reducing the need for expensive labeled datasets.
Data Source
AI summary
In some embodiments, techniques are provided for analyzing time series data to detect anomalies. In some embodiments, the time series data is processed using a machine learning model. In some embodiments, the machine learning model is trained in an unsupervised manner on large amounts of previous time series data, thus allowing highly accurate models to be created from novel data. In some embodiments, training of the machine learning model alternates between a fitting optimization and a trimming optimization to allow large amounts of training data that includes untagged anomalous records to be processed. Because a machine learning model is used, anomalies can be detected within complex systems, including but not limited to autonomous vehicles such as unmanned aerial vehicles. When anomalies are detected, commands can be transmitted to the monitored system (such as an autonomous vehicle) to respond to the anomaly.