automotive-ml

Automotive Ml

Anomaly Detection

Anomaly Detection for Automotive Systems

Detect unusual vehicle behavior across battery systems, sensors, and drivetrain components using unsupervised ML techniques.

Use Cases

1. Battery Anomalies: Cell voltage drift, thermal runaway precursors, SOC inconsistencies
2. Sensor Failures: LiDAR/radar malfunction, camera degradation, IMU drift
3. Drivetrain Issues: Motor vibration anomalies, inverter faults, cooling system failures
4. Charging Anomalies: Abnormal charging curves, connector issues, grid irregularities

Algorithm Selection

Isolation Forest

Best for: High-dimensional sensor data with mixed feature types

Pros:

• Handles non-Gaussian distributions
• Efficient for large datasets
• No assumptions about normal behavior shape
• Low memory footprint

Cons:

• Sensitive to feature scaling
• May struggle with local anomalies

Use cases: Real-time battery monitoring, sensor fault detection

Autoencoder (Deep Learning)

Best for: Complex time-series patterns, image-based anomalies

Pros:

• Learns compressed representation
• Excellent for time-series sequences
• Handles multi-modal data
• Can detect subtle pattern deviations

Cons:

• Requires significant training data
• Computationally expensive
• Black-box interpretation

Use cases: Camera degradation, LiDAR point cloud anomalies, battery degradation patterns

Local Outlier Factor (LOF)

Best for: Local density-based anomalies

Pros:

• Detects local outliers in varying density regions
• No global threshold needed
• Good for spatial data

Cons:

• Computationally intensive for large datasets
• Requires careful k-neighbor selection

Use cases: Geographic anomalies (GPS data), fleet-wide comparison

One-Class SVM

Best for: Small, well-defined normal behavior regions

Pros:

• Kernel trick for non-linear boundaries
• Robust to outliers in training set
• Theoretical foundation

Cons:

• Difficult hyperparameter tuning
• Slow on large datasets
• Memory intensive