How rahulsathya1911/suspension_analyzer- works
Adaptive Suspension AI combines Rust suspension simulation and sensor-fusion analysis with a Python comfort-modeling workflow. A typical fusion request enters the backend, which dispatches scenario execution; the engine simulates the plant, synthesizes and synchronizes sensor signals, estimates state, and produces metrics and exported results. The repository also includes simulation APIs and machine-learning outputs. README documents additional core capabilities, but sampled code does not expose their full call paths; several relationships below are therefore limited to explicit documentation or marked unverified.
Main entry points: lib.rs Scenario engine · train.py Training workflow · main.rs Backend API · fusion.rs Fusion routes
Components 23
API and simulation
- Frontend
frontend/src/main.rs - Backend API
backend/src/main.rs - Fusion routes
backend/src/fusion.rs - Core physics
core/src/lib.rs
Fusion engine
- Scenario engine
fusion/src/lib.rs - Scenario config
fusion/src/config.rs - Quarter-car plant
fusion/src/plant.rs - Sensor simulation
fusion/src/sensors.rs - Signal sync
fusion/src/sync.rs - Kalman filter
fusion/src/kf.rs - Comfort analysis
fusion/src/comfort.rs - Scenario metrics
fusion/src/metrics.rs - Result export
fusion/src/export.rs
Comfort modeling
- Feature selection
ml/ml/features.py - Comfort model
ml/ml/model.py - OOD envelope
ml/ml/envelope.py - Damping ranking
ml/ml/semiactive.py - Training workflow
ml/ml/train.py - Analysis plots
ml/ml/plots.py
Results and data
- Analysis outputs
ml/ml/train.py - PostgreSQL
- Fusion datasets
backend/src/fusion.rs
Other components
- Engineer
Connections 28
- Engineer → Frontend: configures
- Frontend → Backend API: submits JSON
README.md - Backend API → Core physics: delegates simulation
README.md - Backend API → PostgreSQL: reads and writes
backend/src/main.rs - Backend API → Fusion routes: routes fusion requests
backend/src/main.rs - Fusion routes → Scenario engine: runs scenario
backend/src/fusion.rs - Scenario engine → Scenario config: uses configuration
fusion/src/lib.rs - Scenario engine → Quarter-car plant: simulates plant
fusion/src/lib.rs - Scenario engine → Sensor simulation: simulates sensors
fusion/src/lib.rs - Scenario engine → Signal sync: synchronizes signals
fusion/src/lib.rs - Scenario engine → Kalman filter: estimates states
fusion/src/lib.rs - Scenario engine → Comfort analysis: computes comfort
fusion/src/lib.rs - Scenario engine → Scenario metrics: computes metrics
fusion/src/lib.rs - Scenario engine → Result export: exports results
fusion/src/lib.rs - Sensor simulation → Quarter-car plant: reads plant output
fusion/src/sensors.rs - Signal sync → Sensor simulation: aligns sensor output
fusion/src/sync.rs - Kalman filter → Signal sync: filters synchronized frames
fusion/src/kf.rs - Scenario metrics → Kalman filter: evaluates estimates
fusion/src/metrics.rs - Fusion routes → Fusion datasets: reads result files
backend/src/fusion.rs - Engineer → Backend API: requests analysis
README.md - Training workflow → Feature selection: extracts features
ml/ml/train.py - Training workflow → Comfort model: trains and predicts
ml/ml/train.py - Training workflow → OOD envelope: evaluates envelope
ml/ml/train.py - Training workflow → Damping ranking: ranks damping
ml/ml/train.py - Training workflow → Analysis plots: generates plots
ml/ml/train.py - Comfort model → Feature selection: selects inputs
ml/ml/model.py - OOD envelope → Feature selection: encodes inputs
ml/ml/envelope.py - Training workflow → Analysis outputs: writes outputs
ml/ml/train.py