7. Data
gathering
Raw
Data
TEST
Data
storage
Data
analysis
Features
selection
Data
preprocessing
Results
analysis
Experiments
Params
calibration
Model
Selection
WORKFLOW
DB
8. Data
analysis
Features
selection
Data
preprocessing
Fx and Fy as functions of the
longitudinal slip “k” and side slip angle β
k_slip
Fx
[N]
Fy
[N]
9. Clean Noisy
Data
analysis
Features
selection
Data
preprocessing
• Noisy signals
• Quantization errors
• Missing data
15. Data
analysis
Features
selection
Data
preprocessing
Curse of dimensionality
Samples distinguishibility
features nr.
Features ranking
16. Data
analysis
Features
selection
Raw
features
Engineers
features
Scikit-Learn
Chi2, Variance
Threshold,
…
Wrappers
features selection
Scikit-Learn
ensemble
methods,
SVM
Scikit-Learn
metrics
Statistical
features selection
Proprietary
algorithms
Domain
knowledge
Data
preprocessing
18. Data
analysis
Features
selection
Data
preprocessing
SVM example:
Evaluate speed and steer signals as
features subset for
Yaw Rate classification
✓
19. Data
analysis
Features
selection
Data
preprocessing
SVM example:
Evaluate speed and battery current
signals as features subset for
Yaw Rate classification
✗
21. Params
calibration
Model
Selection
Neural Networks example:
Yaw Rate classification
x1
class 0 = yawr -3
class 1 = yawr =-3
h1
h2
x2 h3
y
h4
h5
b1
b2
22. Params
calibration
Model
Selection
Neural Networks example:
Yaw Rate classification
class 0 = yawr -3
class 1 = yawr =-3
x = class 0
x = class 1
x = correct
x = error
Labels Predictions
32. E
M
B
E
D
D
E
D
Resources Optimization
Processor Specific Tuning
Multi-Core Polyedrical Optimization
Microprocessors and FPGA Targets
!
SW in-the-loop
HW in-the-loop
31
33. WHAT’S FOR THE FUTURE…
• Libraries versions management (e.g. ANACONDA virtual env.)
• Data/Results analysis tools
• More Design of Experiment
• Some technical details:
• preemption management
• data caching in worker module
• Suggestions?
32