The document discusses machine learning techniques for processing sensor data from vehicles. It describes how machine learning can be used to create virtual sensors from raw data by analyzing features, selecting relevant data, preprocessing to remove noise, and building models. Examples are provided of using support vector machines and neural networks to classify yaw rate from sensor signals. The document also introduces a tool called Distortion that manages machine learning jobs by uploading data, running algorithms, and analyzing results.

1. FROM BLACK BOX
TO BLACK MAGIC
*
:ŸKR
ZK

2. NQI
R
ZK
ͦŃ4N
ŃCI[
Daniele Trainini
Lovera Gloria
1

3. Automotive Sensor Market Worth $35.78 Billion by 2022
2

4. VIRTUAL SENSORS
3

5. WHY MACHINE LEARNING?

6. 20/50 ENGINE
SIGNALS

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

10. Random irrelevant patterns
ML Model : “Grey cars are very fast!”

11. Random irrelevant patterns
ML Model : “ ??? ”

12. wheel rad
9,65[inch] 24,5[cm]
205[mph] 330[km/h]
CONVENTION DOESN’T EXIST

13. BELFAGOR : OUR
PREPROCESSING TOOL

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

17. Data
analysis
Features
selection
Data
preprocessing Wrappers
features selection
SVM

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
✗

20. Params
calibration Neural Networks
Model
Selection
x1
x2
Neuron/Perceptron
w1
w2 Σ | y

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

23. Deep Neural Networks

24. RESULTS EVALUATION
CONFUSION MATRIX
Predicted class
class = 1 class = 0
class = 1 f11 f10
class = 0 f01 f00
True
class
Accuracy = # of correct predictions / # of predictions
= (f11 + f00) / (f11 + f10 + f01 + f00)
Error rate = # of wrong predictions / # of predictions
= (f10 + f01) / (f11 + f10 + f01 + f00)

25. WHERE WE WERE
24

26. DISTORTION
“One Tool to rule them all, !
One Tool to find them,!
One Tool to bring them all !
and in the BlackBox correlate them”
25

27. DISTORTION MAP
Adapter
Data
Uploader
Job
Manager
Worker[s]
Algorithms
API
…
26

28. JOB MANAGER
27

29. JOB MANAGER
28

30. VIEW
OPEN TRIGGER
WHY ?
RELATIONAL PL
29

31. WHY PYTHON?
• it’s awesome
30

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

34. Questions?
it.linkedin.com/in/dani84bs/it
@Dani84bs
it.linkedin.com/pub/gloria-lovera/5b/
152/4a8/
@LoveraGloria