FROM BLACK BOX TO BLACK MAGIC

Daniele Trainini Lovera Gloria

1

Automotive Sensor Market Worth $35.78 Billion by 2022

2

VIRTUAL SENSORS3

WHY MACHINE LEARNING?

20/50 ENGINE SIGNALS

Data gathering

Raw Data

TEST

Data storage

Data analysis

Features selection

Data preprocessing

Model Selection

Results analysis

Experiments

Params calibration

WORKFLOW

DB

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]

Clean Noisy

Data analysis

Features selection

Data preprocessing

• Noisy signals • Quantization errors • Missing data

Random irrelevant patterns

ML Model : “Grey cars are very fast!”

Random irrelevant patterns

ML Model : “ ??? ”

CONVENTION DOESN’T EXIST

wheel rad

24,5[cm]9,65[inch]

330[km/h]205[mph]

BELFAGOR : OUR PREPROCESSING TOOL

Data analysis

Features selection

Data preprocessing

Samples distinguishibility

features nr.

Curse of dimensionality

Features ranking

Raw features

Engineers features

Scikit-Learn Chi2, Variance

Threshold, …

Scikit-Learn ensemble methods,

SVM

Wrappers features selection

Scikit-Learn metrics

Statistical features selection

Proprietary algorithms

Domain knowledge

Data analysis

Features selection

Data preprocessing

Data analysis

Features selection

Data preprocessing Wrappers

features selection SVM

Data analysis

Features selection

Data preprocessing

SVM example: Evaluate speed and steer signals as

features subset for Yaw Rate classification

✓

Data analysis

Features selection

Data preprocessing

SVM example: Evaluate speed and battery current

signals as features subset for Yaw Rate classification

✗

Model Selection

Params calibration Neural Networks

x1

x2∑ | yw2

w1

Neuron/Perceptron

Model Selection

Params calibration

Neural Networks example: Yaw Rate classification

x1

x2 y

h5

h4

h3

h2

h1

b1

b2

class 0 = yawr < -3 class 1 = yawr >=-3

Model Selection

Params calibration

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

Deep Neural Networks

class = 1 class = 0

class = 1 f11 f10

class = 0 f01 f00

CONFUSION MATRIX

Predicted class

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)

RESULTS EVALUATION

WHERE WE WERE

24

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

Adapter

DISTORTION MAP

Data Uploader

Job Manager

Worker[s]

Algorithms API

…

26

JOB MANAGER

27

JOB MANAGER

28

WHY ?

RELATIONAL PL

OPEN TRIGGER

VIEW

29

WHY PYTHON?

• it’s awesome

30

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

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

Questions?

it.linkedin.com/in/dani84bs/it

@Dani84bs

it.linkedin.com/pub/gloria-lovera/5b/152/4a8/

@LoveraGloria