from EECVC2016

1. Computer Vision for Advanced Driver Assistance Systems (ADAS)
From R&D to Production
Viktor Sdobnikov
EECVC June 2016

2. Introduction
2
COMPUTER
VISION
Pattern
Recognition
Image Processing
Physics
Signal Processing
Artificial
Intelligence
Mathematics
• 1957 (Rosenblatt, Perceptron) –
probably, Birthday
• Took results and approaches
from statistics, discrete
mathematics and other fields but
at some point started to return
fundamental results: statistical
learning and unsupervised
learning theory, two dimensional
Chomsky grammars
generalization etc.
• Heuristics and invalid practical
approaches, important at the
beginning, brake the evolution
today

3. ADAS use cases
3
City Driving
Pattern
Active Park Search
Advanced ACC
Augmented
Navigation
Info-graphics
Help in low visibility mode
Recognition based
dynamic DB update

4. Automotive limitations and restrictions
4
• Intel Celeron
• 1296MHz - 1024 Mb RAM
• 256 Kb L2 Cache
• Resources are partially utilized by navigation,
voice recognition, RSE data exchange, etc.
• Real-time system has strict requirements on
latency
• System should be “up-to date” during 6-15 years

5. Framework structure
5
Derivative Morphologic
Integral
image
Objects
detection
VP detection segmentation
Road
recognition
Facades
detection
Vehicles
detection
Arrows
Pipeline
Facades
Pipeline
Markings
Pipeline
Augmented
Navigation
Marker Based
Augmentation
BASIC ALGS
CV ALGS
APPLIED ALGS
PIPELINES
SOLUTIONS

6. Augmented navigation
6
Output is an extendable metadata which
describes all the augmented objects/hints
and supports natural features ON/OFF

7. Augmented navigation – LCD and HUD
7

8. Collision warning/lane changes tracking
8

9. Proper problem formulation
9
Examples:
• w(d,d')=0 if d=d', 1 if d!=d‘
• eps if d=unknown
• w(k,k')=0 if |k-k‘|<d, 1 if |k-k‘|>=d
• w(k,k')=|k-k‘|
• No a priory probability
• Lack of training data (heart disease
vs skin color segmentation)

10. Proper and improper strategies - example
10
https://drive.google.com/file/d/0B3h0DfKqcKP9M01FTW1ic09tdTg/view

11. Efficient convolution computation
11
w • x = (Δ • w) • (Δ−1
• x)

12. From R&D to production: Criteria and Acceptance
12
Case sensitive:
a. Roundabout, lack of markings
b. Highway, moderate traffic

13. From R&D to production: Testing
13
HW and SW based approach of
data collection, storage and
usage in test suite for testing
on different levels of various
ADAS applications

14. Q&A