Computer Vision for Advanced Driver Assistance Systems (ADAS) From R&D to Production

Viktor Sdobnikov

EECVC June 2016

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

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

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

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

Augmented navigation

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Output is an extendable metadata which describes all the augmented objects/hints

and supports natural features ON/OFF

Augmented navigation – LCD and HUD

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Collision warning/lane changes tracking

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Proper problem formulation

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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)

Proper and improper strategies - example

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https://drive.google.com/file/d/0B3h0DfKqcKP9M01FTW1ic09tdTg/view

Efficient convolution computation

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w • x = (Δ • w) • (Δ−1 • x)

From R&D to production: Criteria and Acceptance

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Case sensitive: a. Roundabout, lack of markings b. Highway, moderate traffic

From R&D to production: Testing

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HW and SW based approach of data collection, storage and

usage in test suite for testing on different levels of various

ADAS applications

Q&A