Lviv IT Arena is a conference specially designed for programmers, designers, developers, top managers, inverstors, entrepreneurs and startuppers. Annually it takes place at the beginning of October in Lviv at Arena Lviv stadium. In 2016 the conference gathered more than 1800 participants and over 100 speakers from companies like Microsoft, Philips, Twitter, UBER and IBM. More details about the conference at itarena.lviv.ua.
5. www.luxoft.com
Levels of Autonomous Driving
Active ADAS
Autonomous
Driving
Informational ADAS
DISPLAY
Augmented
Reality
HUD
Top View
ACC
Lane keeping
LDW
distance
rear cam
Piloted
Parking …
Source: Intel
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Computer Vision and Augmented Reality Applications
COMPUTER
VISION
Pattern
Recognition
Image
Processing
Physics
Signal
Processing
Arfificial
Intelligence
Mathematic
s
City Driving Pattern
Augmented Navigation
Help in low visibility mode
Signs recognition Infographics
Next Gen of Adaptive Cruise
Active Park Search
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Car Network - V2X
MIT Technology Review
Announced V2X
communication as one of the
10 breakthrough
technologies 2015.
https://www.technologyreview.
com/s/534981/car-to-car-
communication/
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Infrastructure for Autonomous Driving
According to the Nikkei Asian
Review, Tokyo-based Dynamic
Map Planning is developing high
definition and three-dimensional
maps for self-driving cars.
The map will enable the vehicles
to drive smoothly, relaying critical
information such as traffic lights,
sharp turns, slopes in roads and
road construction works.
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Unique Solution
We offer a Unique Solution capable to create Augmented, mixed visual Reality for drivers and passengers based
on Computer Vision, vehicle sensors, map data, telematics, navigation guidance using Data Fusion technique.
CVNAR
SolutionAutomotive Cameras
Sensors/CAN
Navigation System/Map Data
Vehicle displays
Projection on wind shield
Smart Glasses
VR devices
Telematics/V2X
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CVNAR Features
Natural Augmented Reality
1. Basic vehicle data
2. Lanes and road boundaries
3. Road signs and cautions
4. Navigation data and hints
5. Facades highlights
6. Parking Places
7. Narrow street infographics
8. Street names and Complex Junctions
boards
9. POI and OEM specific information
Highlights:
◆ CPU, GPU; OS: Linux, QNX, Android
◆ HW: Intel, nVidia, TI, Renesas
◆ Extrapolation engines for latency
avoidance
◆ Neural networks: CNN, DNN, Deep
Learning
Road scene recognition and objects tracking
◆ Road boundaries
◆ Lane detection
◆ Vehicle recognition and tracking
◆ Distance and time to collision estimation
◆ Pedestrian detection and tracking
◆ Facade recognition and texture extraction
◆ Road signs recognition
◆ Parking slot recognition
Positioning
◆ Precise relative and absolute positioning
◆ Flexible data fusion and smooth Map Matching
◆ Automotive constrained SLAM
Integration and Fusion
1. Sensor data
2. External positioner data (optional)
3. External recognition engine integration
4. Pupil tracking
5. Telematics: V2I and V2V
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MRF-BASED VIDEO STITCHING
Normal case – two images
Highly distorted case – several images
Smooth, seamless and computational efficient stitching of video from calibrated cameras
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Dynamic Information & Learning Navigation
Compact Delta Information
is compiled using
collected and preprocessed
data
Backend access and analyses
different cloud sources
(Learning Navigation) for
compilation
Each car provides collected
data to the backend
Dynamic Navigation
Information is provided to
users via OTA
Cars collect actual navigation
data from roads using built-in
cameras & sensors
Dynamic
Data
External
Navigation data
providers
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Recognition-based Update of Navigation DB
◆ Lanes recognition for DB update:
◆ Number of lanes
◆ Highway exit recognition and localization
◆ Lane marking type recognition
◆ Temporal/new road signs recognition for DB update
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THANK YOU
“Our mission is to GO BEYOND the client expectations by bringing together
technology, talent, innovation and highest quality standards.”