These are the slides from a talk given to the 'Self-driving and Autonomous Vehicles' meetup group, in Brighton on 12/02/2017. It provides an overview of how Artificial Intelligence (AI), Machine Learning and Deep Learning are shaping the future of the automotive industry.

1. Artificial Intelligence for
Connected and Autonomous Vehicles
The hitch-hikers guide to
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2. 12/04/2017 © Astius Technology 2017 2
This talk was given to an audience of enthusiasts at
the ‘Self-driving and Autonomous Vehicles’ meetup
group.
The event was held at Barclays Eagle Labs at Brighton,
on April 12th 2017

3. Bill Harpley MSc
• 30+ years in technology sector
• Founder of Astius Technology
• Organiser of Brighton IoT meetup group
(700+ members)
• Initiator of Brighton node of the global
Things Network
• Organiser of the Self-driving Cars &
Autonomous Vehicles meetup group
https://uk.linkedin.com/in/billharpley
bill.harpley@astius.co.uk
www.astius.co.uk
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4. • A brief history of Artificial Intelligence
• The difference between
– Artificial Intelligence (AI)
– Machine Learning (ML)
– Deep Learning (DL)
• Vehicles are getting smarter
• Key industry players and partners
• The future of motive AI
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5. A brief history of Artificial Intelligence (AI)
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6. Key milestones since 1950
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1950
• Alan Turing published
the Turing Test
• Isaac Asimov published
the Three Laws of Robotics
1956
Term artificial
intelligence created
by John McCarthy
1951
First game-playing
AI programs written at
Manchester University
1958
LISP programming
language invented
1959
MIT AI Lab
founded
1961
First industrial
robot deployed
on an assembly line
1972
PROLOG
programming
language invented
1969
SRI developed
Shakey the Robot
1979
Stanford AI Lab
built the
Stanford Cart
1986
Team at Munich
University build
first robot cars
Mid-1980s
Neural networks
become widely
used
1995
CMU Navlab
Vehicle drives
from Pittsburgh
to San Diego
2009
Google
builds self-
driving car
2004
DARPA Grand
Challenge
How AI has contributed to the development of Autonomous Vehicles

7. Evolution of AI technologies
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Source: Nvidia
GROWING DEMAND
FOR COMPUTATIONAL
RESOURCES

8. AI applications used within CAV context
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Expert
Systems
Natural
Language
Processing
Language
Translation
Object
Recognition
Facial /
Gesture
Recognition
Voice
Recognition
Diagnostics
Character /
Symbol
Recognition
Robotic
control

9. Flavours of Artificial Intelligence (AI)
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Artificial Intelligence
•“Weak AI” – programs
developed to perform
specific tasks
•Used when the problem
domain can be modelled as
a clear set of rules
•Example: expert systems
Machine Learning
• Deploys algorithms which can
learn to make predictions or
decisions, based on input from
a set of training data
•“neural networks” are
commonly used for this task
•Used in situations in which it
is hard to programme a clear
set of rules
•Examples: Computer Vision,
Speech Recognition, Vehicle
Routing
Deep Learning
•You can think of this as an
advanced form of Machine
Learning
•Data is consumed at the input
and processed through a series
of stages
•Each processing layer adds an
additional layer of meaning to
the result
•Example: vehicle onboard
computer can be trained to
interpret camera images using a
360° field of view
GROWING DEMAND FOR
COMPUTATIONAL RESOURCES
Overview of principal AI technologies

11. Acceleration of AI learning
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GPUs
(Graphics Processing Unit)
FPGAs
(Field Programmable Gate Array)
NVIDIA Drive PX2 board (2 x GPUs)
• A GPU consists of hundreds of
processing cores.
• Facilitates massively parallel
processing of a data set.
Spartan-6 FPGA
evaluation kit
• The programme logic is defined by the
customer after the board has been
manufactured.
• Facilitates optimisation for a specific task.

12. GPUs and FPGAs compared
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GPU
• Provides “software acceleration”
• Cheaper than FPGA boards
• Less energy efficient than FPGAs
FPGA
• Provides “hardware acceleration”
• More expensive than GPU boards
• More energy efficient than GPUs
These are the basic factors which differentiate GPU and FPGA technology

13. Why vehicles are getting smarter
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14. SAE –J3016 classification standard
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15. Timeline for deployment
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Each level of autonomy requires increased “intelligence” and computational power

16. Advanced Driver Assistance System (ADAS)
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What is ADAS?
ADAS can be thought of as
a set of technologies
which provide improved
safety to the driver and
other road users:
• LIDAR
• RADAR
• Embedded Vision
• GPS and Digital Mapping
Examples of ADAS applications
Cruise control
Assisted parking
Collision avoidance
Vehicle navigation
Assisted lane changing

17. Example: ADAS vision system
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18. Example: Nissan Leaf
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Nissan Leaf cars avoid collisions by utilising 12 cameras,
five radars and four lasers to enable them to identify
surrounding objects and calculate their position on the
road to within a few centimetres.

19. Players and partners in automotive AI
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20. Nvidia Xavier chip
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Photo (left) shows the result of
collaboration between Nvidia and
Bosch to produce an AI automotive
computer based in the Xavier chip.

21. Key partnerships
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BMW + Intel + Mobileye
Audi + Qualcomm
Audi + Nvidia
BMW + IBM Watson
Just a few of the key partnerships which have emerged between
automakers and hardware / software vendors. Notice how automakers
are hedging their bets!

22. Example: Audi
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Artificial Intelligence (AI) is already used in automotive applications such as
Smart Parking, Driver alertness monitoring, Automated lane changing.
Audi and Nvidia have announced
partnership to bring AI-controlled
car to market by 2020 (SAE Level 4)
Audi virtual cockpitAudi AI test vehicle
Other auto-makers such as BMW, Volvo,
Toyota and Mercedes are exploring use
of AI / ML in connected cars
These types of AI applications employ neural networks. These need to be trained
using large data sets, hence a role for Cloud Computing and Big Data.

23. What the future holds for automotive AI
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24. The car of the future
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https://www.toyota.com/concept-i/ http://www.discover-sedric.com/en/
All major motor manufacturers are developing futuristic ‘concept cars’.
Here are two examples from Toyota and Volkswagen.

25. The taxi of the future
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There seems to be universal consensus that driverless taxis are the way forward.
In fact, they are already being trialled in major cities around the world.
http://nutonomy.com/
nuTonomy ( Singapore ) Uber (Pittsburgh, USA)
http://uber.com

26. The transport revolution
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AI motive technology is now being deployed to build the next generation of personal
transportation and commercial logistics
Driverless buses
Driverless trucks Rolls Royce autonomous ship Commuter drones
Hyperloop

27. Summary of key points
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1. Research into AI goes back more than half a century
2. Flavours are “Weak” AI, Machine Learning and Deep Learning
3. AI is fundamental to progress towards Autonomous vehicles
4. GPUs and FPGAs are used to implement “intelligent” functions
5. Many key partnerships have emerged between different players
6. Motive AI technology now used in other areas of transport

28. Advanced System Technologies
in Urban Spaces™
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