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Artificial Intelligence for
Connected and Autonomous Vehicles
The hitch-hikers guide to
12/04/2017 © Astius Technology 2017 1
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
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
www.astius.co.uk
12/04/2017 © Astius Technology 2017 3
• 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
12/04/2017 © Astius Technology 2017 4
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
Evolution of AI technologies
12/04/2017 © Astius Technology 2017 7
Source: Nvidia
GROWING DEMAND
FOR COMPUTATIONAL
RESOURCES
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
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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
Acceleration of AI learning
12/04/2017 © Astius Technology 2017 11
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.
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
Timeline for deployment
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Each level of autonomy requires increased “intelligence” and computational power
Advanced Driver Assistance System (ADAS)
28/03/2017 Connected Cars & Autonomous Vehicles 16
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
Example: Nissan Leaf
28/03/2017 Connected Cars & Autonomous Vehicles 18
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.
Nvidia Xavier chip
12/04/2017 © Astius Technology 2017 20
Photo (left) shows the result of
collaboration between Nvidia and
Bosch to produce an AI automotive
computer based in the Xavier chip.
Key partnerships
12/04/2017 © Astius Technology 2017 21
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!
Example: Audi
28/03/2017 Connected Cars & Autonomous Vehicles 22
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 cockpit Audi 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.
The car of the future
28/03/2017 Connected Cars & Autonomous Vehicles 24
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.
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
The transport revolution
28/03/2017 Connected Cars & Autonomous Vehicles 26
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
Summary of key points
12/04/2017 © Astius Technology 2017 27
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