Autonomous Navigation of
Electric Vehicles
BY
Giridhar S. Chavan
Design Thinking
“Design is not what it looks like or feels like.
Design is how it works.”
Steve Jobs
In 10 minutes
DESIGN
THINKING
by Ken Baldauf
Design Thinking
Four Pillars
Design Thinking
Empathy
Design Thinking
Collaboration
Design Thinking
Inclusion
Design Thinking
Repeat/Iterate
Design Thinking
• Empathize with people's needs,
• Collaborate with others across disciplines, skill
sets, and perspectives,
• Include every idea in visible form for evaluation,
and
• Repeat, iterating and testing solutions to perfect
them, always with human needs at the center.
Design Thinking Quiz
Design Thinking or NOT Design Thinking
Design Thinking Process
Problem
Design
Challenge
Space
Solution
Space
Design
Solution
Design Thinking
Problem
Design
Challenge
Space
Solution
Space
Design
Solution
Design Thinking Branding
Design Thinkers Group
Stanford d.school
Ideo
Ideo v2.0
Luma Institute
IBM
FSU Innovation Hub
Empathize | (Re)frame | Ideate | Prototype | Test
Empathize | Define | Ideate | Prototype | Test
Gather | Generate | Make | Share
Inspiration | Ideation | Implementation
Looking | Understanding | Making
Observe | Reflect | Make
Empathize | Ideate | Build
Design Thinking Methods & Tools
Design Thinkers Group
Stanford d.school
Ideo
Ideo v2.0
Luma Institute
IBM
FSU Innovation Hub
Empathize | (Re)frame | Ideate | Prototype | Test
Empathize | Define | Ideate | Prototype | Test
Gather | Generate | Make | Share
Inspiration | Ideation | Implementation
Looking | Understanding | Making
Observe | Reflect | Make
Empathize | Ideate | Build
Design Thinking Steps in Perspective
Design Thinking Steps in Perspective
Thank you!
www.innovation.fsu.edu
DESIGN
THINKING
Problem Statement
How might we improve the grocery shopping
experience in a manner that positively
impacts people and the environment?
The power of design thinking can unlock a new e-mobility ecosystem for environmental gain.
How will understanding
the need for collaborative
design impact your strategy
now?
The IPCC (Intergovernmental
Panel on Climate Change) –
estimates traffic accounts for
24% of carbon emissions
worldwide today. Think about
that. By redesigning the way we
move people and goods from A
to B on a macro scale, we can
set the world on a path to
reducing almost one quarter of
global emissions.
Does building an e-mobility
ecosystem mean more than
consumer vehicles alone?
That means careful consideration of
geographic location, population
density and human traffic to scale
appropriately and create
environments that promote
convenience and enjoyable travel
experiences. Lifestyle
considerations too – bike racks,
easy on and off entrances,
entertainment systems, and smart
charging ports for all users, as well
as demographic design
considerations. Are we
guaranteeing and designing for
improved mobility, safety and
assurance for elderly travelers as
well as young families?
What will moving beyond
design thinking mean for you?
For those companies seeking
investment opportunities, consider
initiating outreach to the wide
diversity of public transportation,
electrification initiatives and
private sector startups working to
effect change. Consider where you
can fast track the game-changing
ideas and technologies that will
begin to super-charge the emobility ecosystem that’s coming.
Summary
There’s opportunity for EV manufacturers, battery producers and
utilities embarking on charging station network construction, to
align and improve charging deliverables and pricing. And at the
macro level, for every transit mode to design more efficient ways
for greener, data-driven vehicles and transit systems to align and
intersect efficiently for 21st Century consumer travel. This is an
enormous, complex and multifaceted challenge and a true
watershed moment to disengage with the models of the past and
reinvent a more efficient, interconnected and greener future.
Autonomous vehicle sensors
The information collected with the
sensors in autonomous vehicles,
including the actual path ahead, traffic
jams, and any obstacles on the road,
can also be shared between cars that
are connected through M2M
technology.
This is called vehicle-to-vehicle
communication, and it can be an
incredibly helpful resource for driving
automation.
Autonomous vehicles would be impossible without sensors:
they allow the vehicle to see and sense everything on the
road, as well as to collect the information needed in order to
drive safely.
Furthermore, this information is processed and analyzed in
order to build a path from point A to point B and to send the
appropriate instructions to the controls of the car, such as
steering, acceleration, and braking.
The majority of today’s automotive
manufacturers most commonly use
the following three types of sensors in
autonomous vehicles: cameras,
radars, and lidars.
Lidar (Light Detection and Ranging) sensors work similar to radar systems, with the only difference being that they use lasers
instead of radio waves.
Apart from measuring the distances to various objects on the road, lidar allows creating 3D images of the detected objects and
mapping the surroundings.
Moreover, lidar can be configured to create a full 360-degree map around the vehicle rather than relying on a narrow field of view.
These two advantages make autonomous vehicle manufacturers such as Google, Uber, and Toyota choose lidar systems.
Moreover, lidar can be configured to create a full 360° map around the vehicle rather than simply relying on a narrow field of view.
These two advantages have led autonomous vehicle manufacturers such as Google, Uber, and Toyota to choose lidar systems for
their vehicles.
What is M2M communication ?
We live in a world
where there are more
internet of things (IoT)
devices than people.
These devices are
interconnected through
wired and wireless
systems, which allows
them to exchange
information with no or
minimal human
assistance. This is
called machine-tomachine (M2M)
communication.
Automotive Radar
Automotive radars are used to detect the speed and range of objects in the vicinity
of the car. An automotive radar consists of a transmitter and a receiver. The
transmitter sends out radio waves that hit an object and bounce back to the
receiver, determining the objects' distance, speed and direction.
Automotive radar sensors can be classified into two categories:
Short-Range Radar (SRR), and
Long-Range Radar (LRR).
Short-range radar (SRR): Short-range radars (SRR) use the 24 GHz frequency
and are used for short range applications like blind-spot detection, parking aid
or obstacle detection and collision avoidance. These radars need a steerable
antenna with a large scanning angle, creating a wide field of view.
Long-range radar (LRR): Long-range radars (LRR) using the 77 GHz band (from
76-81GHz) provide better accuracy and better resolution in a smaller package.
They are used for measuring the distance to, speed of other vehicles and
detecting objects within a wider field of view e.g. for cross traffic alert
systems. Long range applications need directive antennas that provide a higher
resolution within a more limited scanning range. Long-range radar (LRR) systems
provide ranges of 80 m to 200 m or greater
How image processing is necessary in autonomous car?
The images captured from the autonomous car are processed by the proposed system which is used to control
the autonomous vehicle. Canny edge detection was applied to the captured image for detecting the edges, Also,
Hough transform was used to detect and mark the lanes immediately to the left and right of the car.
Developers of self-driving cars use vast amounts of data from image recognition systems, along with machine
learning and neural networks, to build systems that can drive autonomously. The neural networks identify patterns in
the data, which is fed to the machine learning algorithms.
Autonomous vehicles sense their surroundings using special gadgets placed on the car, examples include the
lidar, radar, GPS, cameras etc.
Advanced control systems (artificial intelligence) interpret sensory information to identify appropriate
navigation paths, as well as obstacles and relevant sign.
Autonomous vehicles are capable of updating their maps based on sensory input, allowing the vehicles to keep
track of their position even when conditions change or when they enter uncharted environments.
LIDAR
3D map and allow the car to “see” potential hazards by bouncing a laser
beam off of surfaces surrounding the car in order to accurately determine
the distance and the profile of that object. Mounted on top the car on a
rotating motor
RADAR
Ability to accurately monitor speed of surrounding vehicles in real time. Mounted on the
bumpers, with two sensors in the front bumper, and two in the rear, the radar units allow
the car to avoid impact by sending a signal to the on-board processor. works in
conjunction with other features on the car such as inertial measurement units
SONAR AND HIGH-POWERED
CAMERAS
Sonar technology have narrow field of view
and its relatively short effective range (about 6
meters) but allows the car to effectively crossreference data from other systems in real time
Cameras mounted to the exterior with slight
separation in order to give an overlapping view
of the Car’s surroundings just like the human
eye which provides overlapping images to the
brain before determining things like depth of
field, peripheral movement, and dimensionality
of objects. Each camera has a 50- degree field
of view and is accurate to about 30 meters.
POSITIONING AND SOFTWARE
The Positioning system works alongside the on-board cameras to process realworld information as well as GPS data, and driving speed to accurately
determine the precise position of each vehicle, down to a few centimetres all
while making smart corrections for things like traffic, road construction, and
accidents.
The software processes all of the data in real-time as well as modelling
behavioural dynamics of other drivers, pedestrians, and objects around you.
While some data is hard-coded into the car, such as stopping at red lights, other
responses are learned based on previous driving experiences. Every mile driven
on each car is logged, and this data is processed in an attempt to find solutions to
every applicable situation. The learning algorithm processes the data of not just
the car you’re riding in, but that of others in order to find an appropriate
response to each possible problem