Ai in automobile

FLOW OF PRESENTATION
• USED CASES – AUTOMOBILE INDUSTRY
• FUNCTIONS & AI IMPLEMENTATION
– MARKETING
– OPERATIONS
– R&D
– HR
– FINANCE
– IT

OUTSELL
• Outsell offers the AI-driven marketing automation platform that empowers automotive dealerships
and OEMs to create lasting customer relationships driving incremental sales and profits.
• In the present competitive market the automotive industries should be active on a wider range of
platforms, devices, and channels than ever before.
• From social media and email campaigns to direct mailers and out-of-home promotions, the ways we
reach consumers has grown at a rapid pace – and is propelling marketing opportunities forward.
• Within this ever-evolving landscape, effectively targeting the in-market car shopper across a
complete range of channels often makes the difference between a high conversion rate and a loss of
business.
• If you can tailor your message to reach your audience exactly where they are in their individual
lifecycle, your business will see exponential benefits.
Outsell is one such company that provides harnesses massive amounts of data creating accurate and
powerful consumer profiles that engage your customers and prospects exactly where they are in their
individual lifecycles.

IT PROVIDES AUTOMOTIVE INDUSTRY WITH NEW
OPPORTUNITIES FOR:
How can you use AI to enhance marketing campaigns.
How will AI affect the auto-dealers marketing strategy.
What criteria should you use when selecting AI solutions.

AUBURN VW USES OUTSELL FOR
PERSONALIZED DIGITAL MARKETING
Outsell has offered personalized automated follow-up that help strengthen customer-relationship, reports,
identify in market buyers for salesperson.
Auburn VW drives more revenue to their business by transforming how they engage customers and prospects
throughout customer lifecycle.
Using outsell’s technology dealers are tipped off when customers are most ready to engage, buy or service.
Outsell makes dealers lives easier by keeping them in front of customers on a consistent, individualized basis,
and automating follow up.
Their multi-channel customer engagement platform manages millions of interactions every month for dealers
representing all major automotive brands

BRUCE TITUS AUTOMOTIVE GROUP USES ARTIFICIAL
INTELLIGENCE SOLUTIONS
- EMBRACING THE FUTURE TO WIN
TODAY’S CUSTOMERS
• In a place like Seattle/ Tacoma, which is metro area that is highly competitive
across all the brands, this is where Bruce Titus wanted to stand out of crowd in
both new cars and used car sales. In order to raise above the competition they
need to increase strength of its platform in three ways:
– Relevance-knowing what the customers present car status is and helpig the
customer in better way that a dealership can build ever lasting relationship.
– Consistency of message-“They wanted to maintain consistency in marketing, both
from a dealership standpoint — and want every message that they send out to look
like a ‘Bruce Titus’ message
– Customer retention-streamline the customer lifecycle for the dealerships.

THE BENEFITS THAT COMPANY HAS DERIVED
Increase in the sales and
profitability-boosting incremental
sales by at least 5% every month
Reduced the digital marketing cost.
The new technology was much
more easier to use than before.
High accurate report generation
regarding the market and in –
market buyers.
There was 75% reduced time spent
on email marketing.
Successful to automate email
newsletters and other ongoing
campaigns – “set it and forget it”.

DRIVE.AI
• Founded in 2015 by graduate students
out of Stanford University’s Artificial
Intelligence labs.
• Uses deep learning to create self driving
systems that are adaptive and scalable
• Work with public and private partners to
solve transportation challenges quickly
and safely with geofenced level 4 self
driving solutions
• Launched self-driving on-demand service
in Fresco, Texas that covers a 2 mile route
and can be hailed using a smartphone app
• Cars capable of interacting with
passengers

DEUTSCHE BAHN
• One of the most advanced rail networks in the world
• Adapted KONUX solutions to replace manual
measurements with a position measurement system
based on custom-made MEMS (micro-
electromechanical) sensor clusters
• This enables autonomous and continuous monitoring
with wireless data transmission
• The data is pre-processed in the sensors, and machine
learning algorithms in the cloud detect critical wear.
• The benefits include a cost reduction of 25%, achieved
by minimizing downtime and maximizing performance

USED CASES
• NVIDIA PILOTNET – Deep Learning System for self driving car controller
Source: Explaining How End-to-End Deep Learning Steers a Self-Driving
Car, By Mariusz Bojarski, Larry Jackel, Ben Firner and Urs Muller, May 23,
2017; https://devblogs.nvidia.com/deep-learning-self-driving-cars/
• NAUTO’s Intelligent Driver Safety System
Source: Inside Nauto with Annie Cheng, By Jon So, Jul 09, 2018;
https://blog.nauto.com/inside-nauto-with-annie-cheng-vp-of-engineering

NVIDIA
PILOTNET
• As part of our autonomous driving research, NVIDIA
has created a deep-learning based system, known
as PilotNet, which learns to emulate the behavior of
human drivers and can be deployed as a self-
driving car controller
• PilotNet is trained using road images paired with
the steering angles generated by a human driving a
data-collection car. It derives the necessary domain
knowledge from data, eliminating the need for
human engineers to anticipate what is important
and to forsee all the necessary rules

TRAINING
THE PILOTNET
SELF DRIVING
CAR SYSTEM
• It is based on convolutional neural network (CNN)
is trained to output steering commands given input
images of the road ahead.
• The training data consists of images from a front-
facing camera in a data collection car coupled with
the time-synchronized steering angle recorded
from a human driver. The motivation for PilotNet
was to eliminate the need for hand-coding rules
and instead create a system that learns by
observing. Initial results are good, although major
improvements are still required before such a
system can drive without the need for human
intervention.

EXPLAINING PILOTNET RESULTS
Fig.1
• NVIDIA developed a simple method for highlighting those
parts of an image that are most salient in determining
steering angles. They call these salient image sections the
salient objects.
• Fig. 1 the bases of cars are highlighted as well as lane-
indicator lines (dashed and solid), while a nearly horizontal
line from a crosswalk is ignored. Also there are no lanes
painted on the road, but the parked cars which indicate the
edge of the drivable part of the road are highlighted
• Figure 2 shows a view inside our test car (left). At the top of
the image we see the actual view through the windshield. A
PilotNet monitor is at the bottom center displaying
diagnostics. It further shows a blowup of the PilotNet
monitor (right). The top image is captured by the front-
facing camera. The green rectangle outlines the section of
the camera image that is fed to the neural network. The
bottom image displays the salient regions. Note that
PilotNet identifies the partially occluded construction
vehicle on the right side of the road as a salient object. To
the best of our knowledge, such a vehicle, particularly in the
pose we see here, was never part of the PilotNet training
data.
Fig.2

ANALYSIS
• Input images are taken into two classes.
• Class 1 is meant to include all the regions that have a significant effect on the steering angle output by
PilotNet. These regions include all the pixels that correspond to locations where the visualization mask is
above a threshold. These regions are then dilated by 30 pixels to counteract the increasing span of the
higher-level feature map layers with respect to the input image. The exact amount of dilation was
determined empirically.
• The second class includes all pixels in the original image minus the pixels in Class 1.
• If the objects found by our method indeed dominate control of the output steering angle, we would expect
the following: if we create an image in which we uniformly translate only the pixels in Class 1 while
maintaining the position of the pixels in Class 2 and use this new image as input to PilotNet, we would
expect a significant change in the steering angle output. However, if we instead translate the pixels in Class
2 while keeping those in Class 1 fixed and feed this image into PilotNet, then we would expect minimal
change in PilotNet’s output

N A U T O ’ S I N T E L L I G E N T D R I V E R S A F E T Y
S Y S T E M

NAUTO’S INTELLIGENT
DRIVER SAFETY SYSTEM
• Detection of distraction and assess situational risk in
real time
• The approach to AI is unique in that they are applying
AI at two points: at the edge and in the cloud. The
combination gives us an intelligent, closed-loop driver
safety platform.
• Challenge is different from many IoT environments
that are connected over stable networking
environment. For stationary devices, which operate in
predictable networking environments, AI can be run in
the cloud.
• However, the luxury is not available since the vehicles
are on the move, so devices need to operate in
variable networking conditions. This creates an
additional challenge, as the devices need to function
regardless of the network connection, which means to
intelligently choose what runs on the device versus
the cloud.

PRINCIPLE
• STOP COLLISION AT SOURCE
• When we detect distraction or other high-risk events, we need to be able to alert drivers in
real-time to help coach and stop risky behaviours, and simultaneously, upload these high-
risk events to the cloud to ensure that fleet and safety managers get the right data at the right
time — even when we aren’t guaranteed a well-connected environment. We’re able to do
this by leveraging AI to assess and understand the driver in the cabin and road ahead. For
example, inside the vehicle, AI is integrated into proprietary algorithms on the device that
we use to assess driver behavior. One example is our distraction algorithm, which processes
incoming images in real-time to detect whether or not driver distraction occurred.
• Outside the vehicle camera, we use machine learning and deep learning technologies to not
only detect objects and understand overall context. By using AI on the edge, we’re able to
operate reliably in any environment, regardless of networking connection conditions.

ATS SYSTEM
R e c r u i t i n g a n d
h i r i n g t o o l
W H Y AT S ?
E a s e o f s o r t i n g a n d
c o l l e c t i n g
O r g a n i z e d a s w e l l
a s E E O C c o m p l i a n t
S a v e t i m e b y
a u t o m a t i c a l l y
s u r f a c i n g a n d
h i g h l i g h t i n g t o p
c a n d i d a t e s

H O W I T S W O R K S ?
• collect and store resumes in a database
• Viewing Applications
• Automatic Rankings
• Keyword Searches

DRAWBACK
• S o m e AT S p a r s e t h e d o c u m e n t
i n t o a d i g i t a l p r o f i l e t o m a k e
t h i n g s u n i f o r m a n d s e a r c h a b l e ,
c a u s i n g y o u r r e s u m e i n f o r m a t i o n t o
g e t d i s t o r t e d o r l o s t .
• A c c t o a s u r v e y, 6 2 p e r c e n t o f
c o m p a n i e s u s i n g a p p l i c a n t t r a c k i n g
s y s t e m s a d m i t “s o m e q u a l i f i e d
c a n d i d a t e s a r e l i k e l y b e i n g
a u t o m a t i c a l l y f i l t e r e d o u t o f t h e
v e t t i n g p r o c e s s b y m i s t a k e ,”

HIREVUE:
SONIC
AUTOMOTIVE
• S o n i c a u t o m o t i v e i n f o r t u n e 5 0 0 b r a n d i s
o n e o f t h e l a r g e s t a u t o m o t i v e r e t a i l e r s i n t h e
c o u n t r y w i t h m o r e t h a n 1 0 0 l o c a l l y b r a n d e d
d e a l e r s h i p s n a t i o n w i d e .
• “ O n e o f t h e b i g g e s t h u r d l e s t o o v e r c o m e
w a s t h e h i r i n g t e a m s ’ t i m e . w e k n e w w e h a d
t o f i n d a b e t t e r w a y t h a t w a s m o r e e f f i c i e n t ,
e m p o w e r u s t o f i n d b e t t e r t a l e n t , a n d
p r o v i d e d a n i m p r o v e d c a n d i d a t e e x p e r i e n c e .”
J o h n Pe r e z
( S e n i o r d i r e c t o r o f t a l e n t a c q u i s i t i o n )

•S o n i c a u t o m o t i v e r o l l e d o u t H i r e v u e
a s a s o l u t i o n t o o f f e r s c a l e , e f f i c i e n c y
a n d c o n s i s t e n c y t o t h e i r h i r i n g
p r o c e s s e s .
•A p p l i c a n t s a r e i n v i t e d t o t a k e a v i d e o
i n t e r v i e w a n d a s s e s s m e n t a n d t h o s e
h i g h e s t s c o r i n g a r e s e n t t o t h e h i r i n g
t e a m s t o r e v i e w .
• S o n i c a u t o m o t i v e a l s o s a w t h e i r
r e t e n t i o n i m p r o v e o v e r 2 0 % w h e n
h i r i n g t e a m s u t i l i z e d t h e n e w h i r i n g
s o l u t i o n s .

HOW IT
WORKS?
 First step, is to get a machine
transcription of the Interview audio
analyze every word, voice track,
expressions on the video. so it
combines print, audio and video
analysis.
 analytical dashboard uses 10,000
attributes to compare every
response.
 Then it offers dials on the candidate’s
engagement, motivation ,empathy
and other categories. And the
offers a percentage for each
candidate.
 percentage is not telling the
company whom to hire but only
suggesting which ones to pursue
first.

ADVANTAGES
• C A N D I D A T E S R E C O R D A N S W E R S O N T H E I R
O W N S C H E D U L E S ,
• S T O R I N G T H E I N T E R V I E W S I N T H E C L O U D ,
• M A K I N G T H E M A V A I L A B L E T O R E C R U I T E R S
A N D H I R I N G M A N A G E R S A T T H E I R
C O N V E N I E N C E 2 4 / 7 .
• M A N A G I N G T H E M A S S I V E S C H E D U L I N G
P R O B L E M
• S A V E S T R A N S P O R T A T I O N C O S T
• B E F O R E H I R E V U E S H I P P E D C A N D I D A T E S A
W E B C A M . N O W , T H E O T H E R 8 5 % O F
C A N D I D A T E S R E C O R D T H E V I D E O I N T E R V I E W S
O N T H E I R O W N D E V I C E S — 4 0 % O N T H E I R
S M A R T P H O N E S ( A F E W O N T A B L E T S ] , A N D 6 0 %
O N T H E I R L A P T O P S

DISADVANTAGES
• C o n n e c t i v i t y p r o b l e m s
• s o m e c a n d i d a t e s a r e n o t c o m f o r t a b l e
b e i n g " o n - c a m e r a "
• B a d l i g h t i n g c a n p r e s e n t a n
u n f l a t t e r i n g v i e w.
• T h i s m e t h o d d o e s n ' t w o r k f o r t o p
l e v e l m a n a g e m e n t p o s i t i o n s .

ARTIFICIAL INTELLIGENCE (USED CASES)
–
FINANCE FUNCTION
According to Experian Automotive, More than 80 percent of new
vehicles sold across the country are either loaned or leased. Of
these transacting customers, more than 70 percent found it
challenging to gather financing information

• founded in 2009
• Applying big data and machine
learning to credit underwriting.
• Using ZAML platform which can turn
data from any source including messy
or erroneous data into highly predictive
credit decision information”.
• Using google style machine learning
algorithms and sophisticated
mathematical models

TECHNOLOGY
Since 2009, Zest Finance has been developing and refining its
underwriting platform as it provided credit scores for hundreds of
millions of prospective borrowers worldwide. Now, with ZAML, they are
offering end-to-end technology platform and underwriting expertise to
financial firms around the world.
The company is already working with one of the “Big Three” captives to
implement this system for its auto originations.
Data
Assimilation
Rapidly discover,
acquire, and onboard
data sources at a
massive scale
Modeling
Tools
Train, ensemble, and
productionalize machine
learning models in one
streamlined workflow.
Modeling
Explainability
Unpack the “black box” of
machine learning models to
clearly communicate
economic value and support
compliance.

GINIMACHINE
GiniMachine is an AI-based credit scoring solution that blends
advanced machine learning techniques with lender’s historical
data.
Key benefits of the solution are:
1. Fast, fully autonomous and automated model building process.
2. Ease of use
3. Built-in evaluation and validation tools.
4. Ability to use unstructured, big data., imperfect data and missing
data.
5. High predictive power. Typically, up to 15 points of the Gini Index
compared to traditional models based on logistic regression

ARTIFICIAL INTELLIGENCE IN ACTION
Combine advanced machine learning techniques with your loan
portfolio history to uncover the full potential of your data. Build unique
scoring models in a matter of minutes — not weeks, and get an
accurate way to assess individual default risk.
Gini Machine is a full-scale credit scoring platform that utilizes
advanced machine learning algorithms and your historical data. The
system automatically builds, validates and deploys high-performing
risk models.

Model Building
Gini Machine needs at least 1000 records of previously issued loans with a
status: good (repaid) or bad (overdue). The model builder does not require
any preliminary analysis or data preparation.
Validation
Every time the model is built, Gini Machine automatically provides you
with a detailed validation report. Track the model's discriminatory power,
all valuable insights and relevant statistics, like the calculated Gini Index,
important features, etc.
Deployment
The models can be instantly integrated into your credit scoring process.
Once the model is created and validated, it’s ready for scoring calculations
& real-time predictions.

USE CASES OF
AI IN IT
FUNCTIONAL
AREA OF
AUTOMOBILE
INDUSTRIES
Use Case 1: How AI can be used in Telematics?
• Telematics is the branch of IT which associates with
long-distance transmission of computerized
information.
Case A:
• Remoto, AI-First turnkey connected car platform
for OEMs and large dealership groups uses AI and
ML to provide recommendations to customers
based on their incoming telematics data.
• They collects terabytes of data from connected cars
and have created a data lake on the basis of
Microsoft Azure to store these data.

Working:
 Remoto AI is capable of processing 1.5 terabytes of data from connected
car users from various countries.
 It analyzes and evaluates customer’s lifestyle and based on that it
accessories and can even sell cars based on driving and individual styles.
 Moreover from the telematics data collected, it can predict when the user
need to give their cars for service.
 Data collected includes; speed rate, total driving time, amount of time
spent at certain periods, total number of trips, how long the trunk was
or closed etc.
 Based on these data it allows dealers to provide an offer to the customer.

• Use Case 1:
• Tesla employs similar technologies to connect with its fleet of cars.
• It crowd sources data from all its vehicles and drivers using internal and external
sensors, send data directly to cloud, analyzes them and assists drivers in taking
actions.
• Data is used to generate data-dense maps which features several features
from traffic speeds to the location of hazards on the road.
• A second level of decision making exists where machine learning in the cloud
helps in educating the fleet of cars hence enabling them to take actions that are
required.
• A third level of decision-making also exists, with cars able to form networks with
other Tesla vehicles nearby in order to share local information and insights.

• Use Case 2:
• Artificial Intelligence which is a major component of Industry 4.0 can be further used to
determine the reasons of an accident and also to predict emergency situations inside the
car.
• A startup firm in India developed an AI system uses powerful camera that employs
machine learning techniques to analyze the driving patterns. It thereby helps in
determining the cause of accidents.
• AI enabled smarter sensors can detect any technical or medical emergency situations
inside the car, thus saving from any emergencies. These sensors can also act as security
guard for cars in our absence and uses AI technologies to make predictions.

ANECDOTE OF
THE
METHODOLOGY
ADOPTED IN
USED CASES
Domain
Methodology
Used Case 1 Used Case 2
Marketing
Supervised Learning
Unsupervised
Learning
Operations
Supervised Learning
Supervised
Learning
R&D
Supervised & Re-
inforcement Learning
Re-inforcement
Learning
HR
Unsupervised Learning
Machine Learning
& Re-inforcement
Learning
Finance Supervised
Re-inforcement
Learning
IT Unsupervised Learning
Unsupervised
Learning

REFERENCES
• https://www.forbes.com/sites/bernardmarr/2018/01/08/the-amazing-ways-tesla-is-using-
artificial-intelligence-and-big-data/#6af2ca742704
• https://www.pwc.in/assets/pdfs/publications/2018/advance-artificial-intelligence-for-
growth-leveraging-ai-and-robotics-for-india-s-economic-transformation.pdf
• https://remoto.com/white-paper-artificial-intelligence/Artificial-Intelligence-transforms-
the-automotive-industry.pdf
• BISWAS, J. (2018). top use cases of AI.
• Shields, J. (2018). things you need to know about applicant tracking system.
• sonic automotive case study. (n.d.). Retrieved from hirevue: www.hirevue.com
• https://www.outsell.com/

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