Telematics is the branch of information technology which deals with the long-distance transmission of computerized information. It is the convergence of telecommunications and information processing, the term later evolved to refer to automation in automobiles, such as the invention of the emergency warning system for vehicles. GPS navigation, integrated hands-free cell phones, wireless safety communications and automatic driving assistance systems all are covered under the telematics umbrella. This is a basic presentation which describes a futuristic model of telematics system which will not only provide the vehicle user guidance, emergency or road side assistance or vehicle diagnostic/ health report on data captured but will also provide in much more deeper level a real time risk of any failure or service requirement in a vehicle, User Based Insurance services, driver detection etc. These predictions and services are implemented by highly sophisticated and well-integrated advanced telematics model and the intelligent optimization and management of on board and off board data with the help of machine learning.

1. ADVANCED VEHICLE TELEMATICS
SYSTEM MODEL
Guided by :- Dr. Dileep P N
Presented by :- Dhaniklal K C
Department of Mechanical Engineering, TKMCE
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2. 2
CONTENTS
• Introduction to telematics.
• Evolution of vehicular telematics.
• Advanced telematics system.
• New lifestyle delivery.
• Machine Learning.
• Vehicle predictive maintenance.
• Optimized UBI.
• Telematic fingerprint.
• Advantages.
• Disadvantages.
• Conclusion.
• Reference.
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3. Ever thought
about future
cars?!
“Telematics”
3
Branch of information technology
which deals with the long-distance
transmission of computerized
information
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4. TELEMATICS IN AUTOMOBILES
During 90’s During 2010’s
•Satellite Navigation
•Vehicle Tracking
•Multimedia
•Internet
•Mobile Data
•Wireless vehicle safety
communications
•Emergency warning system for
vehicles
•Usage based Insurance
•Vehicle user guidance
•Vehicle diagnostics
etc.,.
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5. ADVANCED INTEGRATED FUTURE
VEHICLE TELEMATICS SYSTEM MODEL
Features:
•Provides a real time risk of any failure or service requirement
in a vehicle in depth.
•UBI (User Based Insurance)
•Driving behavior detections and Telematic Fingerprint.
•Application of machine learning
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6. 6
HIGH LEVEL MODEL
ECUSENSORS ACTUATORS
COMPUTING UNIT
PREPROCESSOR
PREPROCESSORCloud Computing
units/Cloud servers
DATA CENTERS
DISTRIBUTION CENTERS,SERVICE CENTERS
INDUSTRIES AND SERVICE SECTORS
On Board Vehicle
Devices
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7. 7
LIFESTYLE DELIVERY
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9. 9
VEHICLE PREDICTIVE MAINTENANCE
 Predictive Maintenance : Employs monitoring and prediction modelling to determine
the condition of the machine and to predict what is likely to fail and when it is going to
happen.
 On Board Data : Signals from sensors and ECUs, that are communicated through
CAN network . They are sent repeatedly with a specified frequency.
 Off Board Data : Data from off Board data centers .Most corporates like VOLVO
Group have accumulated large amounts of data over years in Off Board databases.
 LVD : Logged Vehicle Data.
 VSR : Vehicle Service Records.
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Applying ‘Machine Learning’
LVD and VSR databases are combined ,preprocessed and data mined for patterns linked to
different components of vehicles by Supervised approach.
Vehicle Date Mileage LVD1 LVD2 ... Time to Failure Label
A-12345 2010-06-01 1030023 100 35 ... 125 Normal
A-12345 2010-08-15 1123001 101 25 ... 50 Normal
A-87654 2010-04-20 9040223 120 29 ... 110 Normal
A-87654 2010-01-21 9110223 121 26 ... 21 Faulty
A-34567 2010-11-05 1330033 90 23 ... >301 Normal
A-34567 2011-03-11 1390033 121 26 ... >175 Normal
Learning from historical data :
Algorithms: KNN, Random Forest learning algorithm.
Software: R
Model of training set
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OPTIMIZED USER BASED INSURANCE
• Huge amount of data that needs to be analyzed is the major challenge.
• TensorFlow machine learning framework has been used by AXA on the Google
Cloud Machine Learning Engine for predicting "large-loss" car accidents involving its
clients
• Identify which drivers are at higher risk for cases of large loss
• By using deep learning (neural network), 78% accuracy in predictions has been
obtained.
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12. 12
Neural Network Model
Features :
 About 70 values including:
• Age range of the driver
• Region of the driver's address
• Annual insurance premium
range
• Age range of the car
 Three hidden layers, with a
ReLu as the activation
function
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13. 13
DRIVER BEHAVIOUR DETECTION AND TELEMATIC
FINGERPRINT
• Identification of which trip was driven by whom.
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14. 14
GPS Data:
• Preprocessing the data
• Driving Vocabulary - made of “Driving Slides”
• Each trip –combination of “Driving Behaviors” made
of “Driving Slides” –Using Gensim Library
KAGGLE COMPETITION
200 Trips,2736 Drivers
5.92 GB GPS Data
Training Data Preparation :
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15. 15
Approach:
• Transpose all trips into the new Driving Behaviours Space.
• Take one by one each trip from a selected Driver
• Build a prediction model trained with all other trips in the
dataset:
Trues if they belong to the selected Driver
Falses if they do not belong to this Driver
• Predict with the trained model, the belonging of the selected Trip
to the Driver
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ADVANTAGES
 Increased Personalization and interactivity
 Easier to use
 Emphasis on infotainment
 More local information
 Safer vehicles
 More sophisticated insurance
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DISADVANTAGES
 Criminal gangs intent on:
• Stealing Personally Identifiable Information
(e.g. Credit Card numbers)
• Deploying “ransomware”
 Rise of car hackers
 Lack of privacy
 Unemployment
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CONCLUSION
The futuristic telematics model is a highly optimized, safer and
intelligent approach.
More optimization and development of mathematical models can
be taken up with the advancements in data science and machine learning.
The importance of data and its implementations are going to be the
most important entities of the future.
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REFERENCE
 “Automobile Driver Fingerprinting,Miro Enev, Alex Takakuwa, Karl Kosher
Proceedings on Privacy Enhancing Technologies ; 2016 (1):34–51
 “Advanced Integrated Future Vehicle Telematics System Concept Modeling”
Ashmika Agarwal and Vinay Yadav; Agarwal et al., Global J Technol Optim 2017,8:2
 “An Intelligent Data Processing Engine for Spatial Data Management in Vehicular
Telematics System” Animesh Tripathy, Subhalaxmi Das, Prashanta Kumar Patra 2009
International Conference on Advances in Computing, Control, and
Telecommunication Technologies
 "Predicting the Need for Vehicle Compressor Repairs Using Maintenance Records
and Logged Vehicle Data" submitted to Engineering Applications of Artificial
Intelligence,2014. R. Prytz, S Nowaczyk, T Rögnvaldsson, S Byttner.
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THANK YOU
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