This document discusses using HPCC Systems' big data analytics platform to conduct risk analysis on autonomous vehicles using a dataset of over 700,000 data points representing vehicle simulations. It also assesses potential cybersecurity risks associated with connected autonomous vehicles. The document introduces the topic, describes how ECL was used to query the dataset and draw conclusions, and discusses insurers facing decreased losses of $165 billion by 2050 due to autonomous vehicles. It concludes that autonomous vehicles will significantly impact society and the economy, but also presents new challenges regarding safety, financial impacts, and cybersecurity that must be addressed carefully.

1. Intro/Abstract
Section 1: Why does this matter?
Section 3: What are the potential IT security risks?
Section 2: How did I use HPCC Systems?
Introduction:
The concept of self-driving vehicles is
one that has fascinated mankind for
generations. It was once a topic thought
to be so farfetched that it could only
exist in a child’s cartoon. Yet, society
now finds itself thoroughly entrenched
in this new reality. M any enterprises
have already taken major strides toward
progressing on this once fanciful idea
w ith a new sense of validity. Racing
closer to that dream, we must pause to
consider the potential risks that we may
be creating for ourselves.
This project focuses on risk analysis
regarding autonomous vehicles using
HPCC Systems’ big data analytics
platform. The central instrument of this
research is a dataset composed of over
700,000 individual data points. These
points represent simulations of the
pertinent information autonomous
vehicles will track and log. The columns
in this dataset are: vehicle ID,
timestamp, x coordinate, y coordinate,
and velocity.
As a secondary focus of this project,
there is an assessment of the potential
cybersecurity risks associated w ith this
topic. Assuming these new vehicles w ill
be connected to the internet and to the
infrastructure in the world around them,
the question is posed: How w ill these
vehicles respond to cyberattacks? Conclusion and Future work
Section 1: Why does this matter?
Section 1: Why does this matter?
Section 1: Why does this matter?
Everett M atthew Upchurch Butler | EBUTLE19@students.kennesaw.edu
Route 1 Risk Factor: 45 Route 2 Risk Factor: 30
There are currently five recognized
levels of autonomy in vehicles.
The most advanced self driving
vehicles available today are level 2.
Predictions suggest by 2030,
approximately 77% of all vehicles on
the road w ith be either lever 3 or 4.
Instantaneous Traffic Density Map
Enterprise Control Language
(ECL) was used to query the
dataset and form meaningful
conclusions.
This show s an example of the
code used to query the data.
This query returns the number
of occasions a specific vehicle
was “ speeding” during a given
time frame.
Solution Architecture
Route Choice Risk Calculation
Insurers are predicted to
face a decrease of 81% in
overall personal auto losses
by 2050.
This equates to
approximately $165 Billion
lost.
Source: KPM G LLP actuarial analysis
NHTSA multilayered approach to autonomous vehicle cybersecurity:
The average modern vehicle
has over 100 M illion electronic
units.
This creates a vast number of
potentially vulnerable areas.
Source: The National Highway Traffic Safety
Administration
Conclusion:
It is no longer a matter of if a world with self-driving vehicles is a plausible reality. Experts in both the automotive and insurance industries are already busy
trying to understand how this burgeoning technology w ill impact our world in the not-so-distant future. Those who are the pioneers, understand why it is
critical to get onto the leading edge of this revolution. Autonomous vehicles are not going away. Our society is changing in a drastic way because of this. Not
only w ill these vehicles change our human behavior, they w ill also have lasting financial impacts on our economy, and our standards and expectations for
public safety. It is worth noting that the adoption of this technology is not w ithout its risks. As we move forward, we must take care to use good judgement,
and be thorough in our preparations. With each new technological revolution, we are exposed to new challenges that we have never before faced. As
conscientious and contributing citizens, it is not simply our goal to overcome these challenges, it is our duty and our obligation.
The Future of Automotive Telemetry
A n A ssessment On Inherent Risk Implications & Cyber-Security Vulnerabilities
1. Prioritized identification process for safety-critical vehicle control systems.
2. Rapid detection and response to vehicle cybersecurity incidents on roads.
3. Architecture and methodologies that build in cyber resiliency.
4. Intelligence and information sharing across the industry.

2. Transforming our technology, because
standing still is not an option
OCTOBER 9, 2018
2018 HPCC Systems Conference
Arjuna Chala
The Multidimensional Programmer

3. Do you code ECL? Do you use JavaScript/HTML?
Do you work on OSX or Ubuntu?
Do you use Node.js?
Build Extensions?

4. Welcome to Multidimensional programming Interface – VS Code
File/Workspace Explorer
Search/Replace
Git
Run/Debug
Manage Extensions
ECL Workunit Browser
Code Outline
Code Editor

5. Btw, VS Code works on MS Windows too
1000s of extensions Multiple Lang support

6. VS Code is built on the Electron Framework
Non blocking architecture enables for
fantastic User Interface Responsiveness …….

7. Blocking Example
Non-Blocking Example
Brilliant for UI
rendering

8. Code a Cancer Research Use Case with VS Code
Sample Raw Data – BYAGE.txt
Data Source

9. Goal is to create something like….
But, with a little more excitement

10. Let us begin
1 2
3

11. VS Code
1
2
Project Folders
Next…
Client JS Code
ECL ETL and Job
Code
NodeJS Server

13. 1) Data
2) ECL Queries
3) A Dashboard Framework (Dazz)
For viewing Cancer Research we needed three things

14. After coding for about 10 days …..

15. ECL queries and ETL for Cancer Research
ECL queries for the Dazz framework
ECL queries for the Sales Demo
https://github.com/hpcc-systems/Solutions-Dazz

16. Client JavaScript dashboard framework
based on Google Polymer 3
Node.js server

18. ECL Sample

19. JS Client Sample

20. Server Sample

21. Using Dazz you can produce pure ECL code that would create the dashboards

22. +
The Multi Dimensional Programmer, where nothing is impossible
=

23. Thank You!!!