Autonomous Vehicles Technology Review Report - Semester 1

This report provides a comprehensive review of autonomous vehicle technology. It begins with an introduction to autonomous vehicles, emphasizing the absence of human control and the use of sensors like LIDAR, GPS, and IMU for vehicle localization. The report then delves into a literature survey, discussing the implementation of radar for obstacle detection and the role of artificial intelligence in enabling autonomous operation. The core components of perception, including localization, object recognition, and tracking, are explained. Furthermore, the report highlights the importance of cyber security in autonomous vehicles, addressing vulnerabilities and the need for additional security measures to protect critical infrastructure such as sensors and electronic circuits. It concludes by emphasizing the importance of a layered research approach and proposed guidelines for advancing motor vehicle cyber security to ensure safe and appropriate actions in the event of attacks.

Technology Review #2: Autonomous Vehicles
Introduction
The vehicle which does not requires the physical human Interface to control the functionalities
such as accelerator, moving direction control, use of braking to stop etc comes under the
category of autonomous vehicle series. In simple term autonomous vehicle does not have the
driver assistance for moving on the road, ser or in air.
Google has discovered a autonomous car that runs on the road without the driver control. This
means there is no driver assistance by the human being to drive the car on road. The first part of
autonomous vehicle is sensing. The common sensor which is used with the autonomous vehicle
is Laser Based Radar (LIDAR) (Pomerleau, 1996). The problem of LIDAR is its cost to deploy
with the autonomous vehicle. GPS and sensor such as inertial measurement unit (IMU). The
centimeter level accuracy is defined by combining the data from GPS, IMU and LIDAR to
localize the vehicle.
Literature Survey
To avoid obstacles some more sensors as radar are implemented with autonomous vehicle. This
sensor detects the obstacles in front of vehicle when all other sensors fail to detect the obstacles.
Basically, this sensor detects the obstacles when the vehicle is apart from 10 meters of the
obstacles. Therefore, this sensor avoids he vehicle accidents from obstacles.
In general the Artificial intelligent systems are deployed in the autonomous vehicles to be
autonomous while running on the path.
The major characteristics of autonomous vehicle is that it runs without the intervention and
support of any user . The intelligence is implanted by technology to operate the vehicle without
support of any operator or (Shladover, 2009) user.
The autonomous vehicles are Artificial Intelligence based self monitoring capable for the
running. There are three major components for the perception of the autonomous vehicles. The
first component is localization. This localize the vehicle and on he basis of localization
information the decision about the navigation is taken. Second component is recognition of the

object. To recognize the object deep learning capabilities are enforced into the system of
autonomous vehicle to recognize the objects. For deep learning the camera data is taken to
recognize the object around the vehicle. Third component of autonomous vehicle is racking of
object. The proceeding vehicle is doing what is recognized by deep learning based object
tracking system in autonomous vehicle.
The technology behind the autonomous vehicle is artificial intelligence. This technology has
ability to implant the artificial intelligence to make the system capable to dynamic and situation
based decisions. The artificial intelligence also explores the functional intelligence through the
machine learning and perception enforcement into the system of autonomous vehicle. The rule
based decision engine functionalities are catered the intelligent system having he capability to
drive the decision efficiently whenever system asks or requires. The systematic collaboration of
one system to other system such as the engine collaboration with braking system, accelerating
system, moving direction oriented path ect are mainly a intelligent functional aspects derived
from the artificial intelligent algorithms for the autonomous vehicles. Autonomous vehicle is a
technology whereas the critical infrastructure related with the network that are used to
communicate the sensitive information of the autonomous vehicle during the research and
development. The corporate risk managers expresses the serious concern behind the cyber
security as known as the fleet of the future (Bellatti, J., Brunner, A., Lewis, J., &Annadata, P,
2017) .
The cyber criminals can hijack the autonomous car electronics for the intent of crash and
accident. Self driving cars are susceptible to the hackers. The vulnerabilities are imposed
through the cloud interface network control that is used to connect the autonomous car with
external world. The external network such as cloud is required to internet connectivity for
management of the functionalities of the car. This relies over the outside sensors for making the
decisions which can be hijacked by hackers.
The technology can be used to manage these risks of hijacking the sensors and electronics but it
produces too much complexities in the system. The quick and timely decisions are very much
required to handle the functional aspects of the autonomous vehicles such as cars. The
technology like vehicle autonomous is not the solution for the cyber security. Another
technology such as information security technologies are required here to provide the optimal

security for the electronics and sensors of the autonomous car. Therefore, it is prevalent to the
autonomous vehicle technology to adapt the additional technology to protect the critical
infrastructure related with this.
The security technology is only the solution for protection of overall functionalities of the
autonomous vehicle functionalities and its critical infrastructure. The critical infrastructure of
autonomous vehicle includes the sensors, electronic circuits, outside communication units etc.
these infrastructures are protected by deploying the cyber defense mechanisms with security
technology (Bertozzi, Broggi, & Cellario, 2002). The advancement of the electronics, sensors
and computing power features of the autonomous vehicle focused on cyber security to ensure
proper working of the system as intended.
A layered research approach are required with the industries of autonomous vehicle technology
to ensure the cyber security by improving the functional aspects with secured platform. There Is
also proposed guideline for the advancement of the motor vehicle cyber security. The guideline
include the layered approach and solution to ensure the system to take safe and appropriate
actions while moving on the road or anywhere else. It is also included in the guideline that in the
condition of attack the system, must ensure the safe and proper actions (Coppola, & Morisio,
2016). It is also required to facilitate the rapid response and recovery from the cyber security
incidents and attacks.
Conclusion
Autonomous vehicle is future technology for the vehicles where there is no need of the operator
or driver to control the various functionalities as required for the vehicle. The autonomous
vehicle uses the external network with cloud system to handle the functional aspects of sensors,
electronics by getting the intelligent information from the control center. At present state of the
technology associated with the autonomous vehicle is prone to be attacked by hackers for
intentional activities such as crashing and making targeted accident. So there is requirements of
the future technology to protect the critical infrastructure that are used with autonomous vehicle
system.

References
Pomerleau, D. (1996). Rapidly adapting machine vision for automated vehicle steering - IEEE
Xplore Document. Ieeexplore.ieee.org. Retrieved 31 June 2017, from
http://ieeexplore.ieee.org/abstract/document/491277/
Shladover, S. (2009). Cooperative (rather than autonomous) vehicle-highway automation
systems - IEEE Xplore Document. Ieeexplore.ieee.org. Retrieved 1 June 2017, from
http://ieeexplore.ieee.org/abstract/document/5117654/
Bellatti, J., Brunner, A., Lewis, J., &Annadata, P. (2017). Driving habits data: Location privacy
implications and solutions. IEEE Security & Privacy, 15(1), 12-20.
Bertozzi, M., Broggi, A., & Cellario, M. (2002). Artificial vision in road vehicles - IEEE Xplore
Document. Ieeexplore.ieee.org. Retrieved 31 June 2017, from
http://ieeexplore.ieee.org/abstract/document/1032807/
Coppola, R., & Morisio, M. (2016). Connected Car: Technologies, Issues, Future
Trends. http://dl.acm.org. Retrieved 31 June 2017, from http://dl.acm.org/citation.cfm?
id=2971482