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Assignment II: Technological

The technologies of autonomous vehicles

I. The technologies:

The Autonomous vehicles (AVs) are equipped with these technologies:
  • Cameras: they are used to monitor the traffic, other vehicles, barriers,...
  • Ultra-Sonic Sensors: Imitating the navigation process of bats, ultrasonic sensors send out sound waves. When the waves hit an object they produce echoes – revealing the exact location of the obstacle
  • GPS: GPS or Global Positioning System is a network of orbiting satellites that send precise details of their position in space back to earth. The signals are obtained by GPS receivers, such as navigation devices and are used to calculate the exact position, speed and time at the vehicles location.
  • Infrared: It's used at night to detect moving objects.
  • Lidar Sensors: Light Detection and Ranging (LIDAR) sensors scan the environment with a non-visible laser beam. The low intensity, non-harmful beam visualizes objects and measures ranges- creating a 3D image of the car's environment.
  • Radar Sensors: Known from ship and plane navigation, Radio Detection and Ranging (Radar) sensors send out electromagnetic waves. If they hit an obstacle, they waves are reflected, revealing how far away an object is and how fast it is approaching. 
  • Clouds: The clouds serve as a dynamic electronic horizon – it offers highly accurate real-time map data that vehicles draw on. The data will be constantly updated thanks to the collective intelligence of the vehicles, for instance, reporting closed lanes or defective traffic lights – therefore it also is a type of sensor, providing the AV of its surrounding.

II. Details for Clouds:

Autonomous vehicles require intensive parallel computation cycles to process sensors’ data and efficient path planning in the real-world environment. It is certainly not practical to deploy massive onboard computing power with each agent of autonomous vehicle. Such deployments will be cost-intensive and may have certain limitations in parallel processing. Cloud provides massively parallel on demand computation, up to the computing power of super computers, which was previously not possible in standalone on board implementations. Nowadays, a wide range of commercial sources (including Amazon’s EC2, Microsoft’s Azure and Google’s Compute Engine) are available for cloud computing services, with the aim to provide access to tens of thousands of processors for on-demand computing tasks. Initially, web/mobile apps developers used such services; however, they have increasingly been used in technical high-performance applications. Cloud computing can be used for computationally extensive tasks, such as to find out uncertainties in models, sensing and controls, analysis of videos and images, generate rapidly growing graphs (e.g. RRT*) and mapping, etc. . Many applications require real-time processing of computational tasks, in such applications cloud can be prone to varying network latency and quality of service (QoS), and this has been an active research area nowadays.

III. References:

- Here is how the sensors in autonomous cars work:


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