Autonomous Vehicles


Autonomous Vehicles

Autonomous vehicles are no longer just a technology for the future. USDOT has issued its Future of Transportation-Automated Vehicles 3.0 guidelines and directions: https://www.transportation.gov/av/3

The current levels of Autonomous Vehicles are detailed below.


An automated vehicle uses a variety of sensors to collect data about the surrounding environment. Maps and GPS help to localize the vehicle. Onboard computers analyze the data collected by the sensors, as well as the mapping data, to determine the best course and drive the vehicle.

Automated vehicle sensors include:

  • Radar: Detects objects by using radio waves to measure the distance between the host vehicle and nearby obstacles
  • Lidar (Light Detection and Radar): Creates a 360-degree image of the surrounding environment using laser beams. (Some lidars are not 360-degrees. Solid state lidars, for example, can have a limited field of view.)
  • Cameras: Help determine the distance between a vehicle and other objects. They also “see” traffic signals, pedestrians, bicycles and other obstacles.

 

Pilot programs in trucking, taxis, transit and shuttle technology are underway today. Intel outlines the speeds of some of the technologies enabling autonomous vehicles.

 


Autonomous Vehicles do not have to be connected to operate in Level 3 Autonomy. To realize the benefits of Intelligent Transportation Systems, Autonomous Vehicles must be Connected to enable vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) communications at Level 4 and 5.

 

 

DSRC network technology has been the source of much of the connectivity utilized for pilot programs but there is a growing movement to use C-V2X technology which will be based on 4G and 5G network capabilities. Intel predicts that a Connected Autonomous Vehicle will generate 4Tb’s of data per day during the usual daily use of 1.5 hrs.

 

Federal Safety Vision

https://www.nhtsa.gov/sites/nhtsa.dot.gov/files/documents/13069a-ads2.0_090617_v9a_tag.pdf



Archives

Use Cases

City of Bellevue-Smart Mobility Plan

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Autonomous and Connected Vehicles in Arizona

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Connected and Autonomous Vehicles-Florida

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London Street Controls

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Jacksonville, NC Intelligent Transportation System

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B&P Enterprises

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B&P Enterprises Fleet Management using AT&T

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Massachusetts 100% E Toll System

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Google Self Driving Car

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M2M and Traffic Safety Kore

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Traffic and Road Monitoring in Malag

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Connected Car in Smart City

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German Autobahn System

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Sensor Networks

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UNDERGOUND WIRELESS DESIGN FOR RAPID RAIL TRANSIT: CHALLENGES AND BEST PRACTICES

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Library

5GAA White Paper on C-V2X

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Autonomous Vehicle Safety

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Volvo Future of Transportation

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Transportation Networks Building Intelligent Roadways Connected Cars

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Free Book Downloads on NYC Subways

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Intelligent Transportation Systems Traffic Management

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Autonomous Cars

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Smart Roads and Sensors

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Airport Wi Fi Security

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Transportation Revolution Panel

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Transportation in Smart Cities Venkatraman

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Big Data MIT Transportation

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Connected Car

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Potential Issues with V to V

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Telematics

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Waze

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Delta Data Center Crash Power Issue

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Florida Department of Transportation

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How Red Light Cameras Work Simulation

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How Driverless Cars Work

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