Connected vehicles

Significant progress has been made to apply cellular and dedicated short range communication technologies to support a two way link between vehicles and roadside infrastructure. The various communication links involved are described using the following abbreviations:

• V2X: a two-way communication link between vehicles and several other elements such as other vehicles, roadside transceivers, or wide-area cellular networks
• V2V: a two-way communication link between vehicles, from one vehicle to another
• V2N: vehicle to network, another way to describe a two-way communication link from vehicles to a communication network such as cellular

While early work focused on short range communications, latest implementations have incorporated the use of cellular V2X approaches. It is likely in the future that the three different approaches will be used in combination to create additional capability.

In parallel with the development of wireless communication techniques, there has been significant progress in the use of data inside the vehicle. Engine management systems and carry area networks provide the possibility for a rich stream of data to be received from the vehicle. Now this data is only available to mechanics and technicians and other systems on board the vehicle. By combining this advance, with connected vehicle technology, the possibility now exists to externalize in vehicle data and use it as the basis for better insight and understanding for traffic and transportation management. At a basic level, the ability to receive data from the vehicle regarding vehicle identification, instantaneous vehicle speed, original origin for the trip and ultimate destination for the trip will be an important part of the smart mobility revolution. 

The availability of this data will cause sudden and dramatic change in our approaches and our ability to manage traffic and transportation. This new data can be the basis for more sophisticated transportation planning and more effective traffic engineering. Vehicle speed data reduces our dependence on collecting such data from roadside infrastructure sensors, while also offering a much finer level of detail and a better understanding of the variation of vehicle speed throughout the network. An understanding of the original origin and ultimate destination of the vehicle also enables us to place existing investments in traffic signal control and advanced traffic management within the wider context. Such techniques, now, focus on vehicle trip patterns and behavior within the control network and have little information on how the vehicle entered and exited the network. Possessing this additional information will allow us to take a wider, multimodal approach to citywide transportation management. Beyond a basic set of data, the following data can also be obtained from connected vehicles:

• Road conditions: sensors on board the vehicle detect road geometry changes and condition of the road surface. Sensors also detect average vehicle speed which can indicate traffic conditions being experienced

• Environmental conditions: sensors on board the vehicle measure ambient temperature, detection of windshield wiper use can detect rain

• Operating status of the vehicle: a wide range of data that describes the operating status of the engine, transmission, wheels, and other onboard equipment

• vehicle usage: additional sensors monitor vehicle speed, location, and average load weight

• Driver behavior: the use of cell phones and Internet-based services can be monitored

Estimates vary widely, but it is expected that the full set of data that can be obtained from or connected vehicle could be as large as 300 TB per day.

We need to get ready to manage large volumes of data from vehicles and efficiently turn into information. We need to revise our current analytics to include the availability of the new data and develop new analytics for new insight and understanding. We need to make sure that we have data sharing agreements in place to enable us to access the data from the vehicle. This will include a clear definition of data ownership. It is a multitude of new services that can be supported from the new data and you approach to existing services. For example, the insurance industry can be revolutionized by using the data to customize insurance rates the vehicle and driver behavior.