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First stretch of smart highway opens in the US

In 2020, Cavnue was selected by the Michigan Department of Transportation (MDOT) to develop a highway corridor for connected and automated vehicles

Published: Jul 6, 2024 07:21:41 AM IST
Updated: Jul 5, 2024 05:28:48 PM IST

Cavnue has finished construction on the pilot phase of a first-of-its-kind connected corridor in Michigan.
Image: Courtesy of Cavnue©Cavnue has finished construction on the pilot phase of a first-of-its-kind connected corridor in Michigan. Image: Courtesy of Cavnue©

Several kilometers of a future "smart" highway were recently inaugurated in the US state of Michigan. The idea is to be able to connect the highway to all compatible vehicles in order to warn them in real time of upcoming incidents or congestion.

In 2020, Cavnue was selected by the Michigan Department of Transportation (MDOT) to develop a highway corridor for connected and automated vehicles -- a major first. This initiative is designed to improve safety and congestion on one of the state's main thoroughfares, Interstate 94. Cavnue is a Washington, DC-based subsidiary of Alphabet's Sidewalk Infrastructure Partners.

The first 5-kilometer corridor of this "smart" highway has now been inaugurated west of the town of Belleville. This pilot project combines a multitude of advanced digital and physical infrastructure, including cameras, sensors, wireless communications systems and digital twins, which are virtual recreations of existing infrastructure, in this case a road network. Once completed, the entire corridor will be just over 60 km long, running from downtown Detroit to the university town of Ann Arbor.

The idea is to provide sufficiently safe lanes for automated vehicles, so that minimal driver input is required. This theoretically applies to motorists as well as truck drivers. Among other things, road users equipped with compatible technology driving on these lanes will be able to be instantly informed of potential safety risks, such as the presence of stalled vehicles or debris on the highway, while also staying up-to-date in traffic congestion. All these incidents will also be reported to emergency intervention services, to reduce response times.

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Technically, the images and data captured are analyzed in real time using machine learning algorithms. The system can already identify an accident, a stalled vehicle or a potential hazard on the road. In the event of an incident on the road, alerts are sent to both users and the relevant authorities.

While this is not yet the case for many people, Cavnue expects half of cars to be connected and have some level of autonomy by the early 2030s.

A similar project is due to get underway near Austin, Texas.