Whether it’s barrels blocking lanes or paint lines that aren’t visible, we’ve been building and managing roads for humans who can guess and anticipate intricate situations on the roads in ways machines never will. With the impending shift to self-driving vehicles motivated by the demand for shipping in the online economy, increasing labor and cost pressures and environmental effects, the need for autonomy has never been higher.
While time will tell what our reality looks like in 10 or 15 years, we can expect to start seeing more autonomous vehicles on our highways even sooner. With a delivery truck completing an autonomous 132-mile beer delivery in Colorado in 2016 and Tesla’s full self-driving beta allegedly available in May 2023, we’re already seeing these technologies start to emerge. We’re also anticipating how transport and roadway authorities will manage assets and infrastructure in a way that aids and doesn’t hinder these coming technological advancements.
Have you driven through a work zone only to find construction barrels that may have been hit by a vehicle or blown over by a heavy gust of wind? These types of scenarios are cause for concern because an autonomous vehicle could interpret this obstacle as a closed lane and potentially come to a complete stop due to a lack of clarity on where it can drive—something a human would note as an anomaly more easily. The same thing might be true for bad paint lines that aren’t straight or were accidentally placed too close or far, thus restricting a lane.
Some agencies are investing in solutions for these types of environments. For example, 3-D scanning is being used to determine which areas of pavement need to be stripped and repaved in Canada. Other places, such as Texas, are welcoming AI video analytics on their roads to increase connectivity.
Given the trends in places like Canada and Texas, it’s obvious that innovative technologies are going to play the most critical role in autonomizing our roadways. Departments of transportation all have one goal in mind: understanding what’s going on where and being able to fix those issues as quickly as possible. Real-time situational awareness is what every department has strived for since the creation of roadway management, and with autonomous vehicles on the way, this goal is more imperative than ever. If vehicles on our roadways are advancing technologically, then so should the roadways themselves.
For the most part, departments currently rely on a boots-on-the-ground approach to collect information on roadways. This usually entails crews having to drive roadways to manually find issues through lidar scanning, surveying and other methods. Now that technologies like artificial intelligence, image processing and machine vision have become more widely available, this manual approach to collecting data seems obsolete and inefficient. That’s because it is.
This method of data collection made sense for the last century, but the world is technologically advancing at a rapid pace and our infrastructure is falling behind. The nation’s Infrastructure Report Card—in which the U.S. earned a “C-”—states that “43% of our public roadways are in poor or mediocre condition.”
How are we supposed to rely on machines to drive us if almost half of our roadways are in dire need of maintenance? It’s time to rely on machines to also help manage our roadways. In order for autonomous vehicles to be successful, they will require a constant stream of up-to-date information on the condition of roadways. The only way for this constant communication between roads and vehicles to occur is if machines are monitoring our roadways and creating digital twins.
The solution is simple: Autonomous cars need autonomous roads.
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