The Autonomous Future Of Road Management
The first emperor of Rome, Augustus Caesar, thought good roads so important he retained the title of Curator Viarum or Commissioner of Roads. For Augustus, road maintenance and maintenance of a strong defense were among the prime duties of government. While we use a variety of impressive advanced materials and technologies today to preserve our global network of eleven million paved road miles, some methods in use today have changed little from the methods the Romans used two millennia ago to manage fifty thousand miles of stone-paved roads.
For the Romans, road monitoring consisted of a chariot driver accompanied by one or two Lictors or road inspectors visually inspecting the superficiem via and miliarium, road surfaces and signs respectively, for overall condition. Along the way they were careful to make notes about what maintenance was needed where and by when.
Today, when, where, why and how we maintain roadways is changing and dramatically so. Big challenges and new opportunities are in play and driving change in the United States and globally.
In the US, the challenge comes from a roadway infrastructure built up over a century that is now showing its age. According to TRIP, a national transportation research group, twenty-eight percent of major US roads are rated "poor" or in need of a complete rebuild. That translates into about $1.25 million per mile to re-mill and resurface a four-lane road, for example. When, again according to TRIP, you add the burden of a $515 annual per vehicle cost for operations and maintenance upkeep of the US fleet of 260 million passenger cars then improved road quality is even more imperative. However, for most in the know about US roads the question is not if but how.
Fortunately, an opportunity is also emerging that is causing a rethink of the way we manage roads. While a growing web of road sensors, in the form of inductive loops, non-intrusive traffic detection devices, and video cameras on or along highways and urban streets are collecting vast amounts of data, a still larger tsunami of roadway data is accumulating that will make the data generated data by Facebook, Amazon and Google seem paltry by comparison: autonomous vehicles.
According to Brian Krzanich, Intel CEO and a leader in the emerging autonomous vehicle space, “Data is truly the new currency of the automotive world.” He added, “In an autonomous car we have to factor in cameras, radar, sonar, GPS and LIDAR … Run those numbers, and each autonomous vehicle will be generating approximately 4,000 GB – or 4 terabytes – of data a day.” If in the next few years only ten percent of the current US passenger fleet became self-driving then those 26 million vehicles would generate an astounding 38.4 zettabytes of data annually. To put that number in perspective, one year’s data production in this scenario is over eight times the volume of all the world’s current data.
That is a lot of data and, in fact, so much so that no single organization of any size on the planet currently has the capacity to manage and exploit it all.
Nevertheless, some have started down this path. For example, Ford is investing $200 million in a new data center in Flat Rock, Michigan to support its own autonomous vehicle efforts and they expect their data storage requirements to grow from 13 petabytes now to over 200 petabytes by 2021.
Others are taking a collaborative approach to the massive data challenge similar to the Star, Oneworld and SkyTeam airline alliances, where competing airlines share complex and expensive infrastructure to lower operating and capital costs with the result lower of ticket prices for all consumers. A wide variety of autonomous vehicle industry players, including automakers, tech companies, equipment manufacturers, governments, civil engineering firms, to name a few, are working together in innovative ways to capture, fuse and use the data that each is collecting separately. A prime example is the mapping company, here, which is owned in part by a consortium of the automotive giants Audi, BMW and Daimler, as well as Intel. One likely and important outcome of this effort for everyone will be better roads.
One obvious beneficiary of all of this data will be the roadways themselves, which is not surprising given that roads and vehicles retain a symbiotic relationship. According to Andrew Ng, one of the world’s leading machine learning experts, one of the most important qualities of a roadway – for human and non-human driver alike – is predictability. Dr. Ng is adamant that most of the world’s roadways simply don’t make the grade. “The problem with poorly maintained roads is not only that they're harder to navigate,” he asserted in a recent Wired article, "Self-Driving Car Won’t Work Until We Change Our Roads."
"But that computers and humans are no longer able to accurately anticipate where others will drive, thus reducing predictability,” he added.
The growing autonomous vehicle fleet, together with countless truck and passenger vehicle fleets on the road now, will be instrumental in passively – read inexpensively – gathering timely, precise and local data that is so essential to better roads. With success, the centuries old process of manual inspection will be replaced with a more cost-effective and cost-effective methods for monitoring roads. While there are admittedly more technical solutions available for assessing road surfaces, including mainly inspection vehicles that use combinations of RADAR, high-definition cameras and LiDAR, these methods often come at a steep money and labor cost, a cost that dramatically limits the frequency of use and, for smaller municipalities, even the affordability.
RoadBotics takes the view that still-better-than-good-enough data fidelity, extreme ease of use, vanishingly small implementation cost, makes for a powerful tool for roadway managers to use in maintaining a high road surface and roadway quality. “It's cutting-edge technology. This has brought us up to the next level,” Richard Albert, Director of Public Works North Huntingdon, Pennsylvania, said. “We're getting a lot of accolades for being part of this.”
The RoadBotics approach takes advantage of what is readily available, which includes a smartphone, a smartphone app and a windshield to collect the data.
Once the data is collected and sent to the cloud the data is analyzed using advanced AI technology. RoadBotics then output the resulting information on the location, size and type of damage for any defect identified and is reported to a city on an overhead map, using color-coded markers to superficially present the presence and degree of road damage.
All of RoadBotics customers can drill further into the data represented by map markers to better understand their numeric evaluation of the defect, view photographic evidence of it, and even override our ratings on occasion and as necessary.
AI technology is all around us, including now all along the road and as advances and the familiarity with autonomous vehicles grows then any number of opportunities to improve our roadways will emerge. We only need only look at roadways as Caesar Augustus did, as one of our most precious assets worthy of our greatest efforts.