By Mark F. DeSantis, Chief Executive Officer, RoadBotics
The first Roman Emperor, Augustus Caesar, thought good roads so essential that he retained the title of Curator Viarum or ‘Commissioner of Roads.’ For Caesar Augustus, maintaining roads was elemental to a vibrant economy and a strong defense. Road maintenance was a prime duty of government and regular road inspection was a critical aspect of it. In that day a chariot driver, accompanied by one or two Lictors, or ‘Road Inspectors,’ visually inspected the superficiem via and miliarium, (‘road way’ and ‘road signs’ respectively) for overall condition. Along the way, inspectors were careful to make notes about what maintenance was needed, where and when, then share it with the local road crews for fixing and upkeep.
While road technologies have advanced beyond the imagination of the ancient Romans, road inspection has not. Manual visual inspection is still the dominant method of road inspection and maintenance around the world, though likely via a Ford F150 or Toyota truck rather than a chariot. Fortunately, advances in AI, and deep learning in particular, are causing civil engineers to rethink when, where, why, and how we maintain roadways. This change could not happen soon enough.
The multi-million-mile US road network, built up largely in the last century has an estimated replacement cost of $6.5T and, regrettably, it is now showing its age. According to TRIP, a US transportation research group, 28 percent of US roads are rated “poor” or in need of a complete rebuild, which translates into about $1.25 million per mile to re-mill and resurface a four-lane road. 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. Interestingly, similar challenging numbers exist for the EU as well. The question is not if roads will be improved but how and when.
Yet new technologies, in combination with tried and true roadway assessment methods, is bringing on a massive rethink of the way roads are managed and maintained. First, an ever-growing web of nearly ubiquitous sensors on, along or near roads – in the form of inductive loops, non-intrusive traffic detection devices, and video cameras – are collecting vast amounts of data. Second, and even more profound, is a growing tsunami of roadway data actively and passively being accumulated by hundreds and perhaps thousands of vehicle fleets globally in anticipation of the advent of 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 collection in this scenario is almost an order of magnitude greater volume of data than the entirety world’s data today.
In fact, it is so large that no single organization of any size on the planet currently has the capacity to manage and exploit it. 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 which, in turn, lowers 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, 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 will be better roads for everyone.
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 Cars 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.”
The growing autonomous vehicle fleet, together with countless truck and passenger vehicle fleets on the road now, will be instrumental in passively and, as a result, 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 methods for monitoring roads. While there are admittedly more technical solutions available for assessing road surfaces, including inspection vehicles that use combinations of RADAR, high-definition cameras, and LiDAR, these methods often come at a steep cost in terms of money and labor, a cost that dramatically limits the frequency of use and, for smaller municipalities, the affordability.
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 outputs 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.
AI technology is all around us, including along the road, and as advances and the familiarity with autonomous vehicles grows, any number of opportunities to improve our roadways will emerge. We need only look at roadways as Caesar Augustus did, as one of our most precious assets, worthy of our greatest efforts.
Want to learn more about Roadway Data from Mark? He will be speaking at the Urban Mobility Summit, 16-17 October in Paris. Get your pass today!
Like what you read? Sign up for the Urban Mobility Weekly newsletter to get more articles like this.