Work zone safety is imperative in today’s world, especially as we race toward a future of autonomous driving. The federal government just took a massive step toward improving the way we think about work zones with the Work Zone Data Exchange. WZDx allows agencies to upload harmonized work zone data to public feeds so that everybody can easily access data and make informed decisions. With this central focus on data, the WZDx initiative is poised to make a significant impact on our roads by more accurately informing drivers of what’s ahead.
Accessing timely and accurate work zone data has proven to be a complicated issue. Since many infrastructure owners maintain the data in their work zones in a paper-focused format, it creates a large issue for third parties to access and understand the information, let alone communicate that information to the public in real time.
According to the FHWA, 857 traffic deaths occurred in work zones in 2020, which is more than three times the rate of non-work zone highways. By making work zone data public, the federal government hopes to increase safety in these areas by allowing drivers and vehicles to know what they’re approaching. The initiative also has the potential to optimize routing and improve congestion.
The WZDx initiative is simple and cost-effective, which is why it might actually work—as long as agencies utilize it to its fullest potential. The WZDx data feed aims to create one common language for work zone data and allow road operators to publish work zone information to a public data repository for anyone to use. It has the potential to truly democratize the world of work zone data.
As U.S. lawmakers ask the USDOT to speed up the deployment of connected vehicles in order to help address the growing number of traffic deaths, the democratization of data could be huge. Under current conditions, connected vehicles will likely not improve the number of traffic fatalities because our infrastructure is not ready. Work zones are one of the largest roadblocks that connected and automated vehicle (CAV) manufacturers face.
While CAV systems are able to identify work zones, it won’t be enough. Since the creation of roads, we’ve been developing infrastructure for humans who can predict and react to complex circumstances on roadways in ways that machines can’t—unless these machines have consistent access to real-time, accurate data. We have technologies like dedicated short-range communications (DSRC) and cellular vehicle-to-everything (C-V2X) for vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communications. Hundreds of millions of dollars have been invested but with little to show for it. The genius of WZDx is that it publishes all data to an open data repository for anyone to use, from app developers to vehicle infotainment systems, with no specialized hardware installed in vehicles required.
By creating a common language for work zone data and by making it accessible to anybody who wants it, these communication technologies may actually have a shot at success. However, the data that agencies choose to share and the way that it is collected will determine how effective it is. The key to gaining adoption will be timely, accurate data available anywhere. Agencies can collect this data by utilizing crowdsourced imagery and artificial intelligence.
Today, agencies manually determine work zone locations and hours of operation internally. When things change on the ground, work zones often change dynamically, leaving an agency without data on when the work zone starts or ends or when lanes are open or closed. If this data was continuously added to the WZDx data feed, we’d have a national, updated database of our construction zones. But humans aren’t perfect, and there’s always the risk that people stop inputting data, forget or may even be out on vacation. Luckily, machines can automatically and accurately automate this process without requiring manual data entry by humans.
Crowdsourcing imagery ensures expansive coverage, and AI allows the imagery to be processed in real time. Dashcams and in-vehicle imagery already exist. It’s just a matter of whether we tap into these resources. This type of technology has countless use cases—from detecting paint lines with 1% accuracy when compared to LiDAR to detecting missing signs. By taking a vision-based approach to data collection and maintenance, we can start to build infrastructure through the lens of an autonomous vehicle.
Companies like Tesla and Toyota have announced their plans for vision-based autonomous driving systems, meaning that the vehicles will mostly use imagery for understanding the world around them. From a maintenance approach, if we can see the roads in the same way that these vehicles will, we can start to build infrastructure for drivers of today and CAVs of tomorrow.
Data is perhaps the most powerful aspect of work zone maintenance. Knowing where your biggest maintenance issues are in real time can ensure quick and effective response times.
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