How autonomous mobility and smart cities are defining the future of mo

The first moving assembly line for the mass production of automobiles was installed by Henry Ford on Dec. 1, 1913. This innovation enabled cars to be built in a fraction of the amount of time it previously took—reducing it from 12 hours to one hour and 33 minutes.

While vehicles were beginning to be mass-produced, Henry Ford puts his support behind the National Highways Association. The NHA developed a plan for a 48,000-mile network of roads. This network of roads would be built, owned, and maintained by the national government and connect cities and towns across the country. This evolved into the Interstate Highway System. For Ford, both the vehicle and the infrastructure had to evolve together to unlock the full potential of mobility options.

Designing and building vehicles and roads was only part of the process. Cities had to account for mixed-use transportation, including pedestrians, horses and buggies, cars, bicyclists, and trolleys. Movement at different speeds and mobility types required communication, coordination, added planning, and collaboration among city planners, carmakers, government officials, and consumers. The result was transportation innovation that laid the foundation for the next century of growth across the US


Fast forward to 2007. A team at Carnegie Mellon University demonstrated how an autonomous vehicle could safely operate through city streets and other urban driving conditions. The DARPA Urban Challenge launched a whole new level of possibilities for how computer-driven vehicles could once again change the future of transportation.

Thanks to advancements in computing, data intelligence, machine learning, and artificial intelligence, whole fleets of autonomous vehicles are operating on roadways in Arizona, California, Nevada, Michigan, and other states. Autonomous semi-trucks are navigating highways in different test scenarios. Meanwhile, multiple autonomous shuttle deployments are underway in downtown Las Vegas, on a medical campus in Georgia, in a retirement community in California, and at entertainment parks in Florida.


Every human decision made in driving and navigating a vehicle represents a line of code in computing. Adjust a mirror, slightly turn a wheel, look over a shoulder, press on the accelerator, tap on the brake, enter a four-way stop—every step requires lines and lines of code to perform the same action with computer-driven vehicles.

Extrapolate all the human decisions made when driving a vehicle across towns, cities, and states times millions of drivers—and all the infrastructure required to communicate with vehicles—and it adds up to a lot of data. A single autonomous vehicle will produce up to 5,894 TB in just one year—more data than produced by 320 million Twitter users in the same period.

Data creates the ecosystem that will drive the future of autonomous mobility. Autonomous vehicles produce enormous repositories of operational data that enable the movement of one vehicle to better inform the safe operations of others. Steer around a pothole, alter course due to a road closure, change speeds due to weather conditions—this is all movement data from one vehicle that can alter how other vehicles approach and operate in dynamic conditions.

With more autonomous vehicle deployments in cities across the US, road mapping, vehicle operations, and subsequent data-capture become the eyes and ears of city infrastructure. Autonomous vehicles are an extension of city operations when it comes to changing road dynamics, signal outages, accidents, crowded areas, and many more scenarios. Having access to road data via autonomous vehicle operations allows for more dynamic road planning, improved emergency response, event management, and delivery of new transportation and infrastructure services.


Autonomous vehicles capture 3D lidar, radar, and multi-sensor fusion data to inform larger autonomous vehicle fleets of their physical surroundings related to people, streets, and their operating environment. On-board cameras and long-distance radar provide a bigger streetscape view for improving navigation and situational awareness of vehicle movement.

It also enables transportation providers to dynamically redirect transportation services to deliver more on-demand accessible transportation to all parts of a community. This technology can identify edge cases—unusual scenarios such as a stop sign placed inside a traffic cone—which allows for both humans and computers to analyze and determine how autonomous vehicles approach and operate in similar driving situations.


Advanced vehicle and infrastructure data can shape long-term city design operations. Data modeling can help city planning and operations with:

  • Where to deploy new transportation services
  • How to avoid legacy safety issues and advance incident response
  • Opening new social and economic development
  • Future-proofing projects for longer-term community benefit
  • Creating a dynamic city environment that can evolve with changing needs of the population

The next “Henry Ford” moment is here. While autonomous mobility is one of the first steps of the last mile, autonomous-mobility players and smart cities that harness advancements in technology across AI, machine learning, and data intelligence are better equipped to unlock the future of mobility.

Jeff Mills, Chief Revenue Officer at iMerit

New Technology Era

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