The Internet of Things revolution is powered by a combination of connectivity, sensing and intelligence, enabling formerly dumb “things” such as vehicles to understand and respond to their environment, driving improvements in efficiency and safety.
The widespread adoption of connectivity, sensing and intelligence in millions of vehicles not only helps to make them individually safer and more efficient, but it also has the secondary effect of generating huge volumes of connected-car datasets, providing insight on road and road-adjacent situations wherever connected cars are driven.
In the last few years, automakers have become wise to this largely untapped opportunity, and a range of companies that offer data ingestion platforms and marketplaces have emerged, including Otonomo, Here and Wejo. These platforms support automakers by normalizing datasets among brands, enriching them with location context and facilitating access to a wide range of customers — typically road-focused or road-adjacent companies with problems that can be solved through access to the insights that connected vehicles can provide.
Without a doubt, connected cars are among the best-positioned IoT devices to provide insights into what is happening in the world. Active safety and autonomous driving are spurring the adoption of camera sensors, which can give rich, semantic insight into each connected car’s environment.
Vehicles are also being equipped with more powerful embedded compute, not only in the advanced driver-assistance systems domain, but also to deliver richer infotainment experiences, providing the necessary headroom to process sensor data and extract insights in field. Unlike a static smart sensor, such as a traffic camera, connected cars roam all over the globe — with each individual vehicle potentially offering insight into situations unfolding over many miles of road.
Furthermore, connected cars are expected to be among the earliest adopters of 5G in the IoT, as automakers, conscious of the lengthy lifetimes of their models and the possibility of network sunsets, seek to future-proof their connected-car designs.
However, as well positioned as automakers are to harvest a new, data-monetization revenue stream, passenger vehicles do have one major weakness — poor utilization. As I wrote this article, my car was sitting idle outside — it was not generating insights of use to anybody. The same is likely true of your car right now. Passenger-vehicle utilization rates (the amount of time a vehicle is in use) are infamously low — typically around 5 percent or eight to nine hours per week.
Conversely, smart mobility and transit vehicles, which are operated in fleets, have much higher utilization rates. A city bus can easily be in operation for eight to 10 hours per day. A typical Uber driver can be on the move for seven hours a day, with caps in place to enforce rest periods after shifts of 10 or 12 hours, and even a typical e-scooter will see more use in a week than most passenger cars.
Therefore, fleet-based smart mobility and transit vehicles are even better positioned than passenger vehicles to provide crowdsourced insights — particularly in urban contexts, where mobility and transit fleets are concentrated, as well as many of the problems that these insights can solve.
While fleet operators don’t always have access to data generated by embedded sensors in their vehicles, they are typically equipped with aftermarket telematics and advanced driver-assistance devices, which can be configured to collect custom datasets.
For example, Mobileye’s Connect 8 aftermarket advanced driver-assistance device is used in thousands of fleet-based vehicles, including ride-hailing vehicles, rental cars and buses. Through its REM mapping platform, Mobileye can provide customized insights to city authorities and local governments on the location of numerous landmarks and traffic events. Through over-the-air updates, Mobileye can modify its machine-vision algorithms to change the assets whose locations and states are aggregated, according to the application being targeted.
In the U.K., Mobileye has partnered with Ordnance Survey to determine the location of streetside assets such as manhole covers and drainage grates to avoid utility strikes during construction and roadwork. This is a good example of how data from fleet-based assets can be used to solve major city-centric problems.
Another major problem facing cities is congestion and the resulting emissions. From 2019 to 2021, Transport for London partnered with Bosch and Here to assess the impact of traffic flow on air quality, combining Bosch’s air-quality monitoring and modeling with a real-time model of driving behavior and traffic dynamics built on connected vehicle and device data ingested by Here’s location platform.
Overall, Transport for London was able to achieve a 20 percent reduction in nitrogen oxide exposure in the borough of Lambeth, while also deepening its understanding of the relationship among traffic flow, weather patterns and NOx dispersion.
As a simple byproduct of their core operation, smart mobility fleet assets regularly generate insights on road and road-adjacent events that are valuable to a wide array of potential customers, including city governments, road operators and retail chains, to help solve issues ranging from hyperlocal weather information to life-saving emission reductions.
With smart mobility operations still plagued by unprofitability, fleet operators would do well to imitate automakers in taking advantage of the data-monetization opportunity available to them.