Carnegie Mellon University
August 18, 2020

Cutting vehicle emissions and inspections via IoT

A new system using remote data transfers and machine learning could cut vehicle emissions, lower testing costs, and drastically reduce the need for in-person emissions testing.

In an attempt to eliminate unnecessary costs and improve the effectiveness of I/M programs, EPP Ph.D. student Prithvi Acharya and his advisor, Scott Matthews, with the help of Paul Fischbeck, published their recent study in IEEE Transactions on Intelligent Transportation Systems.

This system uses data directly from the vehicle and sends it to a cloud server managed by the state or county within which the driver lives, eliminating the need for them to come in for regular inspections. The data would then be run through machine learning algorithms that identify trends in the data and codes prevalent among over-emitting vehicles. This means that most drivers would never need to report to an inspection site unless their vehicle’s data indicates that it’s likely over-emitting, at which point they could be contacted to come in for further inspection and maintenance.

Not only has the team’s work shown that a significant amount of time and cost could be saved through smarter emissions inspecting programs, but their study has also shown how these methods are more effective. Their model for identifying vehicles likely to be over-emitting was 24 percent more accurate than current OBD systems.

To read more about their work, go here.