Autonomous Vehicles and Smart Parking Part 1: Data Sources

  The entire sector of Interconnected Mobility is preparing for the arrival of autonomous vehicles. Many different forms of transportation infrastructure are going to need to chance, and one of those is parking.

  Autonomous vehicles have significant advantages over traditional drivers, but they also look like they will have some shortcomings, at least initially. One big advantage is the ability to formally process and synthesize vast streams of real-time data to calculate variables like optimal route and ETA better than a human ever could. Autonomous vehicles can process data from traffic signals, GPS from other vehicles, weather data, and of course parking sensors, to name a few. They can synthesize this data to estimate the accuracy of ETAs, of expected traffic patterns, and optimal route choices.

  What autonomous vehicles don’t yet have is the collective mass of experience of driving to specific routes frequently that their owners will still possess, even if these cars have millions of miles of test driving. They don’t know yet if their riders prefer shorter commutes in sunny weather and longer commuters off freeways when it’s raining. They don’t yet know if their driver prefers parking on the street or in a garage.

  Parking information is one of the many pieces of information an autonomous vehicle must analyze, and it’s an important one. Human drivers who have been going to a location for a long period of time often know their favorite parking spots, but the car will need explicit information to find a place to park. Parking policies and availability are a good place to start, but quality parking information will include average duration of stay and other trends that will let a smart car know if this parking spot works for its owner.

  As smart cars become more and more common, the sources of data that feed their performance will become more and more necessary, and one of those sources is smart parking.