Navigate the advancements in geospatial technology and AI platforms that are making better maps—and driver assistance capabilities required for future autonomy—possible.
Before digital maps, we used paper maps to plan and navigate trips. These maps were quickly outdated and often inaccurate, relying on manual survey data.
U.S. TRAFFIC DEATHS (1980)
As consumer tech advanced, digital pioneers such as MapQuest.com and portal navigation devices like TomTom to deliver turn-by-turn directions to our destinations.
U.S. TRAFFIC DEATHS (2010)
An increase in imaging satellites and improved resolution, combined with advances in routing and geocoding, enabled in-dash navigation technology with new safety features like lane positioning and blind spot object detection.
U.S. TRAFFIC DEATHS (2017)
Thanks to drastic improvements in compute power and image processing, Maxar can produce HD imagery at scale for HD maps—precision data required for machine learning and AI algorithms to extract features at scale with unprecedented accuracy.
Maxar's commercial satellite imaging constellation leads the industry in accuracy and resolution.
Autonomous vehicle testing is already underway for repeatable routes, like shuttle services and small distance ride-sharing. This phase will be critical for proof of concept, informing future policy and public safety regulations.
U.S. TRAFFIC DEATHS (2018)
As you can see, the number of traffic deaths has increased due to a combination of reasons: more cars on the road, distracted or drowsy driving, vehicle malfunctions, etc.
The goal of autonomous transportation is to eliminate the risk of traffic deaths. Many visionaries hope it will not only reduce the number of accidents caused by human error but also neglected vehicle maintenance and congestion, as traditional vehicle ownership shifts to ride-sharing models using a managed fleet.
Fully autonomous vehicles will use both on-vehicle sensors and HD maps for situational awareness and navigation.