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 These maps were quickly outdated and often inaccurate, relying on manual survey data.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)
Digital maps and PNDs
As consumer tech advanced, digital pioneers such as MapQuest.com and portal navigation devices like TomTom delivered turn-by-turn directions to our destinations.
U.S. traffic deaths (2010)
In-dash navigation and driver assistance
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)
Scalable feature extraction
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.
Specified autonomous routes
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)
Despite advances in technology, the number of traffic deaths in recent years has increased. This could be 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 reduce the number of accidents caused not only by human error but also by neglected vehicle maintenance and congestion, as traditional vehicle ownership shifts toward ride-sharing models using a managed fleet.
Fully autonomous vehicles will use both on-vehicle sensors and HD maps for situational awareness and navigation.