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A DECADE AFTER DARPA: ARGO AIíS VIEW ON THE STATE OF THE ART IN SELF-DRIVING CARS
by Staff Report


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A decade ago, the 2007 DARPA Urban Challenge unofficially kicked off todayís self-driving technology initiatives, and highlighted the need for more advanced computational power and algorithm development. Many Argo AI team members have been in the field of robotics and self-driving cars for well over a decade, and as we now work to bring this technology to the masses, we are leveraging our extensive expertise including our learnings from the DARPA Urban Challenge.

A decade ago, the 2007 DARPA Urban Challenge unofficially kicked off todayís self-driving technology initiatives, and highlighted the need for more advanced computational power and algorithm development. Many Argo AI team members have been in the field of robotics and self-driving cars for well over a decade, and as we now work to bring this technology to the masses, we are leveraging our extensive expertise including our learnings from the DARPA Urban Challenge.

Just a few months shy of its first birthday, the Argo AI team has grown to nearly 200 employees and now has test vehicles on the road in Pittsburgh and Southeast Michigan.

We know firsthand the challenges that come with commercializing the software and hardware that fuel highly automated and intelligent systems. Working in outdoor conditions among vehicle traffic, pedestrians and cyclists operating without strict adherence to a set of rules can be tricky. The effects of real-world conditions like night and day, changing weather, different road geometries and materials can compound things. The dynamics of the environment bring inconsistencies and variability to what robotic system builders have traditionally needed to simplify into a basic set of assumptions.

In the past few years, the game has changed due in part to the computational power now available, but with this has come a new set of complexities we are still learning to manage. Many advancements in processing power, storage and artificial intelligence are coming together so that these computers can reason through problems without requiring a script. They will be able to learn from massive amounts of data, to recognize patterns with astonishing accuracy and to filter out anomalous inputs from sensors to focus on what matters the most.
As we embrace these advancements, we do so knowing that no single tool, technique or algorithm alone will categorically solve all of the self-driving challenges. Here is our take on some considerations to thoughtfully build a self-driving car.
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