Why Autonomous Vehicles Need A New Purpose-Built Solution

Published on
May 18, 2020
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By Manish Singh, VP of Marketing

The car industry is facing two major evolutions: autonomous vehicles (AV) and electric vehicles (EV). Compared to traditional auto manufacturers, Tesla, who uniquely develops their own purpose-built self-driving platform for their cars, is head of the pack with regards to AV and EV technology. Thus, to be competitive and relevant, companies must integrate an industry leading solution to adapt to this rapidly evolving market.

When driving, our brains subconsciously process a plethora of visual data in real time — just take a look at the figure below to see a depiction of this. So, for AVs to become a reality, they must do the same — quickly and accurately understand their surroundings under any circumstance. However, to do this, AVs need both immense processing capability and great power efficiency to interpret scenes that the camera systems acquire. This entails an enormous performance requirement, revealing the largest barrier to realizing full vehicle autonomy: the visual scene-understanding problem.

Visual Scene Understanding Challenge — a busy intersection
Visual Scene Understanding Challenge — a busy intersection

Current solutions for this are based on repurposing legacy technology; however, retrofitting products built for a different application is not optimal — unique problems need purpose-built solutions. Take the smartphone industry, for example. Mobile application processors, with specific functions for various applications, were designed for smartphones due to their many capabilities. The non optimal solution to this was the general purpose processor — however, this did not meet smartphone uses cases, applications, and power envelope requirements, so an innovative product in the form of a mobile application processor was needed.

Circling back to the automotive industry, because current solutions that serve traditional OEMs are not purpose-built for visual perception, they do not have the processing capability to enable autonomy without compromising battery life. AVs with these solutions today still cannot prevent deadly situations such as head-on crashes or collisions with Vulnerable Road Users. Although Tesla cannot achieve full autonomy today, because their self-driving platform is far ahead of what conventional OEMs are using, companies need a novel solution that solves the visual scene-understanding problem and enables them to scale from partial autonomy today to full autonomy in the future.

We at Recogni® Inc. are developing a purpose-built platform to strike down the visual cognition barrier and facilitate vehicle autonomy. Our unique AI-based solution leverages key innovations in machine learning and mathematics to support over 1000 TOPS while consuming minuscule power. Because our module is designed specifically for visual perception, it can enable AVs to interpret high-resolution, high-frame-rate , camera-based visual cues in real time, which allows them to accurately detect a traffic light from 200 m. Due to these unmatched capabilities, we at Recogni Inc. can solve the visual perception problem and serve as the single, scalable solution for traditional OEMs to use to not only develop driver-assistance solutions and partially autonomous vehicles today, but to produce fully autonomous vehicles in the future.

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