Texas Startup Edgetensor Develops AI-based Driver Monitoring System for Autonomous Vehicles

#News(Startup) [ via IoTForIndiaGroup ]

Edgetensor, a new Dallas, Texas-based startup, developed a unique AI-based driver monitoring systems for self-driving cars. The company is addressing the problems of distracted driving, which can be more dangerous if it occurs in a level-3 self-driving vehicle.

A level-3 autonomous vehicle is one that still requires a driver to intervene and take over control of the vehicle in certain situations. Edgetensor is looking to edge-computing as a solution.

“For consumers, they want protection from fatal crashes. According to the NHTSA, 41 percent of all crashes are a caused by a distracted driving. We started looking into edge-computing as a solution.” said Rajesh Narasimha, CEO and co-founder of Edgetensor.

The company’s driver monitor system is designed to run robust AI processing software on low-cost, commodity hardware—using any camera.

Edgetensor offers a driver monitoring system using an AI inference engine optimized for both low power devices, which delivers a high-accuracy cost effective way to monitor passengers in real-time. The system offers face tracking, head posture detection, eye and mouth tracking, and gaze and iris tracking, to make sure a person is always paying attention. The system can even identify your mood based on your facial gestures.

“We were very interested in doing artificial intelligence on the edge, basically edge computing is an upcoming field” Narasimha said. “Most of the artificial intelligence today…the big players like Google, Microsoft and Amazon perform AI inference on the cloud. The problem with doing processing on the cloud is that you have speed and latency issues, and many companies to not want to their IP and private data stored on these cloud servers.”

“Along with speed and latency issues, accuracy under different conditions is also a concern which to a certain degree have been alleviated by deep learning based AI algorithms” added Soumitry Jagadev Ray, CTO and co-founder of EdgeTensor.

Ray stresses that these algorithms were purposely designed to run on low-power devices.

“Our algorithms have been particularly designed and optimized to run on low to medium power devices with ARM or Intel architectures.” he said.

Edgetensor is aiming to solve these pertinent issues by bringing to AI inference to the edge, in this case, inside a moving vehicle—in real-time.

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