DENSO has been researching new developments in the area of computer vision processing. We strongly believe that THINCI’s technology will soon become a key component of next generation autonomous driving systems that require advanced computing techniques combined with deep learning capabilities. DENSO looks forward to supporting THINCI’s technology development and working with our OEM customers to deploy this technology into the automotive industry for autonomous drive.

—Tony Cannestra, DENSO International America’s Director of Corporate Ventures

THINCI Inc. is a venture-backed, deep-learning vision processing start-up based in El Dorado Hills, Calif., with teams in California and Hyderabad, India. THINCI’s technology can be integrated into a wide range of applications, including advanced driver assistance systems in automobiles, personal electronics and smart home automation systems.

The distinction that THINCI brings to the solutions currently coming onto the market is in the technique used to process the data. Deep learning and vision processing involves processing images, analogous to human vision. The image can be a picture or a large data base of “likes” collected on social media, collective purchases made on-line, fingerprints, etc.

The human, for example, sees an image or collection of data and intuits a pattern—a person, place, or thing for the image; the movement of a stock or what’s trending on social media. THINCI says that its deep learning and vision processing machine replicates this human function.

One approach to this processing is to apply graphics processors to compute image elements rather than render them as they do for video games. The approach THINCI has pioneered is to process the entire image in parallel, detecting a pattern the way the brain processes the image detected by the eye. This approach has the benefit of reducing the high frequency interaction with memory of graphics processors, with the benefit of reducing power consumption and boosting performance by eliminating the overhead of memory accesses.

Automotive applications for the technology include driver state analysis; road condition analysis; vehicle localization; environment perception; and path planning.

For the engine and drive train, THINCI deep learning and vision processing technology could continuously monitor and adjust the operation and performance of the vehicle to the driver, detecting the need to switch from 2-wheel to 4-wheel drive, adjust the engine for changes in altitude, temperature, and humidity, and also detect the passenger load and adjust the suspension to accommodate the load.