Silicon Valley Startup Ghost Secures $100 Million in Funding for its Breakthrough Autonomous Driving & Crash Prevention Tech
Silicon Valley-based autonomous driving startup Ghost recently announced the closing of a $100 million Series D financing for an autonomous driving system that the company says offers a revolutionary breakthrough in crash prevention.
Participating in the latest investment round were returning investors Sutter Hill Ventures and Founders Fund, in addition to a new commitment from investment management firm Coatue.
Ghost was founded in 2017 by John Hayes and Volkmar Uhlig and is based in Mountain View, CA. Before co-founding Ghost, Hayes founded data storage company Pure Storage, taking the company public in 2015. Haynes now serves as Ghost's CEO.
Ghost originally set out to build an autonomous driving hardware kit that uses radar, ultrasonic sensors and a suite of cameras that consumers can add to their vehicles to turn it into one capable of self-driving on highways.
But now the company has shifted its focus and plans to supply its autonomous driving technology to automakers. Demand for robust and reliable autonomous driving technology is growing as more vehicles come equipped with autonomous driving capabilities.
"The market for ADAS features is big and growing fast, including all the new safety and semi-autonomous L2 functionality included on new consumer cars," said Hayes.
"At Ghost, we are building a software-centric solution that can safely deliver a L4 autonomous driving experience on the highway, equipped with universal crash prevention technology so that it does not require oversight or last-second intervention from the driver."
Identifying and tracking objects for safe navigation is a challenging task for developers of self-driving vehicles. A significant amount of processing power is needed to identify, then track the movement of objects for safe navigation of an autonomous vehicle.
Unlike many existing autonomous driving systems that rely heavily on classification tasks to identify objects, Ghost says its autonomous driving technology does not need to recognize what an object is in order to avoid it.
From an engineering standpoint, this is far less challenging compared to self-driving vehicle software that continuously tries to identify all objects around the vehicle then uses AI-powered algorithms to predict their movements.
The software powering most autonomous driving systems uses AI-powered machine learning algorithms and deep neural nets to identify and track objects. However, these AI-powered algorithms require a significant amount of compute power.
Instead of using massive compute power for object classification, the Ghost technology relies more on collision avoidance. The result is a leaner crash prevention technology capable of detecting and handling any obstacle, regardless of speed, size, or type, according to Hayes.
Ghost captures a more generalized view of the environment around a vehicle by simply tracking the motion of clusters of pixels in a scene. Its similar to how a human eye works, according to Hayes. Ghost's autonomous driving technology can make faster decisions before classifying anything.
Hayes said this approach is much more reliable for crash prevention, and ensures it can handle any obstacle.
"We skip that (classification) step," Haynes told TechCrunch. "We're going to recognize anything, any mass that appears in the scene, and then we can get a distance and relative velocity to that."
"Using new applications of artificial intelligence and physics, Ghost is designed to detect every potential obstacle without the lapses or errors associated with traditional object recognition and react both faster and harder than a human driver to avoid incoming threats," explained Hayes.
Unlike SAE Level 2 automated driving systems, Ghost says its fully automated crash prevention does not rely on last-second human intervention in order to avoid a collision. It's designed to keep drivers safe in even the most unusual circumstances, which are known in the industry as edge cases.
"Crash prevention is the single most important problem in driving," said Hayes. "Automotive safety innovation has historically focused on alleviating the effects of crashes. Now we finally have the technology to prevent crashes before they occur – the breakthrough needed to bring safe self-driving to our highways."
"In summary, Ghost's universal crash prevention technology is designed to avoid all obstacles – not just ones that it recognizes," Hayes added.
Ghost is currently testing its technology in California. The company will use the new capital to continue development and introduce the product next year.
Ghost recently appointed former NHTSA Chief Counsel and Acting Administrator Jacqueline Glassman as General Counsel. She brings more than 25 years of experience in motor vehicle and consumer safety across both the public and private sectors.
"For decades, I have been involved in helping to reduce the risk of crashes and improve road safety," said Glassman. "I am thrilled to continue that mission at Ghost by helping advance its unique approach to crash prevention, which will ultimately make safe self-driving available on a mass scale. As we bring Ghost's technology to market, we can start saving lives within years, not decades."
Hayes believes that universal crash prevention should be "a cornerstone of every car and foundation of any autonomous system."
To date, Ghost has raised $165 million from investors including Mike Speiser at Sutter Hill Ventures, Keith Rabois at Founders Fund, and Vinod Khosla at Khosla Ventures.
As tech companies and automakers race to commercialize autonomous driving technology, Ghost is one startup to watch.
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