Autonomous driving startup raises $31M in new funding

Autonomous driving startup raises $31M in new funding

Posted on Inc., the developer of a software platform that automakers can use to power self-driving vehicles, today announced that it has closed a $31 million funding round.

The Series C round was led by Freeman Group. Honda Motor Co., ACVC Partners and Amplo participated as well, along with publicly traded auto parts makers Goodyear Tire & Rubber Co. and Sungwoo Hitech Co. Ltd. has raised a total of $78 million from investors to date. 

Menlo Park, California-based offers a software platform that enables automakers to build self-driving vehicles. According to the startup, customers can use its platform to implement Level 4 autonomy. A vehicle with Level 4 autonomy is capable of driving without human input in most situations. says that its platform also supports other use cases. It can be used to power robots as well as advanced driver-assistance systems, which enable cars to automate a limited number of driving tasks.

Autonomous driving systems use artificial intelligence to make navigation decisions. According to, its platform uses a different AI approach than many competing offerings on the  market. The result, according to the startup, is that its software can operate with a high degree of reliability and reduce the costs associated with building an autonomous vehicle.

Automakers hone the reliability of the AI algorithms that power their autonomous vehicles by training them on large amounts of road data. Usually, that road data can’t be processed in its original form. Before processing may begin, engineers must add explanatory labels that help the AI being trained understand the information it’s processing. 

Adding explanatory labels to training data involves a significant amount of time and effort. The more training data is used, the more manual work the task requires. As a result, automakers have to limit the amount of training data they use to train their autonomous vehicles’ AI algorithms, which decreases software reliability. says that its platform addresses the challenge. The startup’s platform is based on an AI approach known as unsupervised learning that removes the need to add explanatory labels to training data. As a result, automakers can train their vehicles’ autonomous driving algorithms on more road data than would otherwise be practical.

Increasing the amount of data used to train an AI increases its accuracy. The result, according to, its that autonomous vehicles powered by its software can make navigation decisions with a high degree of reliability. 

According to the startup, a second benefit of its approach is that it lowers costs. By removing the need to add explanatory labels to training datasets,’s platform reduces the expenses associated with deploying autonomous driving software. The startup claims its technology is up to several orders of magnitude more cost-efficient than competing approaches.

“Our Deep Teaching technology allows us to quickly deliver best-in-class AI software to OEMs and Tier 1s in a hardware-agnostic fashion, accelerating their time to market and enabling their path to software differentiation with high-end ADAS and L4 systems,” said Chief Executive Officer Vlad Voroninski. will use the proceeds from its latest funding round to support research and development. The startup will also invest in a portion of the capital to advance its commercialization efforts. 


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