Honda Invests in Software Startup to Strengthen its Computer Vision and AI Development

Honda Invests in Software Startup to Strengthen its Computer Vision and AI Development

Author: Eric Walz   

Japan's Honda Motor Co. has made an investment in Silicon Valley-based software company in order to strengthen its software development in the fields of computer vision and artificial intelligence (AI), the automaker announced.

Computer vision and AI-powered software technologies are used for vehicle perception systems for autonomous driving and advanced driver assist systems (ADAS) to identify objects, as well as predict their movements.'s core strengths are in the areas of advancing AI-based image recognition technologies through unsupervised learning.

The investment was made in December, but Honda did not make the announcement until Jan 20. The amount of the investment was not disclosed. 

The partnership between the two companies is not new. Honda and have been collaborating since 2019 through "Honda Xcelerator", a global open innovation program of Honda that supports startups working on innovative technology. Xcelerator is headquartered in Silicon Valley with satellite offices around the world.

The financing from Honda will further strengthen the relationship between the two companies and accelerate development of technology solutions that combine Honda technologies with the AI technologies of, which was founded in 2016, is addressing the painstaking task of labeling data manually, which is then used to create datasets to train machine learning models. The company's approach is known as "unsupervised learning", which can make AI more accessible to thousands more companies. It allows learning on massive datasets that otherwise would be difficult and time consuming to do manually.

These datasets are used to train machine learning models across different fields of AI so software can learn to identify objects. For autonomous driving applications, the data is used by engineers to train autonomous driving systems to better perceive the environment. However, training machine learning models requires vast amounts of "training data" before they are accurate enough to deploy autonomous driving software in vehicles.

For example, a dataset of road signs might be used to train AI models to recognize them. Stanford University even has a dataset with pictures of dogs, which can be used to train AI programs to identify different breeds accurately. 

With unsupervised learning, machine learning models can identify patterns and reach conclusions as to what they are, with little to no human intervention. The technology makes them much cheaper and faster to build, according to

Vlad Voroninski, co-founder and CEO of, is using unsupervised learning to build next-generation software for driverless cars. 

For autonomous driving tasks, these training datasets might contain videos of street scenes captured from a self-driving vehicle's real-world environment, such as a busy urban intersection filled with vehicles and pedestrians.

The training data is crunched using machine learning algorithms, so the software can better detect objects in each camera image, including other vehicles and pedestrians, as well as predict their intended trajectory, which helps a self-driving car to safely navigate.

Accurate detection and tracking of objects is crucial for the deployment of autonomous vehicle technology with the highest levels of safety. As a result, there is growing demand for high-quality datasets that are already labeled and ready for training machine learning algorithms.'s software won awards for the most innovative use of AI for autonomous vehicles at Tech.AD Detroit in Nov 2019. The company won the "Most Innovative Use of Artificial Intelligence & Machine Learning in the Development of Autonomous Vehicles & Respective Technologies", as well as the "Overall Community Choice Award". was also selected as the "Best Automotive Startup Using AI" at Automobility LA in 2017. was also a member of the NVIDIA Inception Program, which is designed to help startups working on advances in AI and data science. The program provides members with access to cutting-edge technology and support from NVIDIA experts.'s business model includes licensing its AI software for autonomous driving applications to OEMs and Tier-1 developers. The scalable software addresses the technical challenges of perception, prediction, fusion, mapping and path planning and motion control, so autonomous vehicles can navigate safely.

In Nov 2021, announced that it raised $26 million in series B financing led by Amplo, JMPartners, Base Capital Funding, and Freeman Group. 

Eric Walz
Eric Walz
Originally hailing from New Jersey, Eric is a automotive & technology reporter covering the high-tech industry here in Silicon Valley. He has over 15 years of automotive experience and a bachelors degree in computer science. These skills, combined with technical writing and news reporting, allows him to fully understand and identify new and innovative technologies in the auto industry and beyond. He has worked at Uber on self-driving cars and as a technical writer, helping people to understand and work with technology.
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