Israel-Based VAYAVISION Raises $8 Million In Funding To Build On Its Autonomous Vehicle Technology

By Annie Baker ● October 18, 2018

VAYAVISION is a Tel Aviv, Israel-based provider of raw data fusion and perception for autonomous vehicles that announced it has raised $8 million in seed funding this week. This seed round of funding was led by Viola Ventures, Mizmaa Ventures, and OurCrowd. And strategic investment was provided by Mitsubishi UFJ Capital Co., ltd. (MUCAP) and LG Electronics.

And VAYAVISION’s advanced architecture sets up a precise 3D perception of a vehicle’s environment using a fusion of raw data from multiple sensors such as LiDAR, RADAR, and cameras. And its technology also has integration of deep understanding of the data through machine vision algorithms and deep neural networks to provide better cognition, which is essential for autonomous vehicles. VAYAVISION’s solutions also enhance low-resolution LiDAR and RADAR to HD-like quality.

“Consumers are seeking the safest and most comfortable autonomous vehicle experience – and manufacturers are looking out for their bottom lines,” said VAYAVISION co-founder and CEO Ronny Cohen. “In the race to full autonomy, VAYAVISION’s technology enables AVs to have less missed detections and lower rates of false alarms, even when paired with low-cost sensors, allowing OEMs using our system to save money on those key components.”

VAYAVISION plans to use the funding to fuel its growth and focus on developing customer engagement. And the company is going to market and build partnerships with leading OEMs and Tier 1 suppliers around the world.

“Most current generation autonomous driving solutions are based on ‘Object Fusion’ architecture, in which each sensor registers an independent object, and then must reconcile which data is correct,” added VAYAVISION co-founder and CTO Youval Nehmadi. “This provides inaccurate detections and results in a high rate of false alarms. The industry has recognized that to reach the required levels of safety, more advanced perception paradigms are needed – such as raw data fusion.”