STV (847630)

  https://cordis.europa.eu/project/id/847630

  Horizon 2020 (2014-2020)

  Seeing the View

  SME instrument (EIC-SMEInst-2018-2020)

  autonomous vehicles  ·  computer vision  ·  radar  ·  sensors  ·  computational intelligence

  2019-03-01 Start Date (YY-MM-DD)

  2021-02-28 End Date (YY-MM-DD)

  € 3,465,625 Total Cost

  Description

The automotive industry is amid a disruptive change highlighted by the entry of autonomous vehicles. However, at current stage, self-driving cars technologies are not safe enough for operation on public roads. They suffer from too many missed detections and high false alarm rates. Some autonomous vehicle developers have tried to overcome these problems by putting higher resolution (and higher cost) sensors, yet they solutions still these suffer from inadequate perception. There is a growing market consensus that the limitations of the current perception solutions (called ‘Environmental Models’) are entrenched in their ‘Object level’ fusion architecture. This cannot be fixed by tweaking the algorithms, changing parameters or adding more data for learning. A promising alternative solution is ‘Raw data fusion’ with roots in academia and now diffusing to commercial projects. VAYAVISION “Seeing the View” project is based on ‘Raw Data Fusion’ architecture with up-sample techniques to further increase the effective resolution of sparse measurements from active sensors (LiDARs and RADARs). The solution constructs an accurate RGBd 3D model based even on low cost sensors while enabling the perception algorithms richer data and a more comprehensive view of the environment. Using Machine Vision algorithms and Deep Neural Networks, VAYAVISION detects very small obstacles (such as a 10cm high box) and has much better detection rates and with less false alarms than the legacy ‘Object Fusion’ solutions. VAYAVISION’s raw data fusion platform is planned to enable a much safer and comfortable driving experience at an affordable vehicle price. VAYAVISION solves the heart of autonomous driving challenge of correctly understanding the changing environment of the vehicle by using ‘Raw Data Fusion’ and Up-sampling.

  Complicit Organisations

1 Israeli organisation participates in STV.