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ViSS - Preventing the accident around the corner. A disruptive alerting technology for any road user and autonomous vehicle through C-V2X communication. ViSS technology would significantly reduce accidents involving any road user.
The prevalence of road accidents on a global scale has reached pandemic proportions. These incidents result in not only the loss of human lives and injuries but also generate exorbitant costs to the world economy, amounting to around 2 trillion dollars annually. It is, therefore, imperative to reduce the occurrence of road accidents. The advancement of technology has provided various solutions in this regard. However, it has certain limitations. It lacks collision warning in areas where there is no line of sight, which can potentially prevent accidents. Furthermore, the technology currently in use fails to provide road users with ample warning in advance of an impending collision. Therefore, there is a need to further develop existing technology to address these shortcomings and ensure road safety. By addressing the aforementioned issues, the ViSS solution supplements the current solutions. It also paves the way for widespread autonomous vehicle integration into urban centers.
ViSS offers SW that runs on RSUs located in junctions and critical road points. It estimates the behavior of a road user a long time in advance of possible upcoming collision. ViSS solution constantly tracks road users in the RSU environment utilizing sensor fusion (RDR, Lidar, and Camera). The resulting measurements are fed into a tracker algorithm, containing a deep learning aided Kalman Filter, that tracks every road entity in the RSU environment. Then a collision detector algorithm generates a warning, that is sent to ViSS road users via C-V2X. The warnings include the probability of an accident, in a continuous manner with high P detection and low P false alarm. On the receiving side, the solution can either be a cellular application, a simple HW device, or vehicle's electronics. Furthermore, the RSU information will be fed into a cloud server and will be used to optimize the identification and tracking technologies to generate maps of dangerous locations in space and time domains.
