Foresight Autonomous Holdings has completed the development of a demo version of its new Eye-Net accident prevention system, a vehicle-to-everything (V2X) cellular-based accident prevention solution.

Eye-Net is developed to provide real-time pre-collision alerts to the vehicles and pedestrians by utilising smart phones.

As it uses cellular-based technology, the solution does not require installation of a dedicated hardware system.

The company also carried out multiple successful demonstrations of the new system.

Foresight CEO Haim Siboni said: “Cellular phones are available to every person and driver, and our vision is to use them to reduce the number of traffic accidents that occur in urban environments.

“Our system, designed for use on Android and iOS-based mobile phones, has shown impressive results and we intend to continue the development process and carry out a large-scale urban trial later on.

“Cellular phones are available to every person and driver, and our vision is to use them to reduce the number of traffic accidents that occur in urban environments.”

“The system is designed for integration, cooperation and collaboration with major technology providers, cellular network operators and device manufacturers, in order to achieve extensive exposure to lifesaving technology.”

The Eye-Net system aims to provide an additional layer of protection, as well as extend protection to road users who are outside the direct line of sight and are not covered by conventional alerting systems.

During the demonstrations, the system was stated to notify successfully to the user under different situations that included two vehicles moving towards each other at a 90° angle, and a scenario involving a pedestrian jumping in front of an oncoming vehicle.

Foresight intends to complete the development of the system’s alpha version by the end of this quarter and then carry out a multi-user trial, under which several vehicles will use the system simultaneously.

The trial will enable the company to assess the efficiency of Eye-Net in different real-time scenarios.