The warning systems seem to be one of the most suitable alternatives for cycling enthusiasts. In Spain, there is a record that about 70 cyclists die annually on the roads. It is also estimated that around 7,000 cyclists experience accidents that leave them injured.
This phenomenon is due precisely to the same routes in which cyclists train. They are the same routes through which cars move. How to make compatible then that these routes continue to serve as an alternative training for cyclists without them being injured?
That is precisely what a new strategy of warning systems based on the so-called IoT seeks. IoT, if you don’t know it, means the Internet of Things. This in Spanish means the Internet of Things. The strategy to be used with the warning systems was jointly developed by DGT, SEAT, Ficosa and, Aeorum in addition to DGT support.
An invention that will transform the history of cycling
The essence of the warning system is in a drone that is responsible for tracking the road. This drone is connected to a mobile network that transmits data in real-time, to be accumulated on a server. The other component of the warning system is in the processing performed by the server to identify road problems.
This third component has been referred to as a MEC. Finally, this MC establishes a relationship with the fourth component, which is the one that transfers the situation and displacement of the cyclists to the cars that are connected to said service. The MEC system is then optimized to guarantee real-time alerts about the situation of cyclists on the road.
The cyclist Pedro Delgado has become the leader of this initiative. It is expected that many drivers will benefit from this strategy, to say goodbye to accidents. It is clear that neither the drivers nor the cyclists themselves expect to produce a mishap that ends in injuries or the death of an athlete.
Constant road analysis
The warning systems then have the fundamental pillar of connectivity. But, at the same time, you have to understand how the drones and the server have an algorithm that works cooperatively to warn cars about the movement of cyclists.
The MEC is a software that has a very advanced level of processing, which essentially operates with artificial vision. In addition to this, this artificial vision system cooperates with what is called machine learning. Thanks to this, the drone acquires a type of artificial intelligence, which evolves as the drone performs its activity in the traces.
The Aeorum company was in charge of working on this type of intelligence for drones. The drone is then optimized to perceive phenomena such as landslides on the same road, animals, car accidents, pedestrians, among other factors. According to Aeorum’s algorithm, when a road is clear, the drone perceives it cleanly.
Cooperation between the server and the drones
The issue is that sometimes you may encounter traffic lights, signs or other road infrastructure issues. With the aerial route carried out by the drone, over time the team creates its own algorithm, taking into account the server processes. And when something does not fit within what both systems perceive as “natural,” the warning systems are then activated.
The developers of this technology say that thanks to it, cars will have a “sixth sense”. The bottom line is that the cars themselves begin to integrate and associate with this system. Something that is very viable considering the way in which today technologies such as 4G (and soon 5G) are used in their mobile phones.
At the moment, the warning systems are backed by what is a TCU equipment. It is an electronic unit, whose acronym translates Telematic Control Unit. The product has been developed by the company Ficosa. And in essence, it is presented as a mobile phone that manages to establish a direct connection with the network on which the server works.
A project that could be enhanced over time
In the long term, to make use of the warning systems it would not be necessary to work with additional sophisticated equipment, but rather the entire processing of the MEC could be projected through applications, or, from GPS systems. That way, cars, and trucks are kept constantly informed.
The system established to contribute to the training of cyclists would have a high impact on those towns and cities in which cyclists tend to train more frequently.