IoT services for road transport vehicles including aftermarket telematics units, connected cars and autonomous driving will be a 60 Billion industry by 2020, bringing to the roads approximately 70 Millions of connected vehicles. Moreover, the amount of devices will be exponentially growing until 2040. As mentioned earlier, data-driven services related to these segments are prone to potentially send more than 3600 Million GB per day, or what is the same, around 1300 Billion GB per year.
This context poses the right premises to sell compression technologies that help dealing with this vast amount of data. Several vertical solutions have appeared over the years to solve particular problems such as codecs and intelligent algorithms for compressing text (GZIP), images (JPEG) videos (QuickTime, Alpary) or music (MP3). However, in the connected vehicle domain, the information is extremely heterogeneous since is generated by a wide range of sensors capturing completely different physical magnitudes: acceleration, angular rotation, temperature, velocity, time, GPS coordinates, object distances, etc… Therefore, the sector is requesting a transversal and generic solution able to automatically adapt to each type of signal constraints in order to unlock data-driven services. However current solutions that aim at solving this problem have several drawbacks:
- Filtering techniques require high CPU usage, which makes them not suitable for embedded systems mounted within a car. Moreover, unforeseen events, represented by frequencies that are cut off by the filter, cannot be reproduced, so that relevant information might be lost and thus, the accuracy highly decrease
- Subsampling techniques are not versatile enough since they offer only 2 options: 1) Have a low variation on signal reconstruction but at low compression rates or 2) Have high error on the reconstruction but achieving high compression rates.
- Generic compression techniques (e.g. zlib, glib) are intended to solve only specific data-problems (text, image, etc…), requires high CPU usage and obtains limited compression rates and accuracy when applied to “generic” signals.
However, TERAKI is the answer to all this limitations, since it is a versatile solution capable of compressing any kind of vehicle sensor data with high performance.
Only considering the amount of data to be sent by connected cars in 2020 on our targeted segments, we are targeting a potential business opportunity of more than € 19 Billion in 2020. During the phase 1, we have defined a hybrid business model that will allow us to capture a significant share of this huge market, in short:
1. Virtual telecommunication operator (MVNE) In this scenario, we will act as the operator responsible of the whole process of data gathering, storage and reconstruction in our external servers (including the SIM card provision). We forecast that we will offer a total pricing 30% lower than current offerings.
2. Licensing, we will charge monthly fees to licence our technology to industry customers.
Through these hybrid models, we will sell our services to the most relevant players on the sector. We expect that this will allow us to generate a yearly revenue of €32M already in 2022, generating 60 new jobs in Europe, becoming the intermediate telecom operator for the connected vehicle industry.
TERAKI will unlock new data-driven services associated to the connected car. Leaving apart, the economical outcomes, TERAKI will have high impact on EU society. Just to provide some examples: collision avoidance systems could reduce fatalities by 30% in Europe, with a further 30% reduction in serious injuries. It is clear that if 2 connected cars were communicating to each other in real-time, they could avoid a frontal collision much in advance we can even notice it. From the other side, real-time vehicle location data combined with simulation models to build predictive traffic information has the potential to reduce congestion by 15%, which is extremely relevant considering that it costs EU nearly €100 billion annually.
