The French Mobility Policy Act (LOM law), adopted last year, prescribed the introduction of dedicated carpool lanes as one of the key measures to meet the ecological challenges of the Low Carbon Highway and relieve congestion on the road network. To support this initiative, Cyclope.ai has developed a video analysis solution that can count the number of occupants in a vehicle travelling on the road.
Thanks to state-of-the-art deep learning technologies and a significant investment in R&D, our solution detects automatically the number of occupants present in the vehicle : for front and back seats, both day and night.Ask for a demo
Lançon-de-Provence, A7 highway
The aim of this experiment is to improve and qualify the performance of high-speed detection models (+90km/h), while identifying the optimal set-up and configuration for dedicated lane monitoring system from the roadside.
To support the R&D and design phase of the Carpooling product, VINCI Autoutes (ASF network) provided Cyclope.ai with an experimentation area for the installation of a solution prototype equipped with camera, in order to improve the system algorithms.
Cyclope.ai has mandated CEREMA to evaluate by this summer its firt device performance directly on the test site.
As part of this experiment, Cyclope.ai is responsible for
the collection and processing of personal data of A7-motorway users:
As part of its R&D phase, Cyclope.ai wants to strenghthen its algorithms in order to have a product that delivers a very high level of quality. The Carpooling solution must meet the needs of road infrastructure operators and other mobility mangement authorities for a better control of carpool lanes uses.
The first step for the proper development of an efficient monitoring device is to ensure qualitative data collection: the quality of the image that is going to be processed by the algorithm has a key impact on the final overall system performance.Contcat us
Our product meets all the functionalities of a control device, while respecting the need to optimise interventions during operations: compact device, protected equipments, easy to install, accurate site-specific fine tuning to adapt to each specific set-up, etc.Learn more
Thanks to deep learning technologies, our algorithms are constantly improving. Indeed, the more data they “see”, the more accurate the models are, by learning how to recognize previously undetected cases.Learn more
In order to evaluate the performance on each site in an accurate way, Cyclope.ai provides the operators with a dedicated tool, that can analyze and qualify all our AI models. Once the required level of performance has been reached, the Carpooling algorithm can then be safely deployed at scale, direcly on the network.Contact us
The French Mobility Policy Act (2019), amended the existing legislative framework for bus and taxi lanes, allowing several MOBILITY MANAGEMENT public AUTHORITIES (regions, metropolises and local communities) to extend these lanes to vehicles occupied by at least 2 persons (VR2+)Contact us