Objective
When we drive, our safety is protected by a set of technologies that silently watch over the car’s behaviour, intervening to
minimise the risk of accidents. The Electronic Stability Control (ESC) is by far the most impactful safety technology in cars,
having reduced by around 40% the number of fatal accidents caused by the vehicle’s loss of control. Although effective, any
ESC on the market suffer from one significant flaw: it cannot directly measure the sideslip angle, which is the key indicator of
skidding, namely the situation when the car deviates from the driver’s intended direction. The result is that present ESC can
detect up to 80% of skidding events, thus still leaving room for improvements that can save lives. To address this issue and
catch a huge market opportunity, Modelway has developed a machine learning technology able to accurately estimate the
vehicle’s sideslip angle in real time. And without adding any new sensor to the car. The key to obtain this result is the
proprietary and patented Direct Virtual Sensor technology, which can be embedded in standard ESC units to further improve
the vehicle’s capacity to detect a skidding event. The DVS technology has been prototyped and extensive tests have been
carried out with car manufacturers and their Tier-1 suppliers, showing that the performances are already in line with the
expectations of a highly regulated industry as automotive. Now the development roadmap focuses on understanding the
feasibility of the integration of the DVS technology in commercial ESC units (Phase 1), to enable a co-development effort
with global ESC manufacturers (e.g. Bosch, Magneti Marelli) leading to a pre-commercial validation test-bed (Phase 2). In
terms of business potential, with around 100 million cars sold each year globally and around 50 in Europe and the US where
the use of ESC is mandatory since 2014, we target more than 4 million DSV installed in cars by 2025, leading to more than
28 M€ of revenues.
Fields of science
- engineering and technologymechanical engineeringvehicle engineeringautomotive engineeringautonomous vehicles
- natural sciencescomputer and information sciencessoftware
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringcontrol systems
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringsensors
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
Programme(s)
Funding Scheme
SME-1 - SME instrument phase 1Coordinator
10144 TORINO
Italy
The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.