Official UniNa and DII spin-off

Imagine having an infinite number of sensors on your vehicle.

But they have no weight, no volume, and cannot fail.

They are real time algorithms.

They are virtual sensors.

Imagine having an infinite number of sensors on your vehicle.

But they have no weight, no volume, and cannot fail.

They are real time algorithms.

They are virtual sensors.

TECHNOLOGICAL
FRAMEWORK

01 VEHICLE DEVELOPMENT & TESTING

Kymes: a modular data logging platform with virtual sensing capabilities

Multiple integration modes: stand alone, with CAN bus and with external sensors, each with specific features

Remote co-pilot mode: manage in real-time your outdoor test session from your desk

01

02 ONBOARD ALGORITHMS FOR OEMS TIER 1

Tire-centered virtual sensors with 100% physical approach

Tire Health, Aquaplaning, Grip and Wear real-time monitoring

From +10 years of experience in tire real-time simulations, research and motorsport

02

03 THE TIRE AS THE GATEWAY TO CONNECTIVITY

Road monitoring and vehicle/fleet maintenance

Infrastructure for edge/cloud computing

Real-time algorithms born for racing, grown for safety

03

Scientific Roots

Design of a virtual sensing methodology for vehicle ride and comfort applications

Accurate real-time estimation of the instantaneous vehicle state plays a crucial role in modern automotive research, both in the state diagnostics and anomaly detection and in the design and development of advanced control systems and onboard monitoring strategies.

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T.R.I.C.K. 2.0: Enhanced Vehicle Dynamics Analysis and Estimation Harnessing Advanced Vehicle Sensors

Automotive signal processing is dealt with in several contributions that propose various techniques to make the most out of the available data, typically for enhancing safety, comfort, or performance.

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Design of a Machine Learning Approach to Anomaly Detection in Tyre-Road Interaction

Tyres show a strong non-linear dependence on vertical force, road roughness, wear level,
temperature gradient, and slip resulting in an additional challenge in calibration, whose parameters may vary significantly with the tyre’s condition.

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Adaptive vehicle dynamics state estimator for onboard automotive applications and performance analysis

This work describes the development of a vehicle state estimator, designed to be employed onboard in real-time, based on the data acquisitions already available within the vehicle CAN bus infrastructure.

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Cross-combined UKF for vehicle sideslip angle estimation with a modified Dugoff tire model: design and experimental results

The knowledge of key vehicle states is crucial to guarantee adequate safety levels for modern passenger cars, for which active safety control systems are lifesavers.

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T.R.I.C.K.‐Tire/Road Interaction Characterization & Knowledge - A tool for the evaluation of tire and vehicle performances in outdoor test sessions

The most powerful engine, the most sophisticated aerodynamic devices or the most complex control systems will not improve vehicle performances if the forces exchanged with the road are not optimized by proper employment and knowledge of tires.

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Operating Headquarters:
Dipartimento di Ingegneria Industriale Università degli Studi di Napoli “Federico II”, Via Claudio 21 – 80125 Napoli Incubatore Campania New Steel, Modulo 20, Via Coroglio 57D – 80124 Napoli

Registered office:
Via Annibale Marchese 10 – 80141 Napoli

Email: info@ridesense.eu

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Progetto «RIDESENSE» – CUP B68I23005890007. PR Campania FESR 2021-2027 – ASSE I – Obiettivo Specifico 1.1 Azione 1.1.3 – AVVISO PUBBLICO CAMPANIA STARTUP 2023.

RIDEsense è una tecnologia innovativa basata su virtual sensors, algoritmi avanzati in grado di stimare in tempo reale il coefficiente di aderenza tra pneumatico e strada senza l’uso di sensori fisici aggiuntivi. Il sistema sfrutta i dati provenienti dai sensori già presenti sui veicoli e, attraverso modelli fisico-matematici, fornisce informazioni fondamentali sulla sicurezza e sull’efficienza alla guida.

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