Objectives of the service
In transport and logistics, data analytics is increasingly relied on for making critical decisions. From today’s connected vehicles – fleet managers expect to receive accurate and continuous data. But how do they identify whether telematic data flow has suffered an adversarial cyber-attack? or the vehicle’s sensors are malfunctioning?
This consortium is developing AutoTrust - a cloud-based data analytics tool that has been designed to check the data quality from any connected vehicle and provide an assessment as to whether there has been cyber-attack interference or whether some sensors are reporting inaccurate information. AutoTrust uses the union of continuous monitoring enabled by the integrated 5G/4G and SatCom connectivity and powerful Artificial Intelligence (AI) to deliver a confidence rating on vehicle dataflow.
Users and their needs
Tyre Pressure Monitoring System (TPMS) are used widely in the automotive industry. They include Temperature and Pressure sensors tracking tyres conditions on roads. These sensors are installed by large fleets to identify underperforming tyres. Once an alert is triggered, fleet managers can schedule corrective or preventative maintenance. This can reduce vehicle downtime leading to an efficiently operating fleet.
TPMS sensors operate in a harsh environment and can therefore malfunction leading to a drop in data quality. Fleet management platforms can also experience interference in the form of adversarial attacks. The project aims to address these issues by providing data quality metrics to fleet managers for more insightful decision making. The initial target market are fleets operating Heavy Goods Vehicles (HGVs) in Europe.
Service/ system concept
The key challenges:
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The data encoding: The selection of the encoding strategy will influence the data compression ratio. The aim of the project is to keep the cost of data transfer through the satellite network as low as possible for commercial viabilty.
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The cyberattack detection: The module should accurately detect maliciously manupilated data packets where temperature and pressure readings are critical. Inaccurate flagging risks the value proposition of the project.
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The data confidence analytics: The module provides a single data confidence rating. This needs to be an explainable and interpretable metric to provide useful insights to fleet managers.
The system comprises of several modules that are handing the sensor data ingest, transfer, processing and storage. The core subcomponents of the AutoTrust system are:
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Adversarial cyberattack detection
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Data confidence analytics
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Active Switching Protocol
The analytic modules process the tyre temperature, pressure and GPS trace to output the cybersecurity alert flag and the data confidence rating. These outputs provide guidance to the embedded active switching protocol to transfer to the optimum data flow channel. The results of the analytics are displayed on the platform frontend to the user for informed decision making. The user can also rely on a steadier stream of data utilizing both terrestrial and satellite assets.
Current Status
The project began in October 2021 and completed in February 2025. Commercial launch is planned during 2025. The AutoTrust has 3 core components and is modular. Pilot trials, whist concluded for the project are ongoing during the pre-commercialisation phase. The channels to market are direct to RL Automotive fleets and via third party telematics companies and vehicle manufacturer platforms. Interest has been shown in the complete product as well as in the underlying core components.
Prime Contractor(s)
Subcontractor(s)
