A software module for collecting data from the advanced driver assistance systems of an electric vehicle based on the MEB platform was designed, implemented and verified. The module receives, processes and recalculates data in real time; it also derives additional variables required to analyse vehicle behaviour and ADAS functions.

The open architecture supports the connection of additional sensors that are not part of the vehicle's standard equipment, their time synchronisation with operational data, and experimental verification of their applicability in ADAS.

The processed data are transmitted through a 5G mobile modem to a database hosted on a server within the Brno University of Technology network, where they are stored and made available for subsequent evaluation. At the same time, the data are supplied to the vehicle digital twin in the simulation tool and to the associated virtual test environment. The solution therefore connects the real vehicle, supplementary sensors, data infrastructure and simulation.

It enables measured and simulated vehicle behaviour to be compared, models to be validated, and ADAS functions and different sensor configurations to be tested repeatedly in defined scenarios.

A software module for collecting data from the advanced driver assistance systems of an electric vehicle based on the MEB platform was designed, implemented and verified. The module receives, processes and recalculates data in real time; it also derives additional variables required to analyse vehicle behaviour and ADAS functions.

The open architecture supports the connection of additional sensors that are not part of the vehicle's standard equipment, their time synchronisation with operational data, and experimental verification of their applicability in ADAS.

The processed data are transmitted through a 5G mobile modem to a database hosted on a server within the Brno University of Technology network, where they are stored and made available for subsequent evaluation. At the same time, the data are supplied to the vehicle digital twin in the simulation tool and to the associated virtual test environment. The solution therefore connects the real vehicle, supplementary sensors, data infrastructure and simulation.

It enables measured and simulated vehicle behaviour to be compared, models to be validated, and ADAS functions and different sensor configurations to be tested repeatedly in defined scenarios.

This contribution describes the design and progressive refinement of a digital twin of the experimental Škoda Enyaq electric vehicle, intended for the development and validation of advanced driver assistance systems and related control algorithms. Following an analysis of the available simulation tools, IPG CarMaker was selected as the primary environment, as it supports the modelling of vehicle dynamics, the surrounding environment and defined test scenarios.

The model is based on a parametric Vehicle Dataset comprising chassis geometry, mass properties, suspension, tyres, steering, the braking system and the powertrain. The initial parameterisation was based on the vehicle's technical and design data and was subsequently refined using laboratory measurements and driving experiments.

The solution includes a modular architecture that allows the vehicle model to be connected to external control algorithms and specialised simulation tools. Integration options were analysed through co-simulation with MATLAB/Simulink, automatically generated C code, the C-code Interface and the FMI/FMU standard. The FMU standard was used to connect the vehicle model to a detailed powertrain model created in GT-SUITE.

The powertrain model also includes key control functions, particularly the battery management system, regenerative-braking logic, the DC/DC converter and 12 V electrical-system control. The result is an open and extensible simulation platform that integrates models of vehicle dynamics, the powertrain, the surrounding environment and control algorithms. The platform provides a basis for the iterative refinement of the digital twin and for virtual testing of ADAS functions in defined driving scenarios.

This methodology summarises the potential uses of HD maps in the development, testing and verification of advanced driver assistance systems (ADAS) and automated and autonomous-driving systems. It draws on knowledge gained in the ADASTWIN project, whose objective was to integrate a detailed model of real transport infrastructure, a vehicle digital twin, virtual sensor models, dynamic traffic data and scenario-based testing within a single simulation ecosystem.

The principal conclusion is that the value of HD maps is not limited to more accurate vehicle navigation. An HD map is a reusable digital reference model of the environment that can be used to create realistic, parameterisable and reproducible test scenarios. It can represent the actual road geometry, lane layout, traffic signs and markings, roadside objects, road surface and other elements relevant to vehicle and sensor operation.

When supplemented with dynamic information on traffic, the status of traffic-control equipment or weather conditions, it can become the basis of a digital twin of a real location.

The methodology is intended primarily for vehicle manufacturers and their suppliers, developers of ADAS and automated-driving functions, testing and validation teams, proving-ground operators, simulation-tool developers, research organisations, transport-infrastructure managers and public authorities.

For the Ministry of Transport, it can provide a basis for defining priorities in automated mobility, supporting testing and data infrastructure, advancing standardisation, and assessing the conditions for wider use of virtual methods in vehicle-safety verification.