Connected Mobility and Fleet Data
Vehicles are increasingly being connected to each other, with other road users, with the infrastructure and/or with cloud services. Here, too, the questions are crucial,
- what information and sensor data are needed in what form for the road users and traffic participants in order to be able to make safe and valid decisions to improve traffic flow and safety,
- what information and sensor data are needed from road users and traffic participants to improve the control and management of traffic,
- how to design system-of-systems architectures and information flows in order to get the right and relevant information to the decision makers.
As explained in Big Data Analytics, however, it is above all about the neat and clever formulation of the message contents, so that with as little data as possible the right and relevant information is provided for valid and safe decisions.
In the case that numerous road users are connected to a fleet, a lot of useful information and models can be retrieved from the data:
- The behavioural patterns from traffic participants help to deduce new, performant and cooperative advanced drive assistance systems.
- The identification of especially good and bad behavioural patterns (for example with respect to energy efficiency, traffic efficiency, safety,…) helps for the continuous improvement of according control systems.
- The motion data can be used as virtual sensor for the identification of the local and global traffic situations. Sensor data from advanced driver assistance systems can be used for vehicles as traffic sensors or for collective perception, when vehicles share their traffic observations directly with other vehicles.
- The connected data can be used as fundament for the development of predictive maintenance applications.
- Connected systems in combination with Machine Learning and anomaly detection allow the realization of collective learning, which enables an effective introduction of automated driving.
Beside the according application experience ANDATA has many available ready-made solutions, for a quick, specific evaluation and analysis of fleet data with arbitrary size. Furthermore a broad spectrum of methods is available for the proper realization of anonymization, data efficiency, analysis capabilities in development, implementation and steady improvement and adaption during operations.
Particularly it is almost always a matter of deducing the right and relevant data for optimal and safe decisions in the according control and automation applications. For the application specific determination of the proper information and sensor data, according engineering expertise and development tools are available at ANDATA.
Under the brand of VERONET a comprehensive, modular construction kit for the developmet and implemenation of connected driving solutions is available.
The offered solutions of course are generic in a way that these can also be applied in any arbitrary application for the Internet of Things.
Project Trilogy for Connected, Cooperative, Automated Driving
The project triology is supported and partially funded by
Project DIGEST - Digital Twin of the Traffic Infrastructure
Within the project DIGEST ANDATA with the partners from the project consortium developed a Digital Twin of the road infrastructure from original sources as fundament for trustworthiness exchange of informations between infrastructure providers and vehicles.
Detail about the resulting CCAM Decision Support Plattform can be found in https://ieeexplore.ieee.org/document/10102410
Project EVE - Efficient Right Way for Emergency Vehicles in Automated Traffic
The project EVE dealt with the secure prioritization of emergency vehicles by means of suitable C-ITS services.
ANDATA's contribution in the project was the quantitative assessment of risks and traffic impacts in the case of misuse or incorrect use of the services.
Information about ANDATA's various patents in the field of connected driving can be requested from email@example.com.