INAVET

The INAVET – Institut für angewandte Verkehrstelematik GmbH is a technology-oriented company based in Dresden, founded in 2013. Specializing in traffic telematics, multimodal traffic management, and transportation energy efficiency, INAVET GmbH conducts scientific analyses and devises customized software and system solutions.

In road traffic, our core areas include automated, networked, and cooperative driving (C-ITS) for all types of road users, along with consulting and implementation services in traffic control and information. We also specialize in traffic quality analyses at signal-controlled intersections, as well as road sections and networks in both individual traffic and public transportation.

In rail traffic, our focus lies in developing driver advisory systems and technology for driving simulators to enhance operational processes. Consulting and software services related to multimodal traffic management complement our portfolio. By integrating scientifically recognized telematics methods into everyday operations, INAVET pioneers innovative solutions and establishes them in the market.

You can find more information here.

Participation:

ATO application scenarios – What tests should an automated train be able to pass?

In automated train operation (ATO), cameras and other sensors will take over the observation of the train environment from the train driver in higher degrees of automation (Grade of Automation - GoA 3/4). Artificial [...]

FASaN – Driver advisory systems adaptive sustainability in railway operations

Driver advisory systems (DAS) in railway operations can support train drivers in their work without interfering with vehicle control or safety technology. In the mFUND project FASaN, a prototype advanced driver advisory system is [...]

FAS-D – Expansion of driver assistance systems in the railway sector by improving the data basis

The core objective of this FAS-D project was to show which data is already being used for a driver assistance system (DAS) in the railway sector and to analyse which additional data could bring further benefits for use in a DAS. The [...]