Dreams4Cars Experimental data from Autonomous Test Vehicle

The Horizon 2020 project Dreams4Cars (www.dreams4cars.eu) has developed dream-like (offline) learning methods to be used for the development of Autonomous Driving and –more in general– as mechanisms to increase the Cognition abilities and Autonomy of robots. The purpose of dreamlike learning in Dreams4Cars is to deal with (possibly rare) dangerous events synthetizing correct behaviour and control without needing to experience the events, and more efficiently than via straightforward trial and errors. That is, to discover potential threats before they actually happen and prepare appropriate action strategies in advance.

During the 3-years development process the project has collected and processed a wealth of experimental data from autonomous test vehicles. Parts of these data and advice how to use these data are made available to the public.

The datasets and how they can be accessed is described in the attached report (project deliverable D5.5 Section 2), the datasets are provided in the ZIP-file.

Purpose of the Dataset

The data provided here have the purpose of demonstrating learning of forward models (the first building block of mental imagery and dreams). There are two sets of data: one for the lateral dynamics and another for the longitudinal dynamics. Each dataset has its own example of training of the corresponding forward model). Then following paper provides additional theoretical aspects: M. Da Lio, D. Bortoluzzi, e G. P. Rosati Papini, «Modelling longitudinal vehicle dynamics with neural networks», Vehicle System Dynamics, pagg. 1–19, lug. 2019, doi: 10.1080/00423114.2019.1638947

Contacts:

Mauro Da Lio, University of Trento, [e-mail]

Elmar Berghoefer, Deutsches Forschungszentrum für Künstliche Intelligenz GmbH, [e-mail]

Mehmed Yueksel, Deutsches Forschungszentrum für Künstliche Intelligenz GmbH, [e-mail]