The Challenge
- High-dimensional parameter space: Calculate energy-optimized driving style taking into account topography, speed limits, timetables, preceding and following trains, etc.
- Real-time application with limited CPU/GPU
The Approach
- Extensive topic research, stakeholder interviews
- Evaluate scenarios for relevance
- Modeling train, topography and other relevant data sources
- Design of the software architecture Two-stage algorithm development
Result & Added Value
- An algorithm that provides energy-saving driving recommendations for the train driver
- Driving recommendations that come very close to the real driving style of train drivers
- Detecting implausible scenarios for special treatment
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Our Competencies
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