The focus of the Multimodal Navigation group is to explore solutions for multimodal navigation of passengers in urban environments. The focus relies on public transport and sustainable individual transport modes used for the first/last mile, such as bicycles, e-scooters and walking.
This group has long-standing expertise in sensor-fusion techniques for pedestrian navigation in key urban environments where satellite navigation is unavailable or hindered, such as train stations and airports.
This group has extensive experience as well with machine learning techniques for passenger activity recognition and identification of transport modes. The methods developed for seamless identification of the means of transport have been patented.
The research of the Multimodal Navigation group enables integrated mobility services such as Location-Based Services or E-Ticketing. Therefore, the research carried out in this group supports applications in the public transport and makes an important contribution to a sustainable digitization of the transport system.
2022-2023 - 3rd Party Project: MyWay
Foundations for the extension of mobility-apps to include personalized routing for passengers with diverse profiles
2022-2024 - 3rd Party Project: ModalX
Reliable and automatic modality detection with a trusted edge cloud-based platform for smart mobility services such as location-based services or E-ticketing
2022-2023 - 3rd Party Project: Procope
Smart Security for Smart Cities: Advanced E-Ticketing
2022-2024 - Internal Project: VMo4Orte
Seamless transport mode identification, passenger localization and passenger flows modelling for urban environments
2021-2023 - EU Project: RESCUER
Navigation system with communication for rescue personnel
2022-2024 - Internal Project: KoKoVI
Crowd-sensing smartphone-based localization for cyclists and interaction with an autonomous shuttle
2022-2024 - Internal Project: V&V4NGC
Development and integration of a data-based cyclist movement model for SUMO
2022 - Multi-Sensor Positioning for Navigation in Smart Cities
The mobility of people and goods plays an important role in the life, work and prosperity in smart cities. Particularly, the positioning in train stations or airports is of great importance to understand the needs and preferences of the passengers and their behavioral patterns. In outdoor scenarios, walking, cycling and e-scooters are sustainable mobility options that complement the public transport. These mobility options require a robust positioning to enable their frictionless coexistence with the motorized transport modes.
Artificial Intelligence (AI) can provide a significant boost for understanding mobility behavioral patterns and for the protection of pedestrians, cyclists and e-scooters as well. For the use of AI in safety-critical applications, new methods of validation and training are required. The analysis of big data and the methods for data driven research should be used to gain high quality data dedicated to the training of AI for transport applications.
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Information Personnel Matters & Projects
Publiaations Multimodal Navigation