Foto: DLR
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
Mobility as a Service / User-Centric Mobility (January 2023): The mobility as a service puts the smartphone at the center of the mobility of the future - smartphone becomes the mobility hub (Bitkom Research)
Video: Multimodal Navigation with Wearable Sensors: In the past we concentrated on personal navigation for professional users, such firefighters or police. Back in 2017, we published our first results on the research area of multimodal navigation using wearable sensors. In this video we show a very accurate passenger localization using only inertial sensors introduced in the front pocket of the trousers. Our algorithms are able to provide localization when walking horizontally, walking on stairs, riding a bicycle and driving a car.
Video: Passenger’s Commuting at Stations/Airports – Localization with Smartphones: In 2021, we adapted our wearable-based inertial algorithms to work with the low-cost sensors embedded in commercial smartphones. In this video published in 2022 we present a journey in which a passenger enters and leaves a train station. We use the embedded inertial sensors of the smartphone as well as the barometer and the satellite receiver embedded in the smartphone. By walking outdoors, the satellite-based navigation enables the calibration of the inertial sensors, thus in the station where no satellites are visible, the passenger localization is possible without the use of maps or cell phone/WiFi signals.
Video: Passenger’s Commuting at Stations/Airports – Activity Identification with Smartphones: We have researched in the past the activities’ identification with wearable sensors for professional applications. In 2022, we presented the identification of passengers’ activities using commercial smartphones. In the context of urban mobility, we concentrate on the activities walking, climbing stairs, using the lift, seating and standing/queuing. These activities are relevant in the context of commuting in stations/airports to predict passenger’s flows and to understand the passenger’s needs/behaviour.
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.