GPS navigation systems are nowadays used extensively by drivers to reach locations and minimize traveling time. It is crucial to have up-to-date map information for their correct functioning. Map updating approaches leveraging human intervention may lag behind frequent changes in urban road arrangements like allowed directions. In this work, we use a combination of crowdsensing and deep learning techniques to extract information about allowed directions of the streets in a completely automated way. We present a faster and more sustainable alternative to currently adopted methods to keep maps’ details updated, based on crowdsensing without user intervention. In particular, we use data from on-vehicle GPS receivers, onboard cameras, and OSM queries. We describe experiments with data from different sources, such as real datasets and trip simulators. To address the problem of classifying street allowed directions, we propose two approaches: the first is based on an MLP on GPS traces, and the second consists in adopting a ResNet50 model on onboard camera imagery. We discuss how the cheaper and more easily deployable first approach delivers performance comparable to the second.

Street direction classification using implicit vehicle crowdsensing and deep learning / Luciani, Alessio; Panizzi, Emanuele. - In: FUTURE GENERATION COMPUTER SYSTEMS. - ISSN 0167-739X. - 149:December 2023(2023), pp. 59-70. [10.1016/j.future.2023.07.015]

Street direction classification using implicit vehicle crowdsensing and deep learning

Alessio Luciani
Primo
Conceptualization
;
Emanuele Panizzi
Ultimo
Conceptualization
2023

Abstract

GPS navigation systems are nowadays used extensively by drivers to reach locations and minimize traveling time. It is crucial to have up-to-date map information for their correct functioning. Map updating approaches leveraging human intervention may lag behind frequent changes in urban road arrangements like allowed directions. In this work, we use a combination of crowdsensing and deep learning techniques to extract information about allowed directions of the streets in a completely automated way. We present a faster and more sustainable alternative to currently adopted methods to keep maps’ details updated, based on crowdsensing without user intervention. In particular, we use data from on-vehicle GPS receivers, onboard cameras, and OSM queries. We describe experiments with data from different sources, such as real datasets and trip simulators. To address the problem of classifying street allowed directions, we propose two approaches: the first is based on an MLP on GPS traces, and the second consists in adopting a ResNet50 model on onboard camera imagery. We discuss how the cheaper and more easily deployable first approach delivers performance comparable to the second.
2023
Crowdsensing; Deep learning; Image classification; Street direction; Urban map; Vehicle; Cameras; Deep learning; Global positioning system; Image classification; Learning systems; Crowdsensing; Deep learning; GPS navigation; Images classification; Map updating; Onboard camera; Street direction; Traveling time; Updating approaches; Urban map; Vehicles
01 Pubblicazione su rivista::01a Articolo in rivista
Street direction classification using implicit vehicle crowdsensing and deep learning / Luciani, Alessio; Panizzi, Emanuele. - In: FUTURE GENERATION COMPUTER SYSTEMS. - ISSN 0167-739X. - 149:December 2023(2023), pp. 59-70. [10.1016/j.future.2023.07.015]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1691658
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