Latest revision as of 06:48, 23 March 2021

Abstract

Shared spaces are gaining presence in cities, where a variety of players and mobility types (pedestrians, bicycles, motorcycles, and cars) move without specifically delimited areas. This makes the traffic they comprise challenging for automated systems. The information traditionally considered (e.g., streets, and obstacle positions and speeds) is not enough to build suitable models of the environment. The required explanatory and anticipation capabilities need additional information to improve them. Social aspects (e.g., goal of the displacement, companion, or available time) should be considered, as they have a strong influence on how people move and interact with the environment. This paper presents the Social-Aware Driver Assistance System (SADAS) approach to integrate this information into traffic systems. It relies on a domain-specific modelling language for social contexts and their changes. Specifications compliant with it describe social and system information, their links, and how to process them. Traffic social properties are the formalization within the language of relevant knowledge extracted from literature to interpret information. A multi-agent system architecture manages these specifications and additional processing resources. A SADAS can be connected to other parts of traffic systems by means of subscription-notification mechanisms. The case study to illustrate the approach applies social knowledge to predict people&rsquo

s movements. It considers a distributed system for obstacle detection and tracking, and the intelligent management of traffic signals.

Document type: Article

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The different versions of the original document can be found in:

• http://dx.doi.org/10.3390/s19020221 under the license https://creativecommons.org/licenses/by

• http://dx.doi.org/10.3390/s19020221 under the license http://creativecommons.org/licenses/by/3.0/

• http://europepmc.org/articles/PMC6359130 under the license https://creativecommons.org/licenses/by/4.0

• https://www.mdpi.com/1424-8220/19/2/221/pdf

• https://dblp.uni-trier.de/db/journals/sensors/sensors19.html#Fernandez-Isabel19,

https://www.mdpi.com/1424-8220/19/2/221/pdf,

https://www.mdpi.com/1424-8220/19/2/221,

https://www.ncbi.nlm.nih.gov/pubmed/30634439,

https://academic.microsoft.com/#/detail/2909929459 under the license cc-by

• https://www.mdpi.com/1424-8220/19/2/221/pdf,

http://dx.doi.org/10.3390/s19020221

under the license https://creativecommons.org/licenses/by/4.0/