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Sensors 2019, 19(2), 221; https://doi.org/10.3390/s19020221

Social-Aware Driver Assistance Systems for City Traffic in Shared Spaces

1
Data Science Laboratory (DSLab), Universidad Rey Juan Carlos, 28933 Móstoles, Spain
2
Research Group on Agent-Based, Social & Interdisciplinary Applications (GRASIA), Universidad Complutense de Madrid, 28040 Madrid, Spain
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Received: 5 November 2018 / Revised: 21 December 2018 / Accepted: 3 January 2019 / Published: 9 January 2019
(This article belongs to the Special Issue Advances on Vehicular Networks: From Sensing to Autonomous Driving)
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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’s movements. It considers a distributed system for obstacle detection and tracking, and the intelligent management of traffic signals. View Full-Text
Keywords: shared space; multi-modal traffic; people displacement; traffic social property; social knowledge; Social-Aware Driver Assistance System; Multi-Agent System shared space; multi-modal traffic; people displacement; traffic social property; social knowledge; Social-Aware Driver Assistance System; Multi-Agent System
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Fernández-Isabel, A.; Fuentes-Fernández, R. Social-Aware Driver Assistance Systems for City Traffic in Shared Spaces. Sensors 2019, 19, 221.

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