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

Alberto Fernández-Isabel1, Rubén Fuentes-Fernández2

  • 1Data Science Laboratory (DSLab), Universidad Rey Juan Carlos, 28933 Móstoles, Spain. alberto.fernandez.isabel@urjc.es.

Sensors (Basel, Switzerland)
|January 13, 2019
PubMed
Summary

Integrating social aspects into traffic systems improves automated navigation. The Social-Aware Driver Assistance System (SADAS) uses social context modeling for better prediction and traffic management in shared urban spaces.

Keywords:
Multi-Agent SystemSocial-Aware Driver Assistance Systemmulti-modal trafficpeople displacementshared spacesocial knowledgetraffic social property

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Area of Science:

  • Urban planning and intelligent transportation systems.
  • Human-computer interaction and multi-agent systems.
  • Computer vision and predictive modeling for autonomous driving.

Background:

  • Shared urban spaces with mixed mobility (pedestrians, cyclists, vehicles) pose challenges for current automated traffic systems.
  • Traditional traffic models lack sufficient data, relying only on physical parameters like position and speed.
  • Understanding human behavior and social context is crucial for accurate environmental modeling and prediction.

Purpose of the Study:

  • To introduce the Social-Aware Driver Assistance System (SADAS) for integrating social information into traffic management.
  • To develop a domain-specific modeling language for describing and processing social contexts in traffic.
  • To enhance the predictive and explanatory capabilities of automated systems in complex urban environments.

Main Methods:

  • Development of a domain-specific modeling language for social contexts and their dynamics.
  • Formalization of relevant 'traffic social properties' from literature within the modeling language.
  • Implementation of a multi-agent system architecture to manage social specifications and processing.
  • Utilizing subscription-notification mechanisms for SADAS integration with existing traffic systems.

Main Results:

  • Demonstrated the application of social knowledge for predicting pedestrian and vehicle movements in shared spaces.
  • Showcased the system's ability to improve obstacle detection, tracking, and traffic signal management.
  • Validated the SADAS approach through a case study in a complex urban traffic scenario.

Conclusions:

  • Social factors significantly influence movement and interaction in shared urban spaces, necessitating their inclusion in automated systems.
  • The SADAS approach provides a robust framework for integrating social context into traffic management, enhancing prediction and safety.
  • Future intelligent transportation systems must incorporate social-aware modeling for effective operation in diverse urban environments.