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.
Highlights
When pedestrians are arriving at a crosswalk, it is foreseen that they will continue and cross; when they disappear from images, the expectation that their position is close to the last one will become stale after some time without them reappearing
This paper has presented the Social-Aware Driver Assistance System (SADAS) framework for developing traffic systems
DASs) that use social information. This information is related to characteristics of participants linked to their environment, activities and bonds, and their mutual influence
Summary
One of the potential new types of information are the social aspects considered in this work These include factors that affect people’s behaviors and depend on their personal characteristics (e.g., gender, age, and capabilities), the surrounding people (e.g., type of companion or crowd), the resources they use (e.g., type of vehicle and mobile phones), and the context (e.g., activity and meaning of the environment). It corresponds to a Multi-Agent System (MAS) [13] with several specific social components These include: sensing agents to transform low-level data from sensors into information; reasoner agents to derive new information from that available; observer agents that send the information to the rest of the traffic system and support external queries. The SADAS uses this information to derive new information on people’s potential behaviors given their activities This allows traffic signals and vehicle warnings to be adjusted for safer and more fluid traffic than in the original studies.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
