Navigating in complex and highly dynamic environments such as crowds is still a major challenge for autonomous vehicle such as autonomous wheelchairs or even autonomous cars. This article presents a new way of navigating in crowds by using behavioral clustering for the surrounding agents and representing the crowd as a set of moving polygons. Once the environment has been modelled in this way and the robot has all the information it needs, we then propose a navigation algorithm that is able to guide the vehicle through the scene. The key-points of this algorithm are that (1) it can avoid densely-populated areas in order to minimize the risk of being on a collision course with any of the surrounding dynamic obstacles, (2) it generates socially compliant trajectories.
Socially Compliant Navigation in dense crowds
Roman Bresson,Jacques Saraydaryan,J. Dugdale,A. Spalanzani
Published 2019 in 2019 IEEE Intelligent Vehicles Symposium (IV)
ABSTRACT
PUBLICATION RECORD
- Publication year
2019
- Venue
2019 IEEE Intelligent Vehicles Symposium (IV)
- Publication date
2019-06-09
- Fields of study
Computer Science, Engineering
- Identifiers
- External record
- Source metadata
Semantic Scholar
CITATION MAP
EXTRACTION MAP
CLAIMS
CONCEPTS
- behavioral clustering
A grouping approach that clusters surrounding agents according to observed behavior in the crowd scene.
Aliases: agent behavioral clustering
- crowd navigation algorithm
The planning procedure that computes a vehicle path through the crowd scene.
Aliases: navigation planner
- densely-populated areas
Parts of the scene where many agents are concentrated close together.
Aliases: crowded zones
- moving polygons
A geometric representation that models the crowd as a set of polygons that move over time.
Aliases: moving-polygon crowd model
- socially compliant trajectories
Vehicle trajectories that are shaped to respect social navigation norms around pedestrians.
Aliases: socially compliant paths
REFERENCES
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CITED BY
Showing 1-5 of 5 citing papers · Page 1 of 1