HISS: A Pedestrian Trajectory Planning Framework Using Receding Horizon Optimization
The paper proposes a generative pedestrian trajectory modeling framework named HISS - Human Interactions in Shared Space. The trajectory modeling framework is based on a receding horizon optimization approach utilizing pedestrian behavior and interactions that seeks to capture pedestrian trajectory...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2023-01-01
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| Series: | IEEE Open Journal of Intelligent Transportation Systems |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10143376/ |
| _version_ | 1827917854186405888 |
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| author | Saumya Gupta Mohamed H. Zaki Adan Vela |
| author_facet | Saumya Gupta Mohamed H. Zaki Adan Vela |
| author_sort | Saumya Gupta |
| collection | DOAJ |
| description | The paper proposes a generative pedestrian trajectory modeling framework named HISS - Human Interactions in Shared Space. The trajectory modeling framework is based on a receding horizon optimization approach utilizing pedestrian behavior and interactions that seeks to capture pedestrian trajectory planning and execution. The benefit of the proposed dynamic optimization trajectory generation approach is that it requires minimal calibration data under a variety of traffic scenarios. In this paper, we formalize several pedestrian-pedestrian interaction scenarios and implement trajectories’ conflict avoidance through mixed integer linear programming (MILP). We validate the proposed framework on two benchmark datasets - DUT and TrajNet++. The paper shows that when the framework’s parameters are tuned to certain initial conditions and pedestrian behavior and interaction rules, the framework generates pedestrian trajectories similar to those observable in real-world scenarios, justifying the framework’s capability to provide explanations and solutions to various traffic situations. This feature makes the proposed framework useful for modelers and urban city planners in making policy decisions. |
| first_indexed | 2024-03-13T03:34:24Z |
| format | Article |
| id | doaj.art-635364fcbb424a8fa8fbebb592843d79 |
| institution | Directory Open Access Journal |
| issn | 2687-7813 |
| language | English |
| last_indexed | 2024-03-13T03:34:24Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Intelligent Transportation Systems |
| spelling | doaj.art-635364fcbb424a8fa8fbebb592843d792023-06-23T23:01:18ZengIEEEIEEE Open Journal of Intelligent Transportation Systems2687-78132023-01-01445647010.1109/OJITS.2023.328223710143376HISS: A Pedestrian Trajectory Planning Framework Using Receding Horizon OptimizationSaumya Gupta0https://orcid.org/0000-0002-4244-3307Mohamed H. Zaki1https://orcid.org/0000-0002-2970-2423Adan Vela2Civil, Environmental, and Construction Engineering Department, University of Central Florida, Orlando, FL, USADepartment of Civil and Environmental Engineering, Western University, London, ON, CanadaIndustrial Engineering and Management Systems Department, University of Central Florida, Orlando, FL, USAThe paper proposes a generative pedestrian trajectory modeling framework named HISS - Human Interactions in Shared Space. The trajectory modeling framework is based on a receding horizon optimization approach utilizing pedestrian behavior and interactions that seeks to capture pedestrian trajectory planning and execution. The benefit of the proposed dynamic optimization trajectory generation approach is that it requires minimal calibration data under a variety of traffic scenarios. In this paper, we formalize several pedestrian-pedestrian interaction scenarios and implement trajectories’ conflict avoidance through mixed integer linear programming (MILP). We validate the proposed framework on two benchmark datasets - DUT and TrajNet++. The paper shows that when the framework’s parameters are tuned to certain initial conditions and pedestrian behavior and interaction rules, the framework generates pedestrian trajectories similar to those observable in real-world scenarios, justifying the framework’s capability to provide explanations and solutions to various traffic situations. This feature makes the proposed framework useful for modelers and urban city planners in making policy decisions.https://ieeexplore.ieee.org/document/10143376/Pedestrian behavioractive mobilitytrajectory planningtrajectory generationreceding horizon controlmixed integer linear programming |
| spellingShingle | Saumya Gupta Mohamed H. Zaki Adan Vela HISS: A Pedestrian Trajectory Planning Framework Using Receding Horizon Optimization IEEE Open Journal of Intelligent Transportation Systems Pedestrian behavior active mobility trajectory planning trajectory generation receding horizon control mixed integer linear programming |
| title | HISS: A Pedestrian Trajectory Planning Framework Using Receding Horizon Optimization |
| title_full | HISS: A Pedestrian Trajectory Planning Framework Using Receding Horizon Optimization |
| title_fullStr | HISS: A Pedestrian Trajectory Planning Framework Using Receding Horizon Optimization |
| title_full_unstemmed | HISS: A Pedestrian Trajectory Planning Framework Using Receding Horizon Optimization |
| title_short | HISS: A Pedestrian Trajectory Planning Framework Using Receding Horizon Optimization |
| title_sort | hiss a pedestrian trajectory planning framework using receding horizon optimization |
| topic | Pedestrian behavior active mobility trajectory planning trajectory generation receding horizon control mixed integer linear programming |
| url | https://ieeexplore.ieee.org/document/10143376/ |
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