Multi-Vehicle Motion Planning for Social Optimal Mobility-on-Demand
In this paper we consider a fleet of self-driving cars operating in a road network governed by rules of the road, such as the Vienna Convention on Road Traffic, providing rides to customers to serve their demands with desired deadlines. We focus on the associated motion planning problem that trades-...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
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Institute of Electrical and Electronics Engineers (IEEE)
2018
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| Online Access: | http://hdl.handle.net/1721.1/118967 https://orcid.org/0000-0002-1132-1462 https://orcid.org/0000-0002-2225-7275 https://orcid.org/0000-0001-5473-3566 |
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| author | Karlsson, Jesper Tumova, Jana Vasile, Cristian-Ioan Karaman, Sertac Rus, Daniela L |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Karlsson, Jesper Tumova, Jana Vasile, Cristian-Ioan Karaman, Sertac Rus, Daniela L |
| author_sort | Karlsson, Jesper |
| collection | MIT |
| description | In this paper we consider a fleet of self-driving cars operating in a road network governed by rules of the road, such as the Vienna Convention on Road Traffic, providing rides to customers to serve their demands with desired deadlines. We focus on the associated motion planning problem that trades-off the demands' delays and level of violation of the rules of the road to achieve social optimum among the vehicles. Due to operating in the same environment, the interaction between the cars must be taken into account, and can induce further delays. We propose an integrated route and motion planning approach that achieves scalability with respect to the number of cars by resolving potential collision situations locally within so-called bubble spaces enclosing the conflict. The algorithms leverage the road geometries, and perform joint planning only for lead vehicles in the conflict and use queue scheduling for the remaining cars. Furthermore, a framework for storing previously resolved conflict situations is proposed, which can be use for quick querying of joint motion plans. We show the mobility-on-demand setup and effectiveness of the proposed approach in simulated case studies involving up to 10 self-driving vehicles. |
| first_indexed | 2024-09-23T12:00:42Z |
| format | Article |
| id | mit-1721.1/118967 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T12:00:42Z |
| publishDate | 2018 |
| publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| record_format | dspace |
| spelling | mit-1721.1/1189672022-10-01T07:36:35Z Multi-Vehicle Motion Planning for Social Optimal Mobility-on-Demand Karlsson, Jesper Tumova, Jana Vasile, Cristian-Ioan Karaman, Sertac Rus, Daniela L Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology. Department of Aeronautics and Astronautics Vasile, Cristian-Ioan Karaman, Sertac Rus, Daniela L In this paper we consider a fleet of self-driving cars operating in a road network governed by rules of the road, such as the Vienna Convention on Road Traffic, providing rides to customers to serve their demands with desired deadlines. We focus on the associated motion planning problem that trades-off the demands' delays and level of violation of the rules of the road to achieve social optimum among the vehicles. Due to operating in the same environment, the interaction between the cars must be taken into account, and can induce further delays. We propose an integrated route and motion planning approach that achieves scalability with respect to the number of cars by resolving potential collision situations locally within so-called bubble spaces enclosing the conflict. The algorithms leverage the road geometries, and perform joint planning only for lead vehicles in the conflict and use queue scheduling for the remaining cars. Furthermore, a framework for storing previously resolved conflict situations is proposed, which can be use for quick querying of joint motion plans. We show the mobility-on-demand setup and effectiveness of the proposed approach in simulated case studies involving up to 10 self-driving vehicles. 2018-11-08T20:24:30Z 2018-11-08T20:24:30Z 2018-09 2018-05 Article http://purl.org/eprint/type/ConferencePaper 978-1-5386-3081-5 http://hdl.handle.net/1721.1/118967 Karlsson, Jesper, et al. “Multi-Vehicle Motion Planning for Social Optimal Mobility-on-Demand.” 2018 IEEE International Conference on Robotics and Automation (ICRA), 21-25 May 2018, Brisbane, Australia, IEEE, 2018, pp. 7298–305. https://orcid.org/0000-0002-1132-1462 https://orcid.org/0000-0002-2225-7275 https://orcid.org/0000-0001-5473-3566 en_US http://dx.doi.org/10.1109/ICRA.2018.8462968 2018 IEEE International Conference on Robotics and Automation (ICRA) Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Institute of Electrical and Electronics Engineers (IEEE) Cristian-Ioan Vasile |
| spellingShingle | Karlsson, Jesper Tumova, Jana Vasile, Cristian-Ioan Karaman, Sertac Rus, Daniela L Multi-Vehicle Motion Planning for Social Optimal Mobility-on-Demand |
| title | Multi-Vehicle Motion Planning for Social Optimal Mobility-on-Demand |
| title_full | Multi-Vehicle Motion Planning for Social Optimal Mobility-on-Demand |
| title_fullStr | Multi-Vehicle Motion Planning for Social Optimal Mobility-on-Demand |
| title_full_unstemmed | Multi-Vehicle Motion Planning for Social Optimal Mobility-on-Demand |
| title_short | Multi-Vehicle Motion Planning for Social Optimal Mobility-on-Demand |
| title_sort | multi vehicle motion planning for social optimal mobility on demand |
| url | http://hdl.handle.net/1721.1/118967 https://orcid.org/0000-0002-1132-1462 https://orcid.org/0000-0002-2225-7275 https://orcid.org/0000-0001-5473-3566 |
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