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Interactive behavior modeling of multiple agents is an essential challenge in simulation, especially in scenarios when agents need to avoid collisions and cooperate at the same time.
S. Tang and V. Kumar, “Safe and complete trajectory generation for robot teams with higher-order dynamics,” in 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2016, pp. 1894–1901
1901
Earlier work this paper cites.
B. Piccoli and A. Tosin, “Pedestrian flows in bounded domains with obstacles,” Continuum Mechanics and Thermodynamics , vol. 21, no. 2, pp. 85–107, apr 2009. [Online]. Available: https://doi.org/10.1007%2Fs00161-009-0100-x
2009
Earlier work this paper cites.
S. Sarmady, F. Haron, and A. Z. H. Talib, “Modeling groups of pedestrians in least effort crowd movements using cellular automata,” in 2009 Third Asia International Conference on Modelling and Simulation , 2009, pp. 520–525
2009
Earlier work this paper cites.
J. van den Berg, S. J. Guy, M. Lin, and D. Manocha, “Reciprocal n-body collision avoidance,” in Robotics Research , C. Pradalier, R. Siegwart, and G. Hirzinger, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011, pp. 3–19
2011
Earlier work this paper cites.
W. G. van Toll, A. F. Cook IV, and R. Geraerts, “Real-time density-based crowd simulation,” Computer Animation and Virtual Worlds , vol. 23, no. 1, pp. 59–69, 2012. [Online]. Available: https://onlinelibrary.wiley.com/doi/abs/10.1002/cav.1424
2012
Earlier work this paper cites.
W. van Toll, N. Jaklin, and R. Geraerts, “Towards believable crowds : A generic multi-level framework for agent navigation,” 2015. [Online]. Available: https://api.semanticscholar.org/CorpusID:14620077
2015
Earlier work this paper cites.
D. Zhou, Z. Wang, S. Bandyopadhyay, and M. Schwager, “Fast, on-line collision avoidance for dynamic vehicles using buffered voronoi cells,” IEEE Robotics and Automation Letters , vol. 2, no. 2, pp. 1047–1054, 2017
2017
Earlier work this paper cites.
L. Wang, A. D. Ames, and M. Egerstedt, “Safety barrier certificates for collisions-free multirobot systems,” IEEE Transactions on Robotics , vol. 33, no. 3, pp. 661–674, 2017
2017
Earlier work this paper cites.
N. K. Mahato, A. Klar, and S. Tiwari, “Particle methods for multi-group pedestrian flow,” 2017
2017
Earlier work this paper cites.
A. Fonseca-Morales and O. Hernández-Lerma, “Potential Differential Games,” Dynamic Games and Applications , vol. 8, no. 2, pp. 254–279, June 2018. [Online]. Available: https://ideas.repec.org/a/spr/dyngam/v8y2018i2d10.1007_s13235-017-0218-6.html
2018
Earlier work this paper cites.
J. F. Fisac, E. Bronstein, E. Stefansson, D. Sadigh, S. S. Sastry, and A. D. Dragan, “Hierarchical game-theoretic planning for autonomous vehicles,” in 2019 International Conference on Robotics and Automation (ICRA) , 2019, pp. 9590–9596
2019
Earlier work this paper cites.
G. Sartoretti, J. Kerr, Y. Shi, G. Wagner, T. K. S. Kumar, S. Koenig, and H. Choset, “Primal: Pathfinding via reinforcement and imitation multi-agent learning,” IEEE Robotics and Automation Letters , vol. 4, no. 3, pp. 2378–2385, 2019
2019
Earlier work this paper cites.
R. A. Yeh, A. G. Schwing, J. Huang, and K. Murphy, “Diverse generation for multi-agent sports games,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2019
2019
Earlier work this paper cites.
L. Sun, W. Zhan, D. Wang, and M. Tomizuka, “Interactive prediction for multiple, heterogeneous traffic participants with multi-agent hybrid dynamic bayesian network,” in 2019 IEEE Intelligent Transportation Systems Conference (ITSC) , 2019, pp. 1025–1031
2019
Cited alongside, same era.
A. Desai and N. Michael, “Online planning for quadrotor teams in 3-d workspaces via reachability analysis on invariant geometric trees,” in 2020 IEEE International Conference on Robotics and Automation (ICRA) , 2020, pp. 8769–8775
2020
Cited alongside, same era.
C. E. Luis, M. Vukosavljev, and A. P. Schoellig, “Online trajectory generation with distributed model predictive control for multi-robot motion planning,” IEEE Robotics and Automation Letters , vol. 5, no. 2, pp. 604–611, apr 2020
2020
Cited alongside, same era.
J. Park, J. Kim, I. Jang, and H. J. Kim, “Efficient multi-agent trajectory planning with feasibility guarantee using relative bernstein polynomial,” in 2020 IEEE International Conference on Robotics and Automation (ICRA) , 2020, pp. 434–440
Z.-H. Yin, L. Sun, L. Sun, M. Tomizuka, and W. Zhan, “Diverse critical interaction generation for planning and planner evaluation,” in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2021, pp. 7036–7043
2021
Later among the works it cites.
B. Şenbaşlar, W. Hönig, and N. Ayanian, “Rlss: Real-time multi-robot trajectory replanning using linear spatial separations,” 2022
2022
Later among the works it cites.
C. Wang, H.-C. Lin, S. Jin, X. Zhu, L. Sun, and M. Tomizuka, “Bpomp: A bilevel path optimization formulation for motion planning,” in 2022 American Control Conference (ACC) . IEEE, 2022, pp. 1891–1897
2022
Later among the works it cites.
K. Miller and S. Mitra, “Multi-agent motion planning using differential games with lexicographic preferences,” in 2022 IEEE 61st Conference on Decision and Control (CDC) , 2022, pp. 5751–5756
2022
Later among the works it cites.
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2020
Cited alongside, same era.
R. Spica, E. Cristofalo, Z. Wang, E. Montijano, and M. Schwager, “A real-time game theoretic planner for autonomous two-player drone racing,” IEEE Transactions on Robotics , vol. 36, no. 5, pp. 1389–1403, 2020
2020
Cited alongside, same era.
B. Rivière, W. Hönig, Y. Yue, and S.-J. Chung, “Glas: Global-to-local safe autonomy synthesis for multi-robot motion planning with end-to-end learning,” IEEE Robotics and Automation Letters , vol. 5, no. 3, pp. 4249–4256, 2020
2020
Cited alongside, same era.
Q. Li, F. Gama, A. Ribeiro, and A. Prorok, “Graph neural networks for decentralized multi-robot path planning,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2020, pp. 11 785–11 792
2020
Cited alongside, same era.
C. Wang, J. Bingham, and M. Tomizuka, “Trajectory splitting: A distributed formulation for collision avoiding trajectory optimization,” in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2021, pp. 8113–8120
2021
Cited alongside, same era.
T. Kavuncu, A. Yaraneri, and N. Mehr, “Potential ilqr: A potential-minimizing controller for planning multi-agent interactive trajectories,” 2021
2021
Cited alongside, same era.
M. Wang, Z. Wang, J. Talbot, J. C. Gerdes, and M. Schwager, “Game-theoretic planning for self-driving cars in multivehicle competitive scenarios,” IEEE Transactions on Robotics , vol. 37, no. 4, pp. 1313–1325, 2021
2021
Cited alongside, same era.
S. L. Cleac’h, M. Schwager, and Z. Manchester, “Algames: A fast augmented lagrangian solver for constrained dynamic games,” 2021
2021
Cited alongside, same era.
S. Batra, Z. Huang, A. Petrenko, T. Kumar, A. Molchanov, and G. S. Sukhatme, “Decentralized control of quadrotor swarms with end-to-end deep reinforcement learning,” 2021
2021
Cited alongside, same era.
F. Laine, D. Fridovich-Keil, C.-Y. Chiu, and C. Tomlin, “The computation of approximate generalized feedback nash equilibria,” 2022
2022
Later among the works it cites.
J. Tordesillas and J. P. How, “Mader: Trajectory planner in multiagent and dynamic environments,” IEEE Transactions on Robotics , vol. 38, no. 1, pp. 463–476, 2022
2022
Later among the works it cites.
Q. Zhang, Y. Gao, Y. Zhang, Y. Guo, D. Ding, Y. Wang, P. Sun, and D. Zhao, “Trajgen: Generating realistic and diverse trajectories with reactive and feasible agent behaviors for autonomous driving,” IEEE Transactions on Intelligent Transportation Systems , vol. 23, no. 12, pp. 24 474–24 487, 2022
2022
Later among the works it cites.
L. Sun, C. Tang, Y. Niu, E. Sachdeva, C. Choi, T. Misu, M. Tomizuka, and W. Zhan, “Domain knowledge driven pseudo labels for interpretable goal-conditioned interactive trajectory prediction,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2022, pp. 13 034–13 041
2022
Later among the works it cites.
N. Mehr, M. Wang, M. Bhatt, and M. Schwager, “Maximum-entropy multi-agent dynamic games: Forward and inverse solutions,” IEEE Transactions on Robotics , vol. 39, no. 3, pp. 1801–1815, 2023
2023
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Z. Jian, S. Zhang, L. Sun, W. Zhan, N. Zheng, and M. Tomizuka, “Long-term dynamic window approach for kinodynamic local planning in static and crowd environments,” IEEE Robotics and Automation Letters , vol. 8, no. 6, pp. 3294–3301, 2023
2023
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Z. Williams, J. Chen, and N. Mehr, “Distributed potential ilqr: Scalable game-theoretic trajectory planning for multi-agent interactions,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) , 2023, pp. 01–07
2023
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W.-J. Chang, C. Tang, C. Li, Y. Hu, M. Tomizuka, and W. Zhan, “Editing driver character: Socially-controllable behavior generation for interactive traffic simulation,” IEEE Robotics and Automation Letters , vol. 8, no. 9, pp. 5432–5439, 2023
2023
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Z. Zhou, J. Wang, Y.-H. Li, and Y.-K. Huang, “Query-centric trajectory prediction,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2023
2023
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