Fetching the paper…
Reading the bibliography…
The ability to estimate human intentions and interact with human drivers intelligently is crucial for autonomous vehicles to successfully achieve their objectives.
R. Bellman, “A markovian decision process,” Journal of mathematics and mechanics , vol. 6, no. 5, pp. 679–684, 1957
1957
Earlier work this paper cites.
X. Vives, “Nash equilibrium with strategic complementarities,” Journal of Mathematical Economics , vol. 19, no. 3, pp. 305–321, 1990
1990
Earlier work this paper cites.
D. O. Stahl II and P. W. Wilson, “Experimental evidence on players’ models of other players,” Journal of economic behavior & organization , vol. 25, no. 3, pp. 309–327, 1994
1994
Earlier work this paper cites.
R. D. McKelvey and T. R. Palfrey, “Quantal response equilibria for normal form games,” Games and economic behavior , vol. 10, no. 1, pp. 6–38, 1995
1995
Earlier work this paper cites.
M. Treiber, A. Hennecke, and D. Helbing, “Congested traffic states in empirical observations and microscopic simulations,” Physical review E , vol. 62, no. 2, p. 1805, 2000
2000
Earlier work this paper cites.
L. Kocsis and C. Szepesvári, “Bandit based monte-carlo planning,” in European conference on machine learning . Springer, 2006, pp. 282–293
2006
Earlier work this paper cites.
M. A. Costa-Gomes and V. P. Crawford, “Cognition and behavior in two-person guessing games: An experimental study,” American economic review , vol. 96, no. 5, pp. 1737–1768, 2006
2006
Earlier work this paper cites.
S. Sekizawa, S. Inagaki, T. Suzuki, S. Hayakawa, N. Tsuchida, T. Tsuda, and H. Fujinami, “Modeling and recognition of driving behavior based on stochastic switched arx model,” IEEE Transactions on Intelligent Transportation Systems , vol. 8, no. 4, pp. 593–606, 2007
2007
Earlier work this paper cites.
G. Chaslot, S. Bakkes, I. Szita, and P. Spronck, “Monte-carlo tree search: A new framework for game ai.” AIIDE , vol. 8, pp. 216–217, 2008
2008
Earlier work this paper cites.
M. Ono and B. C. Williams, “Iterative risk allocation: A new approach to robust model predictive control with a joint chance constraint,” in 2008 47th IEEE Conference on Decision and Control . IEEE, 2008, pp. 3427–3432
2008
Earlier work this paper cites.
M. A. Costa-Gomes, V. P. Crawford, and N. Iriberri, “Comparing models of strategic thinking in van huyck, battalio, and beil’s coordination games,” Journal of the European Economic Association , vol. 7, no. 2-3, pp. 365–376, 2009
2009
Earlier work this paper cites.
H. Von Stackelberg, Market structure and equilibrium . Springer Science & Business Media, 2010
2010
Earlier work this paper cites.
D. Silver and J. Veness, “Monte-carlo planning in large pomdps,” Advances in Neural Information Processing Systems , vol. 23, pp. 2164–2172, 2010
2010
Cited alongside, same era.
G. Agamennoni, J. I. Nieto, and E. M. Nebot, “A bayesian approach for driving behavior inference,” in 2011 IEEE Intelligent Vehicles Symposium (IV) . IEEE, 2011, pp. 595–600
2011
Cited alongside, same era.
J. R. Wright and K. Leyton-Brown, “Level-0 meta-models for predicting human behavior in games,” in Proceedings of the fifteenth ACM conference on Economics and computation , 2014, pp. 857–874
2014
Cited alongside, same era.
Y. Breitmoser, J. H. Tan, and D. J. Zizzo, “On the beliefs off the path: Equilibrium refinement due to quantal response and level-k,” Games and Economic Behavior , vol. 86, pp. 102–125, 2014
2014
Cited alongside, same era.
S. Li, N. Li, A. Girard, and I. Kolmanovsky, “Decision making in dynamic and interactive environments based on cognitive hierarchy theory, bayesian inference, and predictive control,” in 2019 IEEE 58th Conference on Decision and Control (CDC) . IEEE, 2019, pp. 2181–2187
2019
Later among the works it cites.
S. Dai, S. Schaffert, A. Jasour, A. Hofmann, and B. Williams, “Chance constrained motion planning for high-dimensional robots,” in 2019 International Conference on Robotics and Automation (ICRA) . IEEE, 2019, pp. 8805–8811
2019
Later among the works it cites.
M. Bouton, A. Nakhaei, D. Isele, K. Fujimura, and M. J. Kochenderfer, “Reinforcement learning with iterative reasoning for merging in dense traffic,” in 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) . IEEE, 2020, pp. 1–6
2020
Later among the works it cites.
M. Naumann, L. Sun, W. Zhan, and M. Tomizuka, “Analyzing the suitability of cost functions for explaining and imitating human driving behavior based on inverse reinforcement learning,” in 2020 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2020, pp. 5481–5487
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
A. Dosovitskiy, G. Ros, F. Codevilla, A. Lopez, and V. Koltun, “CARLA: An open urban driving simulator,” in Proceedings of the 1st Annual Conference on Robot Learning , 2017, pp. 1–16
2017
Cited alongside, same era.
A. Dreves and M. Gerdts, “A generalized nash equilibrium approach for optimal control problems of autonomous cars,” Optimal Control Applications and Methods , vol. 39, no. 1, pp. 326–342, 2018
2018
Cited alongside, same era.
G. Williams, B. Goldfain, P. Drews, J. M. Rehg, and E. A. Theodorou, “Best response model predictive control for agile interactions between autonomous ground vehicles,” in 2018 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2018, pp. 2403–2410
2018
Cited alongside, same era.
R. P. Bhattacharyya, D. J. Phillips, C. Liu, J. K. Gupta, K. Driggs-Campbell, and M. J. Kochenderfer, “Simulating emergent properties of human driving behavior using multi-agent reward augmented imitation learning,” in 2019 International Conference on Robotics and Automation (ICRA) . IEEE, 2019, pp. 789–795
2019
Cited alongside, same era.
D. Isele, “Interactive decision making for autonomous vehicles in dense traffic,” in 2019 IEEE Intelligent Transportation Systems Conference (ITSC) . IEEE, 2019, pp. 3981–3986
2019
Cited alongside, same era.
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) . IEEE, 2019, pp. 9590–9596
2019
Cited alongside, same era.
Q. Zhang, R. Langari, H. E. Tseng, D. Filev, S. Szwabowski, and S. Coskun, “A game theoretic model predictive controller with aggressiveness estimation for mandatory lane change,” IEEE Transactions on Intelligent Vehicles , vol. 5, no. 1, pp. 75–89, 2019
2019
Cited alongside, same era.
A. Liniger and J. Lygeros, “A noncooperative game approach to autonomous racing,” IEEE Transactions on Control Systems Technology , vol. 28, no. 3, pp. 884–897, 2019
2019
Cited alongside, same era.
2020
Later among the works it cites.
2020
Later among the works it cites.
P. Hang, C. Lv, Y. Xing, C. Huang, and Z. Hu, “Human-like decision making for autonomous driving: A noncooperative game theoretic approach,” IEEE Transactions on Intelligent Transportation Systems , vol. 22, no. 4, pp. 2076–2087, 2020
2020
Later among the works it cites.
S. Bae, D. Saxena, A. Nakhaei, C. Choi, K. Fujimura, and S. Moura, “Cooperation-aware lane change maneuver in dense traffic based on model predictive control with recurrent neural network,” in 2020 American Control Conference (ACC) . IEEE, 2020, pp. 1209–1216
2020
Later among the works it cites.
R. Tian, L. Sun, M. Tomizuka, and D. Isele, “Anytime game-theoretic planning with active reasoning about human’s latent states for human-centered robots,” in 2021 International Conference on Robotics and Automation (ICRA) . IEEE, 2021
2021
Later among the works it cites.
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 , 2021
2021
Later among the works it cites.
Z. Zhang and J. F. Fisac, “Safe Occlusion-Aware Autonomous Driving via Game-Theoretic Active Perception,” in Proceedings of Robotics: Science and Systems , Virtual, July 2021
2021
Later among the works it cites.
M. Lutter, S. Mannor, J. Peters, D. Fox, and A. Garg, “Value iteration in continuous actions, states and time,” in Proceedings of the 38th International Conference on Machine Learning , 2021, pp. 7224–7234
2021
Later among the works it cites.