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Anticipating possible behaviors of traffic participants is an essential capability of autonomous vehicles.
1903
Earlier work this paper cites.
H. M. Mandalia and M. D. D. Salvucci, “Using support vector machines for lane-change detection,” in Proceedings of the Human Factors and Ergonomics Society Annual Meeting , vol. 49, no. 22. SAGE Publications Sage CA: Los Angeles, CA, 2005, pp. 1965–1969
1969
Earlier work this paper cites.
J. A. Nelder and R. J. Baker, “Generalized linear models,” Encyclopedia of Statistical Sciences , vol. 4, 2004
2004
Earlier work this paper cites.
J. Quiñonero-Candela and C. E. Rasmussen, “A unifying view of sparse approximate gaussian process regression,” Journal of Machine Learning Research , vol. 6, no. Dec, pp. 1939–1959, 2005
2005
Earlier work this paper cites.
T. Toledo and D. Zohar, “Modeling duration of lane changes,” Transportation Research Record , vol. 1999, no. 1, pp. 71–78, 2007
2007
Earlier work this paper cites.
J. M. Wang, D. J. Fleet, and A. Hertzmann, “Gaussian process dynamical models for human motion,” IEEE transactions on Pattern Analysis and Machine Intelligence , vol. 30, no. 2, pp. 283–298, 2008
2008
Earlier work this paper cites.
S. Lefèvre, C. Laugier, and J. Ibañez-Guzmán, “Evaluating risk at road intersections by detecting conflicting intentions,” in Intelligent Robots and Systems (IROS), 2012 IEEE/RSJ International Conference on . IEEE, 2012, pp. 4841–4846
2012
Earlier work this paper cites.
A. Houenou, P. Bonnifait, V. Cherfaoui, and W. Yao, “Vehicle trajectory prediction based on motion model and maneuver recognition,” in Intelligent Robots and Systems (IROS), 2013 IEEE/RSJ International Conference on . IEEE, 2013, pp. 4363–4369
2013
Earlier work this paper cites.
J. Schlechtriemen, A. Wedel, J. Hillenbrand, G. Breuel, and K.-D. Kuhnert, “A lane change detection approach using feature ranking with maximized predictive power,” in Intelligent Vehicles Symposium Proceedings, 2014 IEEE . IEEE, 2014, pp. 108–114
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
T. Gindele, S. Brechtel, and R. Dillmann, “Learning driver behavior models from traffic observations for decision making and planning,” IEEE Intelligent Transportation Systems Magazine , vol. 7, no. 1, pp. 69–79, 2015
2015
Cited alongside, same era.
A. G. Cunningham, E. Galceran, R. M. Eustice, and E. Olson, “MPDM: Multipolicy decision-making in dynamic, uncertain environments for autonomous driving,” in Robotics and Automation (ICRA), 2015 IEEE International Conference on . IEEE, 2015, pp. 1670–1677
2015
Cited alongside, same era.
E. Galceran, A. G. Cunningham, R. M. Eustice, and E. Olson, “Multipolicy decision-making for autonomous driving via changepoint-based behavior prediction.” in Robotics: Science and Systems , vol. 1, no. 2, 2015
2015
Cited alongside, same era.
A. Alahi, K. Goel, V. Ramanathan, A. Robicquet, L. Fei-Fei, and S. Savarese, “Social LSTM: Human trajectory prediction in crowded spaces,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2016, pp. 961–971
2017
Later among the works it cites.
A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer, “Automatic differentiation in pytorch,” 2017
2017
Later among the works it cites.
2018
Later among the works it cites.
2018
Later among the works it cites.
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2016
Cited alongside, same era.
2016
Cited alongside, same era.
H. Woo, Y. Ji, H. Kono, Y. Tamura, Y. Kuroda, T. Sugano, Y. Yamamoto, A. Yamashita, and H. Asama, “Lane-change detection based on vehicle-trajectory prediction,” IEEE Robotics and Automation Letters , vol. 2, no. 2, pp. 1109–1116, 2017
2017
Cited alongside, same era.
N. Lee, W. Choi, P. Vernaza, C. B. Choy, P. H. Torr, and M. Chandraker, “Desire: Distant future prediction in dynamic scenes with interacting agents,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2017, pp. 336–345
2017
Cited alongside, same era.
A. Zyner, S. Worrall, J. Ward, and E. Nebot, “Long short term memory for driver intent prediction,” in Intelligent Vehicles Symposium (IV), 2017 IEEE . IEEE, 2017, pp. 1484–1489
2017
Cited alongside, same era.
H. Q. Dang, J. Fürnkranz, A. Biedermann, and M. Hoepfl, “Time-to-lane-change prediction with deep learning,” in Intelligent Transportation Systems (ITSC), 2017 IEEE 20th International Conference on . IEEE, 2017, pp. 1–7
2017
Cited alongside, same era.
2017
Cited alongside, same era.
U. D. of Transportation Intelligent Transportation Systems Joint Program Office (JPO), “Next generation simulation (ngsim) vehicle trajectories and supporting data,” [Online] Available: https://www.its.dot.gov/data/
Cited in the paper.
N. Deo and M. M. Trivedi, “Convolutional social pooling,” [Online] Available: https://github.com/nachiket92/conv-social-pooling
Cited in the paper.
N. Deo, A. Rangesh, and M. M. Trivedi, “How would surround vehicles move? A unified framework for maneuver classification and motion prediction,” IEEE Transactions on Intelligent Vehicles , vol. 3, no. 2, pp. 129–140, 2018
2018
Later among the works it cites.
2018
Later among the works it cites.
2018
Later among the works it cites.
H. Xue, D. Q. Huynh, and M. Reynolds, “SS-LSTM: A hierarchical LSTM model for pedestrian trajectory prediction,” in 2018 IEEE Winter Conference on Applications of Computer Vision (WACV) . IEEE, 2018, pp. 1186–1194
2018
Later among the works it cites.
W. Ding and S. Shen, “Online vehicle trajectory prediction using policy anticipation network and optimization-based context reasoning,” in Proc. of the IEEE Intl. Conf. on Robot. and Autom. IEEE, 2019
2019
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