A Transferable Pedestrian Motion Prediction Model for Intersections with Different Geometries

M.-P. Dubuisson and A. K. Jain, “A modified hausdorff distance for object matching,” in Pattern Recognition, 1994. Vol. 1-Conference A: Computer Vision & Image Processing., Proceedings of the 12th IAPR International Conference on , vol. 1. IEEE, 1994, pp. 566–568

1994

Earlier work this paper cites.

A. J. Gonzalez, W. J. Gerber, R. F. DeMara, and M. Georgiopoulos, “Context-driven near-term intention recognition,” The Journal of Defense Modeling and Simulation , vol. 1, no. 3, pp. 153–170, 2004

2004

Earlier work this paper cites.

J. Joseph, F. Doshi-Velez, A. S. Huang, and N. Roy, “A bayesian nonparametric approach to modeling motion patterns,” Autonomous Robots , vol. 31, no. 4, p. 383, 2011

2011

Earlier work this paper cites.

G. S. Aoude, B. D. Luders, J. M. Joseph, N. Roy, and J. P. How, “Probabilistically safe motion planning to avoid dynamic obstacles with uncertain motion patterns,” Autonomous Robots , vol. 35, no. 1, pp. 51–76, 2013

2013

Earlier work this paper cites.

J. F. P. Kooij, N. Schneider, F. Flohr, and D. M. Gavrila, “Context-based pedestrian path prediction,” in European Conference on Computer Vision . Springer, 2014, pp. 618–633

2014

Earlier work this paper cites.

S. Bonnin, T. H. Weisswange, F. Kummert, and J. Schmüdderich, “Pedestrian crossing prediction using multiple context-based models,” in Intelligent Transportation Systems (ITSC), 2014 IEEE 17th International Conference on . IEEE, 2014, pp. 378–385

2014

Earlier work this paper cites.

A. T. Schulz and R. Stiefelhagen, “Pedestrian intention recognition using latent-dynamic conditional random fields,” in Intelligent Vehicles Symposium (IV), 2015 IEEE . IEEE, 2015, pp. 622–627

2015

Cited alongside, same era.

B. Völz, H. Mielenz, G. Agamennoni, and R. Siegwart, “Feature relevance estimation for learning pedestrian behavior at crosswalks,” in Intelligent Transportation Systems (ITSC), 2015 IEEE 18th International Conference on . IEEE, 2015, pp. 854–860

2015

Cited alongside, same era.

Y. F. Chen, M. Liu, and J. P. How, “Augmented dictionary learning for motion prediction,” in Robotics and Automation (ICRA), 2016 IEEE International Conference on . IEEE, 2016, pp. 2527–2534

2016

Cited alongside, same era.

F. Schneemann and P. Heinemann, “Context-based detection of pedestrian crossing intention for autonomous driving in urban environments,” in Intelligent Robots and Systems (IROS), 2016 IEEE/RSJ International Conference on . IEEE, 2016, pp. 2243–2248

2016

J. Miller, A. Hasfura, S.-Y. Liu, and J. P. How, “Dynamic arrival rate estimation for campus mobility on demand network graphs,” in Intelligent Robots and Systems (IROS), 2016 IEEE/RSJ International Conference on . IEEE, 2016, pp. 2285–2292

2016

Later among the works it cites.

A. Sadeghian, F. Legros, M. Voisin, R. Vesel, A. Alahi, and S. Savarese, “Car-net: Clairvoyant attentive recurrent network,” arXiv preprint arXiv:1711.10061 , 2017

Original

2017

Later among the works it cites.

J. Miller and J. P. How, “Predictive positioning and quality of service ridesharing for campus mobility on demand systems,” in Robotics and Automation (ICRA), 2017 IEEE International Conference on . IEEE, 2017, pp. 1402–1408

2017

Later among the works it cites.

N. Jaipuria, G. Habibi, and J. P. How, “Casnsc: A context-based approach for accurate pedestrian motion prediction at intersections,” in NIPS Machine Learning for Intelligent Transportation Systems Workshop (MLITS) , 2017

2017