Fetching the paper…
Reading the bibliography…
Behavior-related research areas such as motion prediction/planning, representation/imitation learning, behavior modeling/generation, and algorithm testing, require support from high-quality motion datasets containing interactive driving scenarios with different driving cultures.
1902
Earlier work this paper cites.
D. C. Brown, “Close-range camera calibration,” PHOTOGRAMMETRIC ENGINEERING , vol. 37, no. 8, pp. 855–866, 1971
1971
Earlier work this paper cites.
D. Pelleg, A. W. Moore et al. , “X-means: Extending k-means with efficient estimation of the number of clusters.” in ICML , vol. 1, 2000, pp. 727–734
2000
Earlier work this paper cites.
V. Alexiadis, J. Colyar, J. Halkias, R. Hranac, and G. McHale, “The Next Generation Simulation Program,” Institute of Transportation Engineers. ITE Journal; Washington , vol. 74, no. 8, pp. 22–26, Aug. 2004
2004
Earlier work this paper cites.
Z. Kim, G. Gomes, R. Hranac, and A. Skabardonis, “A machine vision system for generating vehicle trajectories over extended freeway segments,” in 12th World Congress on Intelligent Transportation Systems , 2005
2005
Earlier work this paper cites.
V. L. Neale, T. A. Dingus, S. G. Klauer, J. Sudweeks, and M. Goodman, “An overview of the 100-car naturalistic study and findings,” National Highway Traffic Safety Administration, Paper , vol. 5, p. 0400, 2005
2005
Earlier work this paper cites.
D. Simon, Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches . New York, NY, USA: Wiley-Interscience, 2006
2006
Earlier work this paper cites.
H. Okuda, N. Ikami, T. Suzuki, Y. Tazaki, and K. Takeda, “Modeling and Analysis of Driving Behavior Based on a Probability-Weighted ARX Model,” IEEE Transactions on Intelligent Transportation Systems , vol. 14, no. 1, pp. 98–112, Mar. 2013
2013
Earlier work this paper cites.
S. Lefèvre, D. Vasquez, and C. Laugier, “A survey on motion prediction and risk assessment for intelligent vehicles,” ROBOMECH Journal , vol. 1, no. 1, pp. 1–14, Jul. 2014
2014
Earlier work this paper cites.
S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks,” in Advances in Neural Information Processing Systems , 2015, pp. 91–99
2015
Earlier work this paper cites.
T. Gu, J. Atwood, C. Dong, J. M. Dolan, and J.-W. Lee, “Tunable and stable real-time trajectory planning for urban autonomous driving,” in 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2015, pp. 250–256
2015
Earlier work this paper cites.
A. Robicquet, A. Sadeghian, A. Alahi, and S. Savarese, “Learning Social Etiquette: Human Trajectory Understanding In Crowded Scenes,” in ECCV 2016 . Springer International Publishing, 2016, pp. 549–565
2016
Earlier work this paper cites.
W. Zhan, C. Liu, C. Y. Chan, and M. Tomizuka, “A non-conservatively defensive strategy for urban autonomous driving,” in 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC) , pp. 459–464
2016
Earlier work this paper cites.
K. Driggs-Campbell, V. Govindarajan, and R. Bajcsy, “Integrating Intuitive Driver Models in Autonomous Planning for Interactive Maneuvers,” IEEE Transactions on Intelligent Transportation Systems , vol. 18, no. 12, pp. 3461–3472, Dec. 2017
2017
Earlier work this paper cites.
F. Altché and A. de La Fortelle, “An LSTM network for highway trajectory prediction,” in 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC) , Oct. 2017, pp. 353–359
2017
Earlier work this paper cites.
B. Coifman and L. Li, “A critical evaluation of the Next Generation Simulation (NGSIM) vehicle trajectory dataset,” Transportation Research Part B: Methodological , vol. 105, pp. 362–377, Nov. 2017
2017
Cited alongside, same era.
K. He, G. Gkioxari, P. Dollár, and R. B. Girshick, “Mask R-CNN,” 2017 IEEE International Conference on Computer Vision (ICCV) , pp. 2980–2988, 2017
2017
Cited alongside, same era.
E. Bochinski, V. Eiselein, and T. Sikora, “High-speed tracking-by-detection without using image information,” in International Workshop on Traffic and Street Surveillance for Safety and Security at IEEE AVSS 2017 , Lecce, Italy, Aug. 2017. [Online]. Available: http://elvera.nue.tu-berlin.de/files/1517Bochinski2017.pdf
2017
Cited alongside, same era.
P. Polack, F. Altché, B. d’Andréa-Novel, and A. de La Fortelle, “The kinematic bicycle model: A consistent model for planning feasible trajectories for autonomous vehicles?” in 2017 IEEE Intelligent Vehicles Symposium (IV) , June 2017, pp. 812–818
V. Ramanishka, Y.-T. Chen, T. Misu, and K. Saenko, “Toward Driving Scene Understanding: A Dataset for Learning Driver Behavior and Causal Reasoning,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 7699–7707
2018
Later among the works it cites.
A. Lukežič, T. Voj’iř, L. Čehovin Zajc, J. Matas, and M. Kristan, “Discriminative correlation filter tracker with channel and spatial reliability,” International Journal of Computer Vision , 2018
2018
Later among the works it cites.
F. Poggenhans, J. Pauls, J. Janosovits, S. Orf, M. Naumann, F. Kuhnt, and M. Mayr, “Lanelet2: A high-definition map framework for the future of automated driving,” in 2018 21st International Conference on Intelligent Transportation Systems (ITSC) , Nov. 2018, pp. 1672–1679
2018
Later among the works it cites.
L. Sun, C. Peng, W. Zhan, and M. Tomizuka, “A Fast Integrated Planning and Control Framework for Autonomous Driving via Imitation Learning,” in ASME 2018 Dynamic Systems and Control Conference . American Society of Mechanical Engineers, Sep. 2018, pp. 1–11
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2017
Cited alongside, same era.
W. Zhan, J. Chen, C. Y. Chan, C. Liu, and M. Tomizuka, “Spatially-partitioned environmental representation and planning architecture for on-road autonomous driving,” in 2017 IEEE Intelligent Vehicles Symposium (IV) , Jun. 2017, pp. 632–639
2017
Cited alongside, same era.
W. Zhan, A. de La Fortelle, Y.-T. Chen, C.-Y. Chan, and M. Tomizuka, “Probabilistic prediction from planning perspective: Problem formulation, representation simplification and evaluation metric,” in Intelligent Vehicles Symposium (IV), 2018 IEEE , 2018, pp. 1150–1156
2018
Cited alongside, same era.
Q. Lin, Y. Zhang, S. Verwer, and J. Wang, “MOHA: A Multi-Mode Hybrid Automaton Model for Learning Car-Following Behaviors,” IEEE Transactions on Intelligent Transportation Systems , pp. 1–8, 2018
2018
Cited alongside, same era.
2018
Cited alongside, same era.
N. Rhinehart, R. McAllister, and S. Levine, “Deep Imitative Models for Flexible Inference, Planning, and Control,” Oct. 2018
2018
Cited alongside, same era.
L. Sun, W. Zhan, M. Tomizuka, and A. D. Dragan, “Courteous autonomous cars,” in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2018, pp. 663–670
2018
Cited alongside, same era.
C. Guo, K. Kidono, R. Terashima, and Y. Kojima, “Toward Human-like Behavior Generation in Urban Environment Based on Markov Decision Process With Hybrid Potential Maps,” in 2018 IEEE Intelligent Vehicles Symposium (IV) , Jun. 2018, pp. 2209–2215
2018
Cited alongside, same era.
M. Naumann, M. Lauer, and C. Stiller, “Generating Comfortable, Safe and Comprehensible Trajectories for Automated Vehicles in Mixed Traffic,” in Proc. IEEE Intl. Conf. Intelligent Transportation Systems , Hawaii, USA, Nov 2018, pp. 575–582
2018
Cited alongside, same era.
2018
Later among the works it cites.
J. Schulz, C. Hubmann, J. Löchner, and D. Burschka, “Interaction-Aware Probabilistic Behavior Prediction in Urban Environments,” in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , Oct. 2018, pp. 3999–4006
2018
Later among the works it cites.
T. Shu, Y. Peng, L. Fan, H. Lu, and S.-C. Zhu, “Perception of human interaction based on motion trajectories: From aerial videos to decontextualized animations,” Topics in cognitive science , vol. 10, no. 1, pp. 225–241, 2018
2018
Later among the works it cites.
2019
Closest in time.
M. Kelly, C. Sidrane, K. Driggs-Campbell, and M. J. Kochenderfer, “HG-DAgger: Interactive Imitation Learning with Human Experts,” to appear in IEEE International Conference on Robotics and Automation (ICRA) , 2019
2019
Closest in time.
K. Messaoud, I. Yahiaoui, A. Verroust-Blondet, and F. Nashashibi, “Relational recurrent neural networks for vehicle trajectory prediction,” in 2019 IEEE Intelligent Transportation Systems Conference (ITSC) , 2019
2019
Closest in time.
M.-F. Chang, J. Lambert, P. Sangkloy, J. Singh, S. Bak, A. Hartnett, D. Wang, P. Carr, S. Lucey, D. Ramanan, and J. Hays, “Argoverse: 3d Tracking and Forecasting With Rich Maps,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 8748–8757
2019
Closest in time.
A. Clausse, S. Benslimane, and A. De La Fortelle, “Large-scale extraction of accurate vehicle trajectories for driving behavior learning,” 30th IEEE Intelligent Vehicles Symposium (IV) , 2019
2019
Closest in time.
W. Zhan, L. Sun, D. Wang, Y. Jin, and M. Tomizuka, “Constructing a Highly Interactive Vehicle Motion Dataset,” in 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2019
2019
Closest in time.
H. Ma, J. Li, W. Zhan, and M. Tomizuka, “Wasserstein Generative Learning with Kinematic Constraints for Probabilistic Prediction of Interactive Driving Behavior,” in 2019 IEEE Intelligent Vehicles Symposium , 2019
2019
Closest in time.
Y. Hu, L. Sun, and M. Tomizuka, “Generic prediction architecture considering both rational and irrational driving behaviors,” in 2019 22st International Conference on Intelligent Transportation Systems (ITSC), to appear , 2019
2019
Closest in time.
L. Sun, W. Zhan, Y. Hu, and M. Tomizuka, “Interpretable modelling of driving behaviors in interactive driving scenarios based on cumulative prospect theory,” in 2019 IEEE Intelligent Transportation Systems Conference (ITSC) , 2019
2019
Closest in time.