Fetching the paper…
Reading the bibliography…
Autonomous vehicle safety is crucial for the successful deployment of self-driving cars.
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.
M. Saerens, P. Latinne, and C. Decaestecker, “Adjusting the outputs of a classifier to new a priori probabilities: a simple procedure,” Neural computation , vol. 14, no. 1, pp. 21–41, 2002
2002
Earlier work this paper cites.
J. Maroto, E. Delso, J. Felez, and J. M. Cabanellas, “Real-time traffic simulation with a microscopic model,” IEEE Transactions on Intelligent Transportation Systems , vol. 7, no. 4, pp. 513–527, 2006
2006
Earlier work this paper cites.
S. Thrun, M. Montemerlo, H. Dahlkamp, D. Stavens, A. Aron, J. Diebel, P. Fong, J. Gale, M. Halpenny, G. Hoffmann, et al. , “Stanley: The robot that won the darpa grand challenge,” Journal of field Robotics , vol. 23, no. 9, pp. 661–692, 2006
2006
Earlier work this paper cites.
J. Leonard, J. How, S. Teller, M. Berger, S. Campbell, G. Fiore, L. Fletcher, E. Frazzoli, A. Huang, S. Karaman, et al. , “A perception-driven autonomous urban vehicle,” Journal of Field Robotics , vol. 25, no. 10, pp. 727–774, 2008
2008
Earlier work this paper cites.
C. Urmson, J. Anhalt, D. Bagnell, C. Baker, R. Bittner, M. Clark, J. Dolan, D. Duggins, T. Galatali, C. Geyer, et al. , “Autonomous driving in urban environments: Boss and the urban challenge,” Journal of field Robotics , vol. 25, no. 8, pp. 425–466, 2008
2008
Earlier work this paper cites.
L. G. Papaleondiou and M. D. Dikaiakos, “Trafficmodeler: A graphical tool for programming microscopic traffic simulators through high-level abstractions,” in VTC Spring 2009-IEEE 69th Vehicular Technology Conference . IEEE, 2009, pp. 1–5
2009
Earlier work this paper cites.
S. Ben-David, J. Blitzer, K. Crammer, A. Kulesza, F. Pereira, and J. W. Vaughan, “A theory of learning from different domains,” Machine learning , vol. 79, pp. 151–175, 2010
2010
Earlier work this paper cites.
F. E. Gunawan, “Two-vehicle dynamics of the car-following models on realistic driving condition,” Journal of Transportation Systems Engineering and Information Technology , vol. 12, no. 2, pp. 67–75, 2012
2012
Earlier work this paper cites.
A. Gretton, K. M. Borgwardt, M. J. Rasch, B. Schölkopf, and A. Smola, “A kernel two-sample test,” The Journal of Machine Learning Research , vol. 13, no. 1, pp. 723–773, 2012
2012
Earlier work this paper cites.
J. Erdmann, “Sumo’s lane-changing model,” in Modeling Mobility with Open Data: 2nd SUMO Conference 2014 Berlin, Germany, May 15-16, 2014 . Springer, 2015, pp. 105–123
2015
Earlier work this paper cites.
G. Ros, L. Sellart, J. Materzynska, D. Vazquez, and A. M. Lopez, “The synthia dataset: A large collection of synthetic images for semantic segmentation of urban scenes,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 3234–3243
2016
Earlier work this paper cites.
“Crash injury research engineering network.” https://crashviewer.nhtsa.dot.gov/CIREN/SearchIndex, 2016
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
A. Dosovitskiy, G. Ros, F. Codevilla, A. Lopez, and V. Koltun, “Carla: An open urban driving simulator,” in Conference on robot learning . PMLR, 2017, pp. 1–16
2017
Earlier work this paper cites.
I. Loshchilov, “Decoupled weight decay regularization,” arXiv preprint arXiv:1711.05101 , 2017
2017
Earlier work this paper cites.
2018
Earlier work this paper cites.
P. A. Lopez, M. Behrisch, L. Bieker-Walz, J. Erdmann, Y.-P. Flötteröd, R. Hilbrich, L. Lücken, J. Rummel, P. Wagner, and E. Wießner, “Microscopic traffic simulation using sumo,” in 2018 21st international conference on intelligent transportation systems (ITSC) . IEEE, 2018, pp. 2575–2582
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
2018
Cited alongside, same era.
W. Zeng, W. Luo, S. Suo, A. Sadat, B. Yang, S. Casas, and R. Urtasun, “End-to-end interpretable neural motion planner,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 8660–8669
2019
Cited alongside, same era.
A. Prakash, S. Boochoon, M. Brophy, D. Acuna, E. Cameracci, G. State, O. Shapira, and S. Birchfield, “Structured domain randomization: Bridging the reality gap by context-aware synthetic data,” in 2019 International Conference on Robotics and Automation (ICRA) . IEEE, 2019, pp. 7249–7255
2019
Cited alongside, same era.
A. Sadat, S. Casas, M. Ren, X. Wu, P. Dhawan, and R. Urtasun, “Perceive, predict, and plan: Safe motion planning through interpretable semantic representations,” in Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIII 16 . Springer, 2020, pp. 414–430
2023
Later among the works it cites.
H. Shao, L. Wang, R. Chen, H. Li, and Y. Liu, “Safety-enhanced autonomous driving using interpretable sensor fusion transformer,” in Conference on Robot Learning . PMLR, 2023, pp. 726–737
2023
Later among the works it cites.
L. Feng, Q. Li, Z. Peng, S. Tan, and B. Zhou, “Trafficgen: Learning to generate diverse and realistic traffic scenarios,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, pp. 3567–3575
2023
Later among the works it cites.
2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2020
Cited alongside, same era.
J. Zhou, R. Wang, X. Liu, Y. Jiang, S. Jiang, J. Tao, J. Miao, and S. Song, “Exploring imitation learning for autonomous driving with feedback synthesizer and differentiable rasterization,” in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2021, pp. 1450–1457
2021
Cited alongside, same era.
A. Cui, S. Casas, A. Sadat, R. Liao, and R. Urtasun, “Lookout: Diverse multi-future prediction and planning for self-driving,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 16 107–16 116
2021
Cited alongside, same era.
S. Casas, A. Sadat, and R. Urtasun, “Mp3: A unified model to map, perceive, predict and plan,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 14 403–14 412
2021
Cited alongside, same era.
S. Ettinger, S. Cheng, B. Caine, C. Liu, H. Zhao, S. Pradhan, Y. Chai, B. Sapp, C. R. Qi, Y. Zhou, et al. , “Large scale interactive motion forecasting for autonomous driving: The waymo open motion dataset,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 9710–9719
2021
Cited alongside, same era.
S. Tan, K. Wong, S. Wang, S. Manivasagam, M. Ren, and R. Urtasun, “Scenegen: Learning to generate realistic traffic scenes,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 892–901
2021
Cited alongside, same era.
2021
Cited alongside, same era.
H. Liu, Z. Huang, J. Wu, and C. Lv, “Improved deep reinforcement learning with expert demonstrations for urban autonomous driving,” in 2022 IEEE intelligent vehicles symposium (IV) . IEEE, 2022, pp. 921–928
2022
Cited alongside, same era.
Z. Huang, J. Wu, and C. Lv, “Efficient deep reinforcement learning with imitative expert priors for autonomous driving,” IEEE Transactions on Neural Networks and Learning Systems , vol. 34, no. 10, pp. 7391–7403, 2022
2022
Cited alongside, same era.
N. Montali, J. Lambert, P. Mougin, A. Kuefler, N. Rhinehart, M. Li, C. Gulino, T. Emrich, Z. Yang, S. Whiteson, B. White, and D. Anguelov, “The waymo open sim agents challenge,” in Advances in Neural Information Processing Systems Track on Datasets and Benchmarks , 2023
2023
Later among the works it cites.
R. OpenAI, “Gpt-4 technical report. arxiv 2303.08774,” View in Article , vol. 2, no. 5, 2023
2023
Later among the works it cites.
D. Dauner, M. Hallgarten, A. Geiger, and K. Chitta, “Parting with misconceptions about learning-based vehicle motion planning,” in Conference on Robot Learning . PMLR, 2023, pp. 1268–1281
2023
Later among the works it cites.
S. Pini, C. S. Perone, A. Ahuja, A. S. R. Ferreira, M. Niendorf, and S. Zagoruyko, “Safe real-world autonomous driving by learning to predict and plan with a mixture of experts,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, pp. 10 069–10 075
2023
Later among the works it cites.
O. Contributors, “Openscene: The largest up-to-date 3d occupancy prediction benchmark in autonomous driving,” https://github.com/OpenDriveLab/OpenScene , 2023
2023
Later among the works it cites.
J. Cheng, Y. Chen, X. Mei, B. Yang, B. Li, and M. Liu, “Rethinking imitation-based planners for autonomous driving,” in 2024 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2024, pp. 14 123–14 130
2024
Later among the works it cites.
2024
Later among the works it cites.
2024
Later among the works it cites.
Z. Li, Z. Yu, S. Lan, J. Li, J. Kautz, T. Lu, and J. M. Alvarez, “Is ego status all you need for open-loop end-to-end autonomous driving?” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 14 864–14 873
2024
Later among the works it cites.
2024
Later among the works it cites.
J. Philion, X. B. Peng, and S. Fidler, “Trajeglish: Traffic modeling as next-token prediction,” in The Twelfth International Conference on Learning Representations , 2024
2024
Later among the works it cites.
2024
Later among the works it cites.
2024
Later among the works it cites.
2024
Later among the works it cites.
D. Dauner, M. Hallgarten, T. Li, X. Weng, Z. Huang, Z. Yang, H. Li, I. Gilitschenski, B. Ivanovic, M. Pavone, A. Geiger, and K. Chitta, “Navsim: Data-driven non-reactive autonomous vehicle simulation and benchmarking,” arXiv , vol. 2406.15349, 2024
2024
Later among the works it cites.