Fetching the paper…
Reading the bibliography…
Trajectory prediction using deep neural networks (DNNs) is an essential component of autonomous driving (AD) systems.
Motion planning for autonomous driving with a conformal spatiotemporal lattice
M. McNaughton, C. Urmson, J. M. Dolan, and J.-W. Lee · 2011
Earlier work this paper cites.
The mnist database of handwritten digit images for machine learning research
L. Deng · 2012
Earlier work this paper cites.
Intriguing properties of neural networks
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus · 2013
Earlier work this paper cites.
Model predictive control
E. F. Camacho and C. B. Alba · 2013
Earlier work this paper cites.
Social lstm: Human trajectory prediction in crowded spaces
A. Alahi, K. Goel, V. Ramanathan, A. Robicquet, L. Fei-Fei, and S. Savarese · 2016
Earlier work this paper cites.
Distillation as a defense to adversarial perturbations against deep neural networks
N. Papernot, P. McDaniel, X. Wu, S. Jha, and A. Swami · 2016
Earlier work this paper cites.
Towards deep learning models resistant to adversarial attacks
A. Madry, A. Makelov, L. Schmidt, D. Tsipras, and A. Vladu · 2017
Earlier work this paper cites.
Towards evaluating the robustness of neural networks
N. Carlini and D. Wagner · 2017
Earlier work this paper cites.
Extending defensive distillation
N. Papernot and P. McDaniel · 2017
Earlier work this paper cites.
Feature squeezing: Detecting adversarial examples in deep neural networks
W. Xu, D. Evans, and Y. Qi · 2017
Earlier work this paper cites.
The kinematic bicycle model: A consistent model for planning feasible trajectories for autonomous vehicles?
P. Polack, F. Altché, B. d’Andréa Novel, and A. de La Fortelle · 2017
Earlier work this paper cites.
R2p2: A reparameterized pushforward policy for diverse, precise generative path forecasting
N. Rhinehart, K. M. Kitani, and P. Vernaza · 2018
Earlier work this paper cites.
Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
A. Athalye, N. Carlini, and D. Wagner · 2018
Cited alongside, same era.
Robust physical-world attacks on deep learning visual classification
K. Eykholt, I. Evtimov, E. Fernandes, B. Li, A. Rahmati, C. Xiao, A. Prakash, T. Kohno, and D. Song · 2018
Cited alongside, same era.
The trajectron: Probabilistic multi-agent trajectory modeling with dynamic spatiotemporal graphs
B. Ivanovic and M. Pavone · 2019
Cited alongside, same era.
Precog: Prediction conditioned on goals in visual multi-agent settings
N. Rhinehart, R. McAllister, K. Kitani, and S. Levine · 2019
Cited alongside, same era.
Social-bigat: Multimodal trajectory forecasting using bicycle-gan and graph attention networks
V. Kosaraju, A. Sadeghian, R. Martín-Martín, I. Reid, H. Rezatofighi, and S. Savarese · 2019
Cited alongside, same era.
Adversarial examples improve image recognition
C. Xie, M. Tan, B. Gong, J. Wang, A. L. Yuille, and Q. V. Le · 2020
Later among the works it cites.
Intriguing properties of adversarial training at scale
C. Xie and A. Yuille · 2020
Later among the works it cites.
C. Xie, M. Tan, B. Gong, A. Yuille, and Q. V. Le · 2020
Later among the works it cites.
Overfitting in adversarially robust deep learning
L. Rice, E. Wong, and Z. Kolter · 2020
Later among the works it cites.
nuscenes: A multimodal dataset for autonomous driving
H. Caesar, V. Bankiti, A. H. Lang, S. Vora, V. E. Liong, Q. Xu, A. Krishnan, Y. Pan, G. Baldan, and O. Beijbom · 2020
Later among the works it cites.
Trajectory forecasts in unknown environments conditioned on grid-based plans
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Extending adversarial attacks and defenses to deep 3d point cloud classifiers
D. Liu, R. Yu, and H. Su · 2019
Cited alongside, same era.
Adversarial training for free!
A. Shafahi, M. Najibi, A. Ghiasi, Z. Xu, J. Dickerson, C. Studer, L. S. Davis, G. Taylor, and T. Goldstein · 2019
Cited alongside, same era.
Me-net: Towards effective adversarial robustness with matrix estimation
Y. Yang, G. Zhang, D. Katabi, and Z. Xu · 2019
Cited alongside, same era.
Theoretically principled trade-off between robustness and accuracy
H. Zhang, Y. Yu, J. Jiao, E. Xing, L. El Ghaoui, and M. Jordan · 2019
Cited alongside, same era.
Trajectron++: Dynamically-feasible trajectory forecasting with heterogeneous data
T. Salzmann, B. Ivanovic, P. Chakravarty, and M. Pavone · 2020
Cited alongside, same era.
A simple fine-tuning is all you need: Towards robust deep learning via adversarial fine-tuning
A. Jeddi, M. J. Shafiee, and A. Wong · 2020
Cited alongside, same era.
Fast is better than free: Revisiting adversarial training
E. Wong, L. Rice, and J. Z. Kolter · 2020
Cited alongside, same era.
N. Deo and M. M. Trivedi · 2020
Later among the works it cites.
Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks
F. Croce and M. Hein · 2020
Later among the works it cites.
Agentformer: Agent-aware transformers for socio-temporal multi-agent forecasting
Y. Yuan, X. Weng, Y. Ou, and K. Kitani · 2021
Later among the works it cites.
Fixing data augmentation to improve adversarial robustness
S.-A. Rebuffi, S. Gowal, D. A. Calian, F. Stimberg, O. Wiles, and T. Mann · 2021
Later among the works it cites.
Nvidia a100 tensor core gpu: Performance and innovation
J. Choquette, W. Gandhi, O. Giroux, N. Stam, and R. Krashinsky · 2021
Later among the works it cites.
On adversarial robustness of trajectory prediction for autonomous vehicles
Q. Zhang, S. Hu, J. Sun, Q. A. Chen, and Z. M. Mao · 2022
Closest in time.
Salt-and-pepper noise — Wikipedia, the free encyclopedia, 2022
Wikipedia contributors · 2022
Closest in time.