Fetching the paper…
Reading the bibliography…
Planning an optimal route in a complex environment requires efficient reasoning about the surrounding scene.
An iterative procedure for the polygonal approximation of plane curves
U. Ramer · 1972
Earlier work this paper cites.
Algorithms for the reduction of the number of points required to represent a digitized line or its caricature
D. H. Douglas and T. K. Peucker · 1973
Earlier work this paper cites.
Vision: A computational investigation into the human representation and processing of visual information
D. Marr · 1982
Earlier work this paper cites.
Vision and navigation for the carnegie-mellon navlab
C. Thorpe, M. H. Hebert, T. Kanade, and S. A. Shafer · 1988
Earlier work this paper cites.
The seeing passenger car ’vamors-p’
E. D. Dickmanns, R. Behringer, D. Dickmanns, T. Hildebrandt, M. Maurer, F. Thomanek, and J. Schiehlen · 1994
Earlier work this paper cites.
Core knowledge
E. S. Spelke and K. D. Kinzler · 2007
Earlier work this paper cites.
A perception-driven autonomous urban vehicle
J. J. Leonard, J. P. How, S. J. Teller, M. Berger, S. Campbell, G. A. Fiore, L. Fletcher, E. Frazzoli, A. S. Huang, S. Karaman, O. Koch, Y. Kuwata, D. Moore, E. Olson, S. Peters, J. Teo, R. Truax, M. R. Walter, D. Barrett, A. Epstein, K. Maheloni, K. Moyer, T. Jones, R. Buckley, M. E. Antone, R. Galejs, S. Krishnamurthy, and J. Williams · 2008
Earlier work this paper cites.
Motion planning under uncertainty for on-road autonomous driving
W. Xu, J. Pan, J. Wei, and J. M. Dolan · 2014
Earlier work this paper cites.
Visualizing and understanding convolutional networks
M. D. Zeiler and R. Fergus · 2014
Earlier work this paper cites.
Learning phrase representations using RNN encoder-decoder for statistical machine translation
K. Cho, B. van Merrienboer, Ç. Gülçehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio · 2014
Earlier work this paper cites.
Deepdriving: Learning affordance for direct perception in autonomous driving
C. Chen, A. Seff, A. L. Kornhauser, and J. Xiao · 2015
Earlier work this paper cites.
A survey of motion planning and control techniques for self-driving urban vehicles
B. Paden, M. Čáp, S. Z. Yong, D. Yershov, and E. Frazzoli · 2016
Earlier work this paper cites.
Learning deep features for discriminative localization
B. Zhou, A. Khosla, A. Lapedriza, A. Oliva, and A. Torralba · 2016
Earlier work this paper cites.
Attention is all you need
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin · 2017
Earlier work this paper cites.
CARLA: An open urban driving simulator
A. Dosovitskiy, G. Ros, F. Codevilla, A. Lopez, and V. Koltun · 2017
Earlier work this paper cites.
Grad-cam: Visual explanations from deep networks via gradient-based localization
R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra · 2017
Earlier work this paper cites.
Real time image saliency for black box classifiers
P. Dabkowski and Y. Gal · 2017
Earlier work this paper cites.
Interpretable explanations of black boxes by meaningful perturbation
R. C. Fong and A. Vedaldi · 2017
Earlier work this paper cites.
Interpretable learning for self-driving cars by visualizing causal attention
J. Kim and J. Canny · 2017
Earlier work this paper cites.
Decoupled weight decay regularization
I. Loshchilov and F. Hutter · 2017
Earlier work this paper cites.
Baidu apollo EM motion planner
H. Fan, F. Zhu, C. Liu, L. Zhang, L. Zhuang, D. Li, W. Zhu, J. Hu, H. Li, and Q. Kong · 2018
Earlier work this paper cites.
Object perception
S. P. Johnson · 2018
Earlier work this paper cites.
Driving policy transfer via modularity and abstraction
M. Müller, A. Dosovitskiy, B. Ghanem, and V. Koltun · 2018
Earlier work this paper cites.
Bert: Pre-training of deep bidirectional transformers for language understanding
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova · 2018
Earlier work this paper cites.
Intentnet: Learning to predict intention from raw sensor data
S. Casas, W. Luo, and R. Urtasun · 2018
Earlier work this paper cites.
Rise: Randomized input sampling for explanation of black-box models
V. Petsiuk, A. Das, and K. Saenko · 2018
Cited alongside, same era.
Textual explanations for self-driving vehicles
J. Kim, A. Rohrbach, T. Darrell, J. Canny, and Z. Akata · 2018
Cited alongside, same era.
Learning by cheating
D. Chen, B. Zhou, V. Koltun, and P. Krähenbühl · 2019
Cited alongside, same era.
End-to-end interpretable neural motion planner
W. Zeng, W. Luo, S. Suo, A. Sadat, B. Yang, S. Casas, and R. Urtasun · 2019
Cited alongside, same era.
Exploring the limitations of behavior cloning for autonomous driving
F. Codevilla, E. Santana, A. M. López, and A. Gaidon · 2019
Cited alongside, same era.
Learning to navigate using mid-level visual priors
A. Sax, B. Emi, A. R. Zamir, L. J. Guibas, S. Savarese, and J. Malik · 2019
Cited alongside, same era.
Lookout: Diverse multi-future prediction and planning for self-driving
A. Cui, A. Sadat, S. Casas, R. Liao, and R. Urtasun · 2021
Later among the works it cites.
Multi-modal fusion transformer for end-to-end autonomous driving
A. Prakash, K. Chitta, and A. Geiger · 2021
Later among the works it cites.
End-to-end urban driving by imitating a reinforcement learning coach
Z. Zhang, A. Liniger, D. Dai, F. Yu, and L. Van Gool · 2021
Later among the works it cites.
Urban driver: Learning to drive from real-world demonstrations using policy gradients
O. Scheel, L. Bergamini, M. Wolczyk, B. Osiński, and P. Ondruska · 2021
Later among the works it cites.
Safetynet: Safe planning for real-world self-driving vehicles using machine-learned policies
M. Vitelli, Y. Chang, Y. Ye, M. Wołczyk, B. Osiński, M. Niendorf, H. Grimmett, Q. Huang, A. Jain, and P. Ondruska · 2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Visual representations for semantic target driven navigation
A. Mousavian, A. Toshev, M. Fiser, J. Kosecká, A. Wahid, and J. Davidson · 2019
Cited alongside, same era.
Does computer vision matter for action?
B. Zhou, P. Krähenbühl, and V. Koltun · 2019
Cited alongside, same era.
Monocular plan view networks for autonomous driving
D. Wang, C. Devin, Q. Cai, P. Krähenbühl, and T. Darrell · 2019
Cited alongside, same era.
Language models are unsupervised multitask learners
A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, and I. Sutskever · 2019
Cited alongside, same era.
Rules of the road: Predicting driving behavior with a convolutional model of semantic interactions
J. Hong, B. Sapp, and J. Philbin · 2019
Cited alongside, same era.
Multipath: Multiple probabilistic anchor trajectory hypotheses for behavior prediction
Y. Chai, B. Sapp, M. Bansal, and D. Anguelov · 2019
Cited alongside, same era.
K. Chitta, A. Prakash, and A. Geiger · 2021
Later among the works it cites.
Offline reinforcement learning as one big sequence modeling problem
M. Janner, Q. Li, and S. Levine · 2021
Later among the works it cites.
Decision transformer: Reinforcement learning via sequence modeling
L. Chen, K. Lu, A. Rajeswaran, K. Lee, A. Grover, M. Laskin, P. Abbeel, A. Srinivas, and I. Mordatch · 2021
Later among the works it cites.
Generalized decision transformer for offline hindsight information matching
H. Furuta, Y. Matsuo, and S. S. Gu · 2021
Later among the works it cites.
Multimodal motion prediction with stacked transformers
Y. Liu, J. Zhang, L. Fang, Q. Jiang, and B. Zhou · 2021
Later among the works it cites.
Learning by watching
J. Zhang and E. Ohn-Bar · 2021
Later among the works it cites.
Masked autoencoders are scalable vision learners
K. He, X. Chen, S. Xie, Y. Li, P. Dollár, and R. B. Girshick · 2021
Later among the works it cites.
Pylot: A modular platform for exploring latency-accuracy tradeoffs in autonomous vehicles
I. Gog, S. Kalra, P. Schafhalter, M. A. Wright, J. E. Gonzalez, and I. Stoica · 2021
Later among the works it cites.
King: Generating safety-critical driving scenarios for robust imitation via kinematics gradients
N. Hanselmann, K. Renz, K. Chitta, A. Bhattacharyya, and A. Geiger · 2022
Closest in time.
Translating Images into Maps
A. Saha, O. Mendez Maldonado, C. Russell, and R. Bowden · 2022
Closest in time.
Cross-view Transformers for real-time Map-view Semantic Segmentation
B. Zhou and P. Krähenbühl · 2022
Closest in time.
BEVSegFormer: Bird’s Eye View Semantic Segmentation From Arbitrary Camera Rigs
L. Peng, Z. Chen, Z. Fu, P. Liang, and E. Cheng · 2022
Closest in time.
M 2 BEV: Multi-Camera Joint 3D Detection and Segmentation with Unified Birds-Eye View Representation
E. Xie, Z. Yu, D. Zhou, J. Philion, A. Anandkumar, S. Fidler, P. Luo, and J. M. Alvarez · 2022
Closest in time.
BEVFormer: Learning Bird’s-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers
Z. Li, W. Wang, H. Li, E. Xie, C. Sima, T. Lu, Q. Yu, and J. Dai · 2022
Closest in time.
Transfuser: Imitation with transformer-based sensor fusion for autonomous driving
K. Chitta, A. Prakash, B. Jaeger, Z. Yu, K. Renz, , and A. Geiger · 2022
Closest in time.
Learning from all vehicles
D. Chen and P. Krähenbühl · 2022
Closest in time.
Online decision transformer
Q. Zheng, A. Zhang, and A. Grover · 2022
Closest in time.
A generalist agent
S. Reed, K. Zolna, E. Parisotto, S. G. Colmenarejo, A. Novikov, G. Barth-Maron, M. Gimenez, Y. Sulsky, J. Kay, J. T. Springenberg, et al · 2022
Closest in time.
Scene transformer: A unified architecture for predicting future trajectories of multiple agents
J. Ngiam, V. Vasudevan, B. Caine, Z. Zhang, H.-T. L. Chiang, J. Ling, R. Roelofs, A. Bewley, C. Liu, A. Venugopal, D. J. Weiss, B. Sapp, Z. Chen, and J. Shlens · 2022
Closest in time.
Control-aware prediction objectives for autonomous driving
R. McAllister, B. Wulfe, J. Mercat, L. Ellis, S. Levine, and A. Gaidon · 2022
Closest in time.