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Machine Learning (ML) has replaced traditional handcrafted methods for perception and prediction in autonomous vehicles.
D. A. Pomerleau, “Alvinn: An autonomous land vehicle in a neural network,” in NeurIPS , 1988
1988
Earlier work this paper cites.
T. Fraichard and C. Laugier, “Path-velocity decomposition revisited and applied to dynamic trajectory planning,” in ICRA , 1993
1993
Earlier work this paper cites.
J. J. Kuffner and S. M. LaValle, “Rrt-connect: An efficient approach to single-query path planning,” in ICRA , 2000
2000
Earlier work this paper cites.
M. Treiber, A. Hennecke, and D. Helbing, “Congested traffic states in empirical observations and microscopic simulations,” Phys. Rev. E , 2000
2000
Earlier work this paper cites.
P. Abbeel and A. Y. Ng, “Apprenticeship learning via inverse reinforcement learning,” in ICML , 2004
2004
Earlier work this paper cites.
M. Riedmiller, M. Montemerlo, and H. Dahlkamp, “Learning to drive a real car in 20 minutes,” in 2007 Frontiers in the Convergence of Bioscience and Information Technologies , 2007
2007
Earlier work this paper cites.
M. Montemerlo, J. Becker, S. Bhat, H. Dahlkamp, D. Dolgov, S. Ettinger, D. Haehnel, T. Hilden, G. Hoffmann, B. Huhnke et al. , “Junior: The stanford entry in the urban challenge,” Journal of Field Robotics , 2008
2008
Earlier work this paper cites.
B. D. Ziebart, A. L. Maas, J. A. Bagnell, A. K. Dey et al. , “Maximum entropy inverse reinforcement learning.” in AAAI , 2008
2008
Earlier work this paper cites.
M. Buehler, K. Iagnemma, and S. Singh, The DARPA urban challenge: autonomous vehicles in city traffic . Springer, 2009, vol. 56
2009
Earlier work this paper cites.
S. Karaman and E. Frazzoli, “Sampling-based algorithms for optimal motion planning,” IJRR , 2011
2011
Earlier work this paper cites.
N. Aghasadeghi and T. Bretl, “Maximum entropy inverse reinforcement learning in continuous state spaces with path integrals,” in IROS , 2011
2011
Earlier work this paper cites.
O. Derbel, T. Peter, H. Zebiri, B. Mourllion, and M. Basset, “Modified intelligent driver model for driver safety and traffic stability improvement,” IFAC Proceedings Volumes , vol. 46, no. 21, pp. 744–749, 2013
2013
Earlier work this paper cites.
2015
Earlier work this paper cites.
O. Arslan and P. Tsiotras, “Machine learning guided exploration for sampling-based motion planning algorithms,” in IROS , 2015
2015
Earlier work this paper cites.
A. Venkatraman, M. Hebert, and J. Bagnell, “Improving multi-step prediction of learned time series models,” AAAI , 2015
2015
Earlier work this paper cites.
M. B. Jensen and M. Philip, “Lisa traffic light dataset,” https://www.kaggle.com/datasets/mbornoe/lisa-traffic-light-dataset/ , 2015
2015
Earlier work this paper cites.
B. Paden, M. Čáp, S. Z. Yong, D. Yershov, and E. Frazzoli, “A survey of motion planning and control techniques for self-driving urban vehicles,” IEEE Transactions on Intelligent Vehicles , 2016
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
J. Ho and S. Ermon, “Generative adversarial imitation learning,” NIPS , 2016
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
M. Althoff, M. Koschi, and S. Manzinger, “Commonroad: Composable benchmarks for motion planning on roads,” in IV , 2017
2017
Earlier work this paper cites.
A. Dosovitskiy, G. Ros, F. Codevilla, A. Lopez, and V. Koltun, “CARLA: An open urban driving simulator,” CoRR , 2017
2017
Earlier work this paper cites.
Y. Zhou and O. Tuzel, “Voxelnet: End-to-end learning for point cloud based 3d object detection,” in CVPR , 2018
2018
Earlier work this paper cites.
R. Krajewski, J. Bock, L. Kloeker, and L. Eckstein, “The highd dataset: A drone dataset of naturalistic vehicle trajectories on german highways for validation of highly automated driving systems,” in ITSC , 2018
2018
Earlier work this paper cites.
S. Shah, D. Dey, C. Lovett, and A. Kapoor, “Airsim: High-fidelity visual and physical simulation for autonomous vehicles,” in Field and Service Robotics: Results of the 11th International Conference , 2018
2018
Earlier work this paper cites.
Z. A. Daniels and D. Metaxas, “Scenarionet: An interpretable data-driven model for scene understanding,” in IJCAI Workshop on Explainable Artificial Intelligence (XAI) 2018 , 2018
2018
Earlier work this paper cites.
Z. Ajanovic, B. Lacevic, B. Shyrokau, M. Stolz, and M. Horn, “Search-based optimal motion planning for automated driving,” in IROS , 2018
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
M. Müller, A. Dosovitskiy, B. Ghanem, and V. Koltun, “Driving policy transfer via modularity and abstraction,” in CoRL , 2018
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
A. H. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang, and O. Beijbom, “Pointpillars: Fast encoders for object detection from point clouds,” in CVPR , 2019
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
M.-F. Chang, J. W. 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 CVPR , 2019
2019
Cited alongside, same era.
M. Bansal, A. Krizhevsky, and A. Ogale, “Chauffeurnet: Learning to drive by imitating the best and synthesizing the worst,” in RSS , 2019
2019
Cited alongside, same era.
F. Codevilla, E. Santana, A. M. López, and A. Gaidon, “Exploring the limitations of behavior cloning for autonomous driving,” in ICCV , 2019
2019
Cited alongside, same era.
M. Bansal, A. Krizhevsky, and A. Ogale, “Chauffeurnet: Learning to drive by imitating the best and synthesizing the worst,” in RSS , 2019
2019
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 CVPR , 2019
D. Chen, V. Koltun, and P. Krähenbühl, “Learning to drive from a world on rails,” in ICCV , 2021
2021
Later among the works it cites.
H. Pulver, F. Eiras, L. Carozza, M. Hawasly, S. V. Albrecht, and S. Ramamoorthy, “Pilot: Efficient planning by imitation learning and optimisation for safe autonomous driving,” in IROS , 2021
2021
Later among the works it cites.
S. Casas, A. Sadat, and R. Urtasun, “Mp3: A unified model to map, perceive, predict and plan,” in CVPR , 2021
2021
Later among the works it cites.
L. Chen, L. Platinsky, S. Speichert, B. Osiński, O. Scheel, Y. Ye, H. Grimmett, L. Del Pero, and P. Ondruska, “What data do we need for training an av motion planner?” in ICRA , 2021
2021
Later among the works it cites.
J. Gu, C. Sun, and H. Zhao, “Densetnt: End-to-end trajectory prediction from dense goal sets,” in ICCV , 2021
2021
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2019
Cited alongside, same era.
A. Kendall, J. Hawke, D. Janz, P. Mazur, D. Reda, J.-M. Allen, V.-D. Lam, A. Bewley, and A. Shah, “Learning to drive in a day,” in ICRA , 2019
2019
Cited alongside, same era.
A. H. Qureshi, A. Simeonov, M. J. Bency, and M. C. Yip, “Motion planning networks,” in ICRA , 2019
2019
Cited alongside, same era.
M. Hekmatnejad, S. Yaghoubi, A. Dokhanchi, H. B. Amor, A. Shrivastava, L. Karam, and G. Fainekos, “Encoding and monitoring responsibility sensitive safety rules for automated vehicles in signal temporal logic,” in International Conference on Formal Methods and Models for System Design , 2019
2019
Cited alongside, same era.
R. Wightman, “Pytorch image models,” https://github.com/rwightman/pytorch-image-models , 2019
2019
Cited alongside, same era.
S. Vora, A. H. Lang, B. Helou, and O. Beijbom, “Pointpainting: Sequential fusion for 3d object detection,” in CVPR , 2020
2020
Cited alongside, same era.
T. Phan-Minh, E. C. Grigore, F. A. Boulton, O. Beijbom, and E. M. Wolff, “Covernet: Multimodal behavior prediction using trajectory sets,” in CVPR , 2020
2020
Cited alongside, same era.
M. Liang, B. Yang, R. Hu, Y. Chen, R. Liao, S. Feng, and R. Urtasun, “Learning lane graph representations for motion forecasting,” in ECCV , 2020
2020
Cited alongside, same era.
Later among the works it cites.
S. Casas, A. Sadat, and R. Urtasun, “MP3: A unified model to map, perceive, predict and plan,” in CVPR , 2021
2021
Later among the works it cites.
K. Chitta, A. Prakash, and A. Geiger, “Neat: Neural attention fields for end-to-end autonomous driving,” in ICCV , 2021
2021
Later among the works it cites.
J. Liu, Z. Yang, Z. Huang, W. Li, S. Dang, and H. Li, “Simulation performance evaluation of pure pursuit, stanley, lqr, mpc controller for autonomous vehicles,” in 2021 IEEE international conference on real-time computing and robotics (RCAR) , 2021
2021
Later among the works it cites.
W. Xiao, N. Mehdipour, A. Collin, A. Y. Bin-Nun, E. Frazzoli, R. D. Tebbens, and C. Belta, “Rule-based optimal control for autonomous driving,” in Proceedings of the ACM/IEEE 12th International Conference on Cyber-Physical Systems , 2021, pp. 143–154
2021
Later among the works it cites.
J. Houston, G. Zuidhof, L. Bergamini, Y. Ye, L. Chen, A. Jain, S. Omari, V. Iglovikov, and P. Ondruska, “One thousand and one hours: Self-driving motion prediction dataset,” in CoRL , 2021
2021
Later among the works it cites.
O. Scheel, L. Bergamini, M. Wolczyk, B. Osinski, and P. Ondruska, “Urban driver: Learning to drive from real-world demonstrations using policy gradients,” in CoRL , 2021
2021
Later among the works it cites.
2021
Later among the works it cites.
L. Gressenbuch, K. Esterle, T. Kessler, and M. Althoff, “Mona: The munich motion dataset of natural driving,” in ITSC , 2022
2022
Later among the works it cites.
O. Scheel, L. Bergamini, M. Wolczyk, B. Osiński, and P. Ondruska, “Urban driver: Learning to drive from real-world demonstrations using policy gradients,” in CoRL , 2022
2022
Later among the works it cites.
Q. Li, Z. Peng, L. Feng, Q. Zhang, Z. Xue, and B. Zhou, “Metadrive: Composing diverse driving scenarios for generalizable reinforcement learning,” PAMI , 2022
2022
Later among the works it cites.
M. Vitelli, Y. Chang, Y. Ye, A. Ferreira, M. Wołczyk, B. Osiński, M. Niendorf, H. Grimmett, Q. Huang, A. Jain et al. , “Safetynet: Safe planning for real-world self-driving vehicles using machine-learned policies,” in ICRA , 2022
2022
Later among the works it cites.
J. Ngiam, V. Vasudevan, B. Caine, Z. Zhang, H.-T. L. Chiang, J. Ling, R. Roelofs, A. Bewley, C. Liu, A. Venugopal et al. , “Scene transformer: A unified architecture for predicting future trajectories of multiple agents,” in ICLR , 2022
2022
Later among the works it cites.
D. Chen and P. Krähenbühl, “Learning from all vehicles,” in CVPR , 2022
2022
Later among the works it cites.
S. Hu, L. Chen, P. Wu, H. Li, J. Yan, and D. Tao, “St-p3: End-to-end vision-based autonomous driving via spatial-temporal feature learning,” in ECCV , 2022
2022
Later among the works it cites.
P. Wu, X. Jia, L. Chen, J. Yan, H. Li, and Y. Qiao, “Trajectory-guided control prediction for end-to-end autonomous driving: A simple yet strong baseline,” in NeurIPS , 2022
2022
Later among the works it cites.
R. McAllister, B. Wulfe, J. Mercat, L. Ellis, S. Levine, and A. Gaidon, “Control-aware prediction objectives for autonomous driving,” in ICRA , 2022
2022
Later among the works it cites.
S. Maierhofer, P. Moosbrugger, and M. Althoff, “Formalization of intersection traffic rules in temporal logic,” in IV , 2022
2022
Later among the works it cites.
S. Jiang, Z. Xiong, W. Lin, Y. Cao, Z. Xia, J. Miao, and Q. Luo, “An efficient framework for reliable and personalized motion planner in autonomous driving,” RA-L , 2022
2022
Later among the works it cites.
M. Igl, D. Kim, A. Kuefler, P. Mougin, P. Shah, K. Shiarlis, D. Anguelov, M. Palatucci, B. White, and S. Whiteson, “Symphony: Learning realistic and diverse agents for autonomous driving simulation,” in ICRA , 2022
2022
Later among the works it cites.
Y. Hu, J. Yang, L. Chen, K. Li, C. Sima, X. Zhu, S. Chai, S. Du, T. Lin, W. Wang et al. , “Planning-oriented autonomous driving,” in CVPR , 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
T. Phan-Minh, F. Howington, T.-S. Chu, S. U. Lee, M. S. Tomov, N. Li, C. Dicle, S. Findler, F. Suarez-Ruiz, R. Beaudoin et al. , “DriveIRL: Drive in real life with inverse reinforcement learning,” in ICRA , 2023
2023
Later among the works it cites.
K. Xiong, S. Gong, X. Ye, X. Tan, J. Wan, E. Ding, J. Wang, and X. Bai, “Cape: Camera view position embedding for multi-view 3d object detection,” in CVPR , 2023
2023
Later among the works it cites.
Y. Hu, K. Li, P. Liang, J. Qian, Z. Yang, H. Zhang, W. Shao, Z. Ding, W. Xu, and Q. Liu, “Imitation with spatial-temporal heatmap: 2nd place solution for nuplan challenge,” 2023
2023
Later among the works it cites.
Z. Huang, H. Liu, and C. Lv, “Gameformer: Game-theoretic modeling and learning of transformer-based interactive prediction and planning for autonomous driving,” 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,” 2023
2023
Later among the works it cites.