Fetching the paper…
Reading the bibliography…
Autonomous driving is an emerging technology that has advanced rapidly over the last decade.
L. Evans, Traffic safety and the driver . Science Serving Society, 1991
1991
Earlier work this paper cites.
C. J. Watkins and P. Dayan, “Q-learning,” Machine learning , pp. 279–292, 1992
1992
Earlier work this paper cites.
M. I. Ribeiro, “Kalman and extended kalman filters: Concept, derivation and properties,” Institute for Systems and Robotics , p. 46, 2004
2004
Earlier work this paper cites.
Z. Lari, A. Habib, and E. Kwak, “An adaptive approach for segmentation of 3d laser point cloud,” International archives of the photogrammetry, remote sensing and spatial information sciences , vol. 38, no. 5, p. W12, 2011
2011
Earlier work this paper cites.
V. Milanés and S. E. Shladover, “Modeling cooperative and autonomous adaptive cruise control dynamic responses using experimental data,” Transportation Research Part C: Emerging Technologies , pp. 285–300, 2014
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
J. Erdmann, “Sumo’s lane-changing model,” in Modeling Mobility with Open Data , 2015, pp. 105–123
2015
Earlier work this paper cites.
V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. Riedmiller, A. K. Fidjeland, G. Ostrovski et al. , “Human-level control through deep reinforcement learning,” nature , pp. 529–533, 2015
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
M. Won, T. Park, and S. H. Son, “Toward mitigating phantom jam using vehicle-to-vehicle communication,” IEEE transactions on intelligent transportation systems , pp. 1313–1324, 2016
2016
Earlier work this paper cites.
A. Talebpour and H. S. Mahmassani, “Influence of connected and autonomous vehicles on traffic flow stability and throughput,” Transportation Research Part C: Emerging Technologies , pp. 143–163, 2016
2016
Earlier work this paper cites.
W. Masson, P. Ranchod, and G. Konidaris, “Reinforcement learning with parameterized actions,” in Thirtieth AAAI Conference on Artificial Intelligence , 2016
2016
Earlier work this paper cites.
I. Syarif, A. Prugel-Bennett, and G. Wills, “Svm parameter optimization using grid search and genetic algorithm to improve classification performance,” TELKOMNIKA (Telecommunication Computing Electronics and Control) , vol. 14, no. 4, pp. 1502–1509, 2016
2016
Earlier work this paper cites.
L. Xiao, M. Wang, and B. Van Arem, “Realistic car-following models for microscopic simulation of adaptive and cooperative adaptive cruise control vehicles,” Transportation Research Record , pp. 1–9, 2017
2017
Earlier work this paper cites.
A. Dosovitskiy, G. Ros, F. Codevilla, A. Lopez, and V. Koltun, “CARLA: An open urban driving simulator,” in Proceedings of the 1st Annual Conference on Robot Learning , 2017, pp. 1–16
2017
Earlier work this paper cites.
R. E. Stern, S. Cui, M. L. Delle Monache, R. Bhadani, M. Bunting, M. Churchill, N. Hamilton, H. Pohlmann, F. Wu, B. Piccoli et al. , “Dissipation of stop-and-go waves via control of autonomous vehicles: Field experiments,” Transportation Research Part C: Emerging Technologies , pp. 205–221, 2018
2018
Earlier work this paper cites.
R. S. Sutton and A. G. Barto, Reinforcement learning: An introduction . MIT press, 2018
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
2019
Earlier work this paper cites.
S. Nageshrao, H. E. Tseng, and D. Filev, “Autonomous highway driving using deep reinforcement learning,” in IEEE SMC , 2019, pp. 2326–2331
2019
Earlier work this paper cites.
J. Chen, B. Yuan, and M. Tomizuka, “Deep imitation learning for autonomous driving in generic urban scenarios with enhanced safety,” in 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2019, pp. 2884–2890
2019
Cited alongside, same era.
M.-F. Chang, J. Lambert, P. Sangkloy, J. Singh, S. Bak, A. Hartnett, D. Wang, P. Carr, S. Lucey, D. Ramanan et al. , “Argoverse: 3d tracking and forecasting with rich maps,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 8748–8757
2019
Cited alongside, same era.
Y. Chen, C. Dong, P. Palanisamy, P. Mudalige, K. Muelling, and J. M. Dolan, “Attention-based hierarchical deep reinforcement learning for lane change behaviors in autonomous driving,” in CVPR Workshops , 2019, pp. 0–0
2019
Cited alongside, same era.
Z. Huang, Z. Zhang, H. Li, L. Qin, and J. Rong, “Determining appropriate lane-changing spacing for off-ramp areas of urban expressways,” Sustainability , p. 2087, 2019
2019
M. Fu, T. Zhang, W. Song, Y. Yang, and M. Wang, “Trajectory prediction-based local spatio-temporal navigation map for autonomous driving in dynamic highway environments,” IEEE Transactions on Intelligent Transportation Systems , 2021
2021
Later among the works it cites.
W. Zeng, C. Lin, K. Liu, J. Lin, and A. K. Tung, “Modeling spatial nonstationarity via deformable convolutions for deep traffic flow prediction,” IEEE Transactions on Knowledge and Data Engineering (TKDE) , 2021
2021
Later among the works it cites.
C. Pérez-D’Arpino, C. Liu, P. Goebel, R. Martín-Martín, and S. Savarese, “Robot navigation in constrained pedestrian environments using reinforcement learning,” in IEEE ICRA , 2021, pp. 1140–1146
2021
Later among the works it cites.
S. Fujimoto and S. S. Gu, “A minimalist approach to offline reinforcement learning,” Advances in neural information processing systems , vol. 34, pp. 20 132–20 145, 2021
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…
Cited alongside, same era.
J. Wang, Q. Zhang, D. Zhao, and Y. Chen, “Lane change decision-making through deep reinforcement learning with rule-based constraints,” in 2019 International Joint Conference on Neural Networks (IJCNN) . IEEE, 2019, pp. 1–6
2019
Cited alongside, same era.
X. Qu, Y. Yu, M. Zhou, C.-T. Lin, and X. Wang, “Jointly dampening traffic oscillations and improving energy consumption with electric, connected and automated vehicles: a reinforcement learning based approach,” Applied Energy , p. 114030, 2020
2020
Cited alongside, same era.
S. Aradi, “Survey of deep reinforcement learning for motion planning of autonomous vehicles,” IEEE Transactions on Intelligent Transportation Systems , 2020
2020
Cited alongside, same era.
D. A. Tedjopurnomo, Z. Bao, B. Zheng, F. M. Choudhury, and A. K. Qin, “A survey on modern deep neural network for traffic prediction: Trends, methods and challenges,” IEEE Transactions on Knowledge and Data Engineering (TKDE) , pp. 1544–1561, 2020
2020
Cited alongside, same era.
M. Zhu, Y. Wang, Z. Pu, J. Hu, X. Wang, and R. Ke, “Safe, efficient, and comfortable velocity control based on reinforcement learning for autonomous driving,” Transportation Research Part C: Emerging Technologies , p. 102662, 2020
2020
Cited alongside, same era.
D. Feng, C. Haase-Schütz, L. Rosenbaum, H. Hertlein, C. Glaeser, F. Timm, W. Wiesbeck, and K. Dietmayer, “Deep multi-modal object detection and semantic segmentation for autonomous driving: Datasets, methods, and challenges,” IEEE Transactions on Intelligent Transportation Systems , pp. 1341–1360, 2020
2020
Cited alongside, same era.
C. Gómez-Huélamo, J. D. Egido, L. M. Bergasa, R. Barea, E. López-Guillén, F. Arango, J. Araluce, and J. López, “Train here, drive there: Simulating real-world use cases with fully-autonomous driving architecture in carla simulator,” in Workshop of Physical Agents . Springer, 2020, pp. 44–59
2020
Cited alongside, same era.
R. Gutiérrez, E. López-Guillén, L. M. Bergasa, R. Barea, Ó. Pérez, C. Gómez-Huélamo, F. Arango, J. Del Egido, and J. López-Fernández, “A waypoint tracking controller for autonomous road vehicles using ros framework,” Sensors , p. 4062, 2020
2020
Cited alongside, same era.
D. Brandfonbrener, W. Whitney, R. Ranganath, and J. Bruna, “Offline rl without off-policy evaluation,” Advances in neural information processing systems , vol. 34, pp. 4933–4946, 2021
2021
Later among the works it cites.
2021
Later among the works it cites.
D. Bertsekas, Rollout, policy iteration, and distributed reinforcement learning , 2021
2021
Later among the works it cites.
W. Fu, Y. Li, Z. Ye, and Q. Liu, “Decision making for autonomous driving via multimodal transformer and deep reinforcement learning,” in 2022 IEEE International Conference on Real-time Computing and Robotics (RCAR) . IEEE, 2022, pp. 481–486
2022
Later among the works it cites.
Ó. Pérez-Gil, R. Barea, E. López-Guillén, L. M. Bergasa, C. Gómez-Huélamo, R. Gutiérrez, and A. Díaz-Díaz, “Deep reinforcement learning based control for autonomous vehicles in carla,” Multimedia Tools and Applications , pp. 3553–3576, 2022
2022
Later among the works it cites.
C. Gómez-Huélamo, A. Diaz-Diaz, J. Araluce, M. E. Ortiz, R. Gutiérrez, F. Arango, Á. Llamazares, and L. M. Bergasa, “How to build and validate a safe and reliable autonomous driving stack? a ros based software modular architecture baseline,” in 2022 IEEE Intelligent Vehicles Symposium (IV) , 2022, pp. 1282–1289
2022
Later among the works it cites.
Y. Liu, Y. Gao, Q. Zhang, D. Ding, and D. Zhao, “Multi-task safe reinforcement learning for navigating intersections in dense traffic,” Journal of the Franklin Institute , 2022
2022
Later among the works it cites.
A. Diaz-Diaz, M. Ocaña, Á. Llamazares, C. Gómez-Huélamo, P. Revenga, and L. M. Bergasa, “Hd maps: Exploiting opendrive potential for path planning and map monitoring,” in 2022 IEEE Intelligent Vehicles Symposium (IV) , 2022, pp. 1211–1217
2022
Later among the works it cites.
B. Varadarajan, A. Hefny, A. Srivastava, K. S. Refaat, N. Nayakanti, A. Cornman, K. Chen, B. Douillard, C. P. Lam, D. Anguelov et al. , “Multipath++: Efficient information fusion and trajectory aggregation for behavior prediction,” in 2022 International Conference on Robotics and Automation (ICRA) , 2022, pp. 7814–7821
2022
Later among the works it cites.
X. Chen, H. Zhang, F. Zhao, Y. Cai, H. Wang, and Q. Ye, “Vehicle trajectory prediction based on intention-aware non-autoregressive transformer with multi-attention learning for internet of vehicles,” IEEE Transactions on Instrumentation and Measurement , pp. 1–12, 2022
2022
Later among the works it cites.
X. Xiao, B. Liu, G. Warnell, and P. Stone, “Motion planning and control for mobile robot navigation using machine learning: a survey,” Autonomous Robots , pp. 569–597, 2022
2022
Later among the works it cites.
T. Shang, G. Lian, Y. Zhao, X. Liu, and W. Wang, “Off-ramp vehicle mandatory lane-changing duration in small spacing section of tunnel-interchange section based on survival analysis,” Journal of Advanced Transportation , 2022
2022
Later among the works it cites.
Y. Xia, S. Liu, X. Chen, Z. Xu, K. Zheng, and H. Su, “Rise: A velocity control framework with minimal impacts based on reinforcement learning,” in Proceedings of the 31st ACM International Conference on Information & Knowledge Management (CIKM) , 2022, pp. 2210–2219
2022
Later among the works it cites.
D. Coelho and M. Oliveira, “A review of end-to-end autonomous driving in urban environments,” IEEE Access , vol. 10, pp. 75 296–75 311, 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,” Advances in Neural Information Processing Systems , vol. 35, pp. 6119–6132, 2022
2022
Later among the works it cites.
S. Liu, Y. Xia, C. Xu, J. Xie, H. Su, and K. Zheng, “Impact-aware maneuver decision with enhanced perception for autonomous vehicle,” in 2023 IEEE 39th International Conference on Data Engineering (ICDE) , 2023
2023
Closest in time.
C.-Y. Wang, A. Bochkovskiy, and H.-Y. M. Liao, “Yolov7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 7464–7475
2023
Closest in time.
X. Jia, P. Wu, L. Chen, J. Xie, C. He, J. Yan, and H. Li, “Think twice before driving: Towards scalable decoders for end-to-end autonomous driving,” in CVPR , 2023
2023
Closest in time.
H. Cui, V. Radosavljevic, F.-C. Chou, T.-H. Lin, T. Nguyen, T.-K. Huang, J. Schneider, and N. Djuric, “Multimodal trajectory predictions for autonomous driving using deep convolutional networks,” in 2019 International Conference on Robotics and Automation (ICRA) , 2019, pp. 2090–2096
2096
Closest in time.