Fetching the paper…
Reading the bibliography…
To ensure safe driving in dynamic environments, autonomous vehicles should possess the capability to accurately predict lane change intentions of surrounding vehicles in advance and forecast their future trajectories.
H. M. Mandalia and M. D. D. Salvucci, “Using support vector machines for lane-change detection,” in Proceedings of the human factors and ergonomics society annual meeting , vol. 49, no. 22. SAGE Publications Sage CA: Los Angeles, CA, 2005, pp. 1965–1969
1969
Earlier work this paper cites.
P. G. Gipps, “A model for the structure of lane-changing decisions,” Transportation Research Part B: Methodological , vol. 20, no. 5, pp. 403–414, 1986
1986
Earlier work this paper cites.
P. Hidas, “Modelling lane changing and merging in microscopic traffic simulation,” Transportation Research Part C: Emerging Technologies , vol. 10, no. 5-6, pp. 351–371, 2002
2002
Earlier work this paper cites.
A. Zyner, S. Worrall, J. Ward, and E. Nebot, “Long short term memory for driver intent prediction,” in 2017 IEEE Intelligent Vehicles Symposium (IV) . IEEE, 2017, pp. 1484–1489
2017
Earlier work this paper cites.
F. Altché and A. de La Fortelle, “An lstm network for highway trajectory prediction,” in 2017 IEEE 20th international conference on intelligent transportation systems (ITSC) . IEEE, 2017, pp. 353–359
2017
Earlier work this paper cites.
L. Xin, P. Wang, C.-Y. Chan, J. Chen, S. E. Li, and B. Cheng, “Intention-aware long horizon trajectory prediction of surrounding vehicles using dual lstm networks,” in 2018 21st International Conference on Intelligent Transportation Systems (ITSC) . IEEE, 2018, pp. 1441–1446
2018
Earlier work this paper cites.
A. Zyner, S. Worrall, and E. Nebot, “A recurrent neural network solution for predicting driver intention at unsignalized intersections,” IEEE Robotics and Automation Letters , vol. 3, no. 3, pp. 1759–1764, 2018
2018
Earlier work this paper cites.
N. Deo and M. M. Trivedi, “Multi-modal trajectory prediction of surrounding vehicles with maneuver based lstms,” in 2018 IEEE intelligent vehicles symposium (IV) . IEEE, 2018, pp. 1179–1184
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 2018 21st international conference on intelligent transportation systems (ITSC) . IEEE, 2018, pp. 2118–2125
2018
Earlier work this paper cites.
G. He, X. Li, Y. Lv, B. Gao, and H. Chen, “Probabilistic intention prediction and trajectory generation based on dynamic bayesian networks,” in 2019 Chinese Automation Congress (CAC) . IEEE, 2019, pp. 2646–2651
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
J. Hong, B. Sapp, and J. Philbin, “Rules of the road: Predicting driving behavior with a convolutional model of semantic interactions,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 8454–8462
2019
Earlier work this paper cites.
R. Izquierdo, A. Quintanar, I. Parra, D. Fernández-Llorca, and M. Sotelo, “Experimental validation of lane-change intention prediction methodologies based on cnn and lstm,” in 2019 IEEE Intelligent Transportation Systems Conference (ITSC) . IEEE, 2019, pp. 3657–3662
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga et al. , “Pytorch: An imperative style, high-performance deep learning library,” Advances in neural information processing systems , vol. 32, 2019
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
S. Mozaffari, O. Y. Al-Jarrah, M. Dianati, P. Jennings, and A. Mouzakitis, “Deep learning-based vehicle behavior prediction for autonomous driving applications: A review,” IEEE Transactions on Intelligent Transportation Systems , vol. 23, no. 1, pp. 33–47, 2020
2020
Cited alongside, same era.
N. Lyu, J. Wen, Z. Duan, and C. Wu, “Vehicle trajectory prediction and cut-in collision warning model in a connected vehicle environment,” IEEE Transactions on Intelligent Transportation Systems , vol. 23, no. 2, pp. 966–981, 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 Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part II 16 . Springer, 2020, pp. 541–556
2020
Cited alongside, same era.
J. Gao, C. Sun, H. Zhao, Y. Shen, D. Anguelov, C. Li, and C. Schmid, “Vectornet: Encoding hd maps and agent dynamics from vectorized representation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 11 525–11 533
T. Kojima, S. S. Gu, M. Reid, Y. Matsuo, and Y. Iwasawa, “Large language models are zero-shot reasoners,” Advances in neural information processing systems , vol. 35, pp. 22 199–22 213, 2022
2022
Later among the works it cites.
2022
Later among the works it cites.
K. Gao, X. Li, B. Chen, L. Hu, J. Liu, R. Du, and Y. Li, “Dual transformer based prediction for lane change intentions and trajectories in mixed traffic environment,” IEEE Transactions on Intelligent Transportation Systems , 2023
2023
Later among the works it cites.
A. Seff, B. Cera, D. Chen, M. Ng, A. Zhou, N. Nayakanti, K. S. Refaat, R. Al-Rfou, and B. Sapp, “Motionlm: Multi-agent motion forecasting as language modeling,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 8579–8590
2023
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2020
Cited alongside, same era.
X. Qiu, T. Sun, Y. Xu, Y. Shao, N. Dai, and X. Huang, “Pre-trained models for natural language processing: A survey,” Science China Technological Sciences , vol. 63, no. 10, pp. 1872–1897, 2020
2020
Cited alongside, same era.
J. Rasley, S. Rajbhandari, O. Ruwase, and Y. He, “Deepspeed: System optimizations enable training deep learning models with over 100 billion parameters,” in Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining , 2020, pp. 3505–3506
2020
Cited alongside, same era.
2021
Cited alongside, same era.
2021
Cited alongside, same era.
S. Mozaffari, E. Arnold, M. Dianati, and S. Fallah, “Early lane change prediction for automated driving systems using multi-task attention-based convolutional neural networks,” IEEE Transactions on Intelligent Vehicles , vol. 7, no. 3, pp. 758–770, 2022
2022
Cited alongside, same era.
S. Shi, L. Jiang, D. Dai, and B. Schiele, “Motion transformer with global intention localization and local movement refinement,” Advances in Neural Information Processing Systems , vol. 35, pp. 6531–6543, 2022
2022
Cited alongside, same era.
L. Ouyang, J. Wu, X. Jiang, D. Almeida, C. Wainwright, P. Mishkin, C. Zhang, S. Agarwal, K. Slama, A. Ray et al. , “Training language models to follow instructions with human feedback,” Advances in Neural Information Processing Systems , vol. 35, pp. 27 730–27 744, 2022
2022
Cited alongside, same era.
Y. Huang, J. Du, Z. Yang, Z. Zhou, L. Zhang, and H. Chen, “A survey on trajectory-prediction methods for autonomous driving,” IEEE Transactions on Intelligent Vehicles , vol. 7, no. 3, pp. 652–674, 2022
2022
Cited alongside, same era.
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
S. Feng, H. Sun, X. Yan, H. Zhu, Z. Zou, S. Shen, and H. X. Liu, “Dense reinforcement learning for safety validation of autonomous vehicles,” Nature , vol. 615, no. 7953, pp. 620–627, 2023
2023
Later among the works it cites.
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) . IEEE, 2019, pp. 2090–2096
2096
Closest in time.