Fetching the paper…
Reading the bibliography…
Ramp merging is one of the bottlenecks in traffic systems, which commonly cause traffic congestion, accidents, and severe carbon emissions.
T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, and D. Amodei, “Language Models Are Few-Shot Learners,” in
1901
Earlier work this paper cites.
M. van Nieuwstadt, M. Rathinam, and R. M. Murray, “Differential flatness and absolute equivalence of nonlinear control systems,”
1998
Earlier work this paper cites.
R. Rajamani,
2006
Earlier work this paper cites.
D. de Waard, C. Dijksterhuis, and K. A. Brookhuis, “Merging into Heavy Motorway Traffic by Young and Elderly Drivers,”
2009
Earlier work this paper cites.
M. Zhou, X. Qu, and S. Jin, “On the Impact of Cooperative Autonomous Vehicles in Improving Freeway Merging: A Modified Intelligent Driver Model-Based Approach,”
2016
Earlier work this paper cites.
F. A. Oliehoek and C. Amato,
2016
Earlier work this paper cites.
2017
Earlier work this paper cites.
K. M. Lynch and F. C. Park,
2017
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
Earlier work this paper cites.
N. Deo and M. M. Trivedi, “Convolutional Social Pooling for Vehicle Trajectory Prediction,” in
2018
Earlier work this paper cites.
A. Gupta, J. Johnson, L. Fei-Fei, S. Savarese, and A. Alahi, “Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks,” in
2018
Earlier work this paper cites.
W. Zeng, W. Luo, S. Suo, A. Sadat, B. Yang, S. Casas, and R. Urtasun, “End-To-End Interpretable Neural Motion Planner,” in
2019
Earlier work this paper cites.
S. Karbalaieali, O. A. Osman, and S. Ishak, “A Dynamic Adaptive Algorithm for Merging Into Platoons in Connected Automated Environments,”
2020
Earlier work this paper cites.
T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell
2020
Earlier work this paper cites.
S. Bhalla, S. Ganapathi Subramanian, and M. Crowley, “Deep Multi Agent Reinforcement Learning for Autonomous Driving,” in
2020
Earlier work this paper cites.
H. Caesar, V. Bankiti, A. H. Lang, S. Vora, V. E. Liong, Q. Xu, A. Krishnan, Y. Pan, G. Baldan, and O. Beijbom, “nuScenes: A Multimodal Dataset for Autonomous Driving,” in
2020
Earlier work this paper cites.
T. Chen, M. Wang, S. Gong, Y. Zhou, and B. Ran, “Connected and Automated Vehicle Distributed Control for On-ramp Merging Scenario: A Virtual Rotation Approach,”
2021
Earlier work this paper cites.
A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, and N. Houlsby, “AN IMAGE IS WORTH 16X16 WORDS: TRANSFORMERS FOR IMAGE RECOGNITION AT SCALE,” 2021
2021
Earlier work this paper cites.
P. Hu, A. Huang, J. Dolan, D. Held, and D. Ramanan, “Safe Local Motion Planning with Self-Supervised Freespace Forecasting,” in
2021
Earlier work this paper cites.
J. Zhu, S. Easa, and K. Gao, “Merging Control Strategies of Connected and Autonomous Vehicles at Freeway On-Ramps: A Comprehensive Review,”
2022
Earlier work this paper cites.
Z. Du, Y. Qian, X. Liu, M. Ding, J. Qiu, Z. Yang, and J. Tang, “GLM: General Language Model Pretraining with Autoregressive Blank Infilling,” in
2022
Earlier work this paper cites.
2022
Earlier work this paper cites.
J. Liu, W. Zhao, and C. Xu, “An Efficient On-Ramp Merging Strategy for Connected and Automated Vehicles in Multi-Lane Traffic,”
2022
Earlier work this paper cites.
E. Hu, Y. Shen, P. Wallis, Z. Allen-Zhu, Y. Li, S. Wang, L. Wang, and W. Chen, “LORA: LOW-RANK ADAPTATION OF LARGE LANGUAGE MODELS,” 2022
2022
Cited alongside, same era.
2022
Cited alongside, same era.
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
2022
Cited alongside, same era.
T. Dao, D. Y. Fu, S. Ermon, A. Rudra, and C. Ré, “FLASHATTENTION: fast and memory-efficient exact attention with IO-awareness,” in
2022
Cited alongside, same era.
X. Cai, Y. Wang, and L. Zhang, “Optimus: An Operator Fusion Framework for Deep Neural Networks,”
2022
J. Liu, W. Zhao, C. Wang, C. Xu, L. Li, Q. Chen, and Y. Lian, “Eco-Friendly On-Ramp Merging Strategy for Connected and Automated Vehicles in Heterogeneous Traffic,”
2023
Later among the works it cites.
L. Yang, J. Zhan, W.-L. Shang, S. Fang, G. Wu, X. Zhao, and M. Deveci, “Multi-Lane Coordinated Control Strategy of Connected and Automated Vehicles for On-Ramp Merging Area Based on Cooperative Game,”
2023
Later among the works it cites.
2023
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, L. Lu, X. Jia, Q. Liu, J. Dai, Y. Qiao, and H. Li, “Planning-Oriented Autonomous Driving,” in
2023
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.
Y. Wang, J. Jiang, S. Li, R. Li, S. Xu, J. Wang, and K. Li, “Decision-Making Driven by Driver Intelligence and Environment Reasoning for High-Level Autonomous Vehicles: A Survey,”
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
K. Sanderson, “GPT-4 is here: what scientists think,”
2023
Cited alongside, same era.
A. Chowdhery, S. Narang, J. Devlin, M. Bosma, G. Mishra, A. Roberts, P. Barham, H. W. Chung, C. Sutton, S. Gehrmann
2023
Cited alongside, same era.
2023
Cited alongside, same era.
R. Taori, I. Gulrajani, T. Zhang, Y. Dubois, X. Li, C. Guestrin, P. Liang, and T. B. Hashimoto, “Stanford Alpaca: An Instruction-following LLaMA model,” 2023
2023
Cited alongside, same era.
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,”
2023
Later among the works it cites.
R. Wang, S. Wang, H. Yan, and X. Wang, “WSiP: Wave Superposition Inspired Pooling for Dynamic Interactions-Aware Trajectory Prediction,”
2023
Later among the works it cites.
2023
Later among the works it cites.
Z. Liu, J. Wang, T. Dao, T. Zhou, B. Yuan, Z. Song, A. Shrivastava, C. Zhang, Y. Tian, C. Ré, and B. Chen, “Deja Vu: contextual sparsity for efficient LLMs at inference time,” in
2023
Later among the works it cites.
2024
Closest in time.
2024
Closest in time.
C. Pan, B. Yaman, T. Nesti, A. Mallik, A. G. Allievi, S. Velipasalar, and L. Ren, “VLP: Vision Language Planning for Autonomous Driving.” arXiv, Jan. 2024
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
Z. Fang, S. Hu, H. An, Y. Zhang, J. Wang, H. Cao, X. Chen, and Y. Fang, “PACP: Priority-Aware Collaborative Perception for Connected and Autonomous Vehicles,”
2024
Closest in time.
X. Chen, Y. Deng, H. Ding, G. Qu, H. Zhang, P. Li, and Y. Fang, “Vehicle as a Service (VaaS): Leverage Vehicles to Build Service Networks and Capabilities for Smart Cities,”
2024
Closest in time.
Z. Lin, G. Qu, X. Chen, and K. Huang, “Split Learning in 6G Edge Networks,”
2024
Closest in time.
Z. Lin, G. Zhu, Y. Deng, X. Chen, Y. Gao, K. Huang, and Y. Fang, “Efficient Parallel Split Learning Over Resource-Constrained Wireless Edge Networks,”
2024
Closest in time.
D. Fu, W. Lei, L. Wen, P. Cai, S. Mao, M. Dou, B. Shi, and Y. Qiao, “LimSim++: A Closed-Loop Platform for Deploying Multimodal LLMs in Autonomous Driving,” in
2024
Closest in time.
J. Mao, J. Ye, Y. Qian, M. Pavone, and Y. Wang, “A Language Agent for Autonomous Driving,” in
2024
Closest in time.
J. Lin, J. Tang, H. Tang, S. Yang, W.-M. Chen, W.-C. Wang, G. Xiao, X. Dang, C. Gan, and S. Han, “AWQ: Activation-aware Weight Quantization for On-Device LLM Compression and Acceleration,” in
2024
Closest in time.
2024
Closest in time.
S. Hu, Z. Fang, Y. Deng, X. Chen, Y. Fang, and S. Kwong, “Toward Full-Scene Domain Generalization in Multi-Agent Collaborative Bird’s Eye View Segmentation for Connected and Autonomous Driving,”
2025
Closest in time.