How to Cite
Abstract
Even though the implementation of AV at scale would considerably minimize the number of traffic jams and accidents, its traffic control algorithms that include slippery road resiliency, disturbing driver behavior, and manual-equipped vehicles which inhibit the optimization of traffic flow through certain time-horizon space. Another short-term solution considering the objective of this work is to control the speed of AVs, such as multi-agent learning algorithms, in which the intersection is designated as the experience point. Nevertheless, our traffic jamming mitigation strategy during traffic volume spikes should also fulfill the open boundary condition. Hence, transfer learning seems the most practicable solution to dissimilar traffic situations. Thus, comparing the performance of a multi-agent learning algorithm with transfer learning was described in this work.
References
Tatineni, Sumanth. "Recommendation Systems for Personalized Learning: A Data-Driven Approach in Education." Journal of Computer Engineering and Technology (JCET) 4.2 (2020).
Vemoori, V. “Towards Secure and Trustworthy Autonomous Vehicles: Leveraging Distributed Ledger Technology for Secure Communication and Exploring Explainable Artificial Intelligence for Robust Decision-Making and Comprehensive Testing”. Journal of Science & Technology, vol. 1, no. 1, Nov. 2020, pp. 130-7, https://thesciencebrigade.com/jst/article/view/224.
D. Yu, H. Lee, T. Kim, and S. H. Hwang, "Vehicle Trajectory Prediction with Lane Stream Attention-Based LSTMs and Road Geometry Linearization," 2021. ncbi.nlm.nih.gov
E. L. Manibardo, I. Laña, E. Villar, and J. Del Ser, "A Graph-based Methodology for the Sensorless Estimation of Road Traffic Profiles," 2022. [PDF]
C. Wu, A. Kreidieh, K. Parvate, E. Vinitsky et al., "Flow: A Modular Learning Framework for Mixed Autonomy Traffic," 2017. [PDF]
X. Zhou, Z. Liu, F. Wang, Y. Xie et al., "Using Deep Learning to Forecast Maritime Vessel Flows," 2020. ncbi.nlm.nih.gov
L. Zheng, S. Son, and M. C. Lin, "Traffic-Aware Autonomous Driving with Differentiable Traffic Simulation," 2022. [PDF]
J. Ji, J. Wang, Z. Jiang, J. Jiang et al., "STDEN: Towards Physics-Guided Neural Networks for Traffic Flow Prediction," 2022. [PDF]
Y. Zhu, M. Wang, X. Yin, J. Zhang et al., "Deep Learning in Diverse Intelligent Sensor Based Systems," 2022. ncbi.nlm.nih.gov
K. Yu, X. Qin, Z. Jia, Y. Du et al., "Cross-Attention Fusion Based Spatial-Temporal Multi-Graph Convolutional Network for Traffic Flow Prediction," 2021. ncbi.nlm.nih.gov
S. Du, H. Guo, and A. Simpson, "Self-Driving Car Steering Angle Prediction Based on Image Recognition," 2019. [PDF]
A. Pamuncak, W. Guo, A. Soliman Khaled, and I. Laory, "Deep learning for bridge load capacity estimation in post-disaster and -conflict zones," 2019. ncbi.nlm.nih.gov
T. Raviteja and R. Vedaraj . I. S, "Global Image Segmentation Process using Machine Learning algorithm & Convolution Neural Network method for Self- Driving Vehicles," 2020. [PDF]
S. Kuutti, R. Bowden, and S. Fallah, "Weakly Supervised Reinforcement Learning for Autonomous Highway Driving via Virtual Safety Cages," 2021. ncbi.nlm.nih.gov
S. Grigorescu, B. Trasnea, T. Cocias, and G. Macesanu, "A Survey of Deep Learning Techniques for Autonomous Driving," 2019. [PDF]
J. Zhang, S. Qu, Z. Zhang, and S. Cheng, "Improved genetic algorithm optimized LSTM model and its application in short-term traffic flow prediction," 2022. ncbi.nlm.nih.gov
G. Singh, V. Choutas, S. Saha, F. Yu et al., "Spatio-Temporal Action Detection Under Large Motion," 2022. [PDF]
B. Yao, A. Ma, R. Feng, X. Shen et al., "A Deep Learning Framework About Traffic Flow Forecasting for Urban Traffic Emission Monitoring System," 2022. ncbi.nlm.nih.gov
H. Yan and Y. Li, "A Survey of Generative AI for Intelligent Transportation Systems," 2023. [PDF]
E. L. Manibardo, I. Laña, and J. Del Ser, "Deep Learning for Road Traffic Forecasting: Does it Make a Difference?," 2020. [PDF]
W. Ding, T. Zhang, J. Wang, and Z. Zhao, "Multi-graph Spatio-temporal Graph Convolutional Network for Traffic Flow Prediction," 2023. [PDF]
Y. Wei and H. Liu, "Convolutional Long-Short Term Memory Network with Multi-Head Attention Mechanism for Traffic Flow Prediction," 2022. ncbi.nlm.nih.gov
G. Wan, S. Liu, F. Bronzino, N. Feamster et al., "CATO: End-to-End Optimization of ML-Based Traffic Analysis Pipelines," 2024. [PDF]
A. Razi, X. Chen, H. Li, H. Wang et al., "Deep Learning Serves Traffic Safety Analysis: A Forward-looking Review," 2022. [PDF]
W. Zhao, "Accurate non-stationary short-term traffic flow prediction method," 2022. [PDF]
C. Corbière, "Robust Deep Learning for Autonomous Driving," 2022. [PDF]
A. Ghany Ismaeel, K. Janardhanan, M. Sankar, Y. Natarajan et al., "Traffic Pattern Classification in Smart Cities Using Deep Recurrent Neural Network," 2024. [PDF]
Y. Li and W. Zhang, "Traffic flow digital twin generation for highway scenario based on radar-camera paired fusion," 2023. ncbi.nlm.nih.gov
S. Kuutti, R. Bowden, Y. Jin, P. Barber et al., "A Survey of Deep Learning Applications to Autonomous Vehicle Control," 2019. [PDF]
N. Peppes, T. Alexakis, E. Adamopoulou, and K. Demestichas, "Driving Behaviour Analysis Using Machine and Deep Learning Methods for Continuous Streams of Vehicular Data," 2021. ncbi.nlm.nih.gov
L. Liang, H. Ye, G. Yu, and G. Ye Li, "Deep Learning based Wireless Resource Allocation with Application to Vehicular Networks," 2019. [PDF]