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DOI: 10.14569/IJACSA.2025.0160268
PDF

Traffic Safety in Mixed Environments by Predicting Lane Merging and Adaptive Control

Author 1: Aigerim Amantay
Author 2: Shyryn Akan
Author 3: Nurlybek Kenes
Author 4: Amandyk Kartbayev

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 16 Issue 2, 2025.

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Abstract: Autonomous driving technology is primarily developed to enhance traffic safety through advancements in motion prediction and adaptive control mechanisms. Highway lane merging remains a high-risk scenario, accounting for approximately 7% of highway collisions globally due to misjudged vehicle interactions, according to international statistics. This paper proposes a two-stage deep learning framework for autonomous lane merging in mixed traffic. Using the Argoverse dataset, which contains over 300,000 vehicle trajectories mapped to high-definition road networks, we first predict vehicle trajectories using a Seq2Seq model with LSTM layers, achieving a 21% improvement in prediction accuracy over a baseline Multi-layer Perceptron model. In the second stage, reinforcement learning is employed for maneuver generation, where a Dueling Deep Q-Network outperforms a standard DQN by 8% in collision avoidance. Experimental results indicate that the combined trajectory prediction and RL-based framework significantly reduces merging delays, enhances data-driven decision-making in mixed traffic environments, and provides a scalable solution for safer autonomous highway merging.

Keywords: Autonomous driving; lane merging; traffic safety; trajectory prediction; deep learning; LiDAR; LSTM

Aigerim Amantay, Shyryn Akan, Nurlybek Kenes and Amandyk Kartbayev, “Traffic Safety in Mixed Environments by Predicting Lane Merging and Adaptive Control” International Journal of Advanced Computer Science and Applications(IJACSA), 16(2), 2025. http://dx.doi.org/10.14569/IJACSA.2025.0160268

@article{Amantay2025,
title = {Traffic Safety in Mixed Environments by Predicting Lane Merging and Adaptive Control},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160268},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160268},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {2},
author = {Aigerim Amantay and Shyryn Akan and Nurlybek Kenes and Amandyk Kartbayev}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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