Optimisation of Decision Efficiency for Autonomous Driving at Unsignalised Intersections Based on DRL and GPT
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The rapid increase in urban vehicle numbers has intensified traffic congestion and safety challenges. Unsignalised intersections pose significant difficulties for autonomous vehicle decision-making. To enhance decision efficiency and safety in such scenarios, this study proposes a decision optimisation method for autonomous driving at unsignalized intersections. The approach first employs a generative pre-trained transformer (GPT) to learn complex interactive behaviour patterns from driving data and acquire prior knowledge. This prior knowledge is then used to initialise the policy network of a deep reinforcement learning (DRL) agent, specifically deep q-network (DQN), which is further optimised through interaction within a simulated environment. This framework aims to combine the powerful sequence modelling capability of GPT with the goal-directed optimisation strength of DRL. Experimental results demonstrate that the proposed method achieves superior performance: The median safe distance reaches 19.58 m (maximum 32.50 m, minimum 8.46 m), the collision rate is as low as 1.07%, and the success rate exceeds 98%. Compared to baseline methods, the proposed approach significantly improves decision-making efficiency and safety for autonomous vehicles at unsignalised intersections, validating its effectiveness.
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Kiran BR, et al. Deep reinforcement learning for autonomous driving: A survey. IEEE Transactions on Intelligent Transportation Systems. 2022;23(6):4909-4926. DOI: 10.1109/TITS.2021.3054625.
Grigorescu S, et al. A survey of deep learning techniques for autonomous driving. Journal of Field Robotics. 2020;37(3):362-386. DOI: 10.1002/rob.21918.
Zhu Z, Zhao H. A survey of deep RL and IL for autonomous driving policy learning. IEEE Transactions on Intelligent Transportation Systems. 2022;23(9):14043-14065. DOI: 10.1109/TITS.2021.3134702.
Liu P, et al. Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing. Acm Computing Surveys. 2023;55(9):1-35. DOI: 10.1145/3560815.
Chavez MR, et al. Chat generative pre-trained transformer: Why we should embrace this technology. American Journal of Obstetrics and Gynecology. 2023;228(6):706-711. DOI: 10.1016/j.ajog.2023.03.010.
Shi Y, et al. A control method with reinforcement learning for urban un-signalized intersection in hybrid traffic environment. Sensors (Basel). 2022;22(3):779-782. DOI: 10.3390/s22030779.
Liu H, et al. Automatic lane-level intersection map generation using low-channel roadside LiDAR. IEEE-CAA Journal of Automatica Sinica. 2023;10(5):1209-1222. DOI: 10.1109/JAS.2023.123183.
Maadi S, et al. Real-time adaptive traffic signal control in a connected and automated vehicle environment: optimisation of signal planning with reinforcement learning under vehicle speed guidance. Sensors. 2022;22(19):7501-7509. DOI: 10.3390/s22197501.
Qu S, et al. Behavioral patterns of drivers under signalized and unsignalised urban intersections. Applied Sciences, 2024, 14(5): 1802-1807. DOI: 10.3390/App14051802.
Huang Z, Wu J, Lv C. Efficient deep reinforcement learning with imitative expert priors for autonomous driving. IEEE Trans Neural Netw Learn Syst. 2023;34(10):7391-7403. DOI: 10.1109/tnnls.2022.3142822.
Chen J, Li SE, Tomizuka M. Interpretable end-to-end urban autonomous driving with latent deep reinforcement learning. IEEE Transactions on Intelligent Transportation Systems. 2022;23(6):5068-5078. DOI: 10.1109/TITS.2020.3046646.
Fuchs F, et al. Super-human performance in gran turismo sport using deep reinforcement learning. IEEE Robotics and Automation Letters. 2021;6(3):4257-4264. DOI: 10.1109/LRA.2021.3064284.
Pang H, Wang Z, Li G. Large language model guided deep reinforcement learning for decision making in autonomous driving. Arxiv Preprint Arxiv. 2024. DOI: 10.48550/arXiv.2412.18511.
Al‑Sharman M, et al. Autonomous driving at unsignalised intersections: A review of decision‑making challenges and reinforcement learning‑based solutions. Arxiv Preprint Arxiv. 2024. DOI: 10.48550/arXiv.2409.13144.
Liu J, et al. MTD‑GPT: A multi‑task decision‑making GPT model for autonomous driving at unsignalised intersections. In: Proceedings of the 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC); 2023 Sep 24‑28; Bilbao, Spain. p. 5154‑5161. DOI: 10.1109/ITSC57777.2023.10421993.
Kodati D, Tene R. Identifying suicidal emotions on social media through transformer-based deep learning. Applied Intelligence. 2023;53(10):11885-11917. DOI: 10.1007/s10489-022-04060-8.
Ji Y, et al. DNABERT: pre-trained bidirectional encoder representations from transformers model for DNA-language in genome. Bioinformatics. 2021;37(15):2112-2120. DOI: 10.1093/bioinformatics/btab083.
Smarandache F. Plithogeny, plithogenic set, logic, probability and statistics: a short review. Journal of Computational and Cognitive Engineering. 2022;1(2):47-50. DOI: 10.47852/bonviewJCCE2202191.
Guo Y, Mustafaoglu Z, Koundal D. Spam detection using bidirectional transformers and machine learning classifier algorithms. Journal of Computational and Cognitive Engineering. 2023;2(1):5-9. DOI: 10.47852/bonviewJCCE2202192.
Lei L, et al. Deep reinforcement learning for autonomous internet of things: Model, applications and challenges. IEEE Communications Surveys and Tutorials. 2020;22(3):1722-1760. DOI: 10.1109/COMST.2020.2988367.
Kuutti S, et al. A survey of deep learning applications to autonomous vehicle control. IEEE Transactions on Intelligent Transportation Systems. 2021;22(2):712-733. DOI: 10.1109/TITS.2019.2962338.
Aradi S. Survey of deep reinforcement learning for motion planning of autonomous vehicles. IEEE Transactions on Intelligent Transportation Systems. 2022;23(2):740-759. DOI: 10.1109/TITS.2020.3024655.
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