Lane Change Strategies for Autonomous Vehicles: A Deep Reinforcement Learning Approach Based on Transformer

Li, Guofa; Qiu, Yibin; Yang, Yifan; Li, Zhenning; Li, Shen; Chu, Wenbo; Green, Paul; Li, Shengbo Eben

doi:10.1109/tiv.2022.3227921

Cited by 38 publications

(5 citation statements)

References 53 publications

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“…Deep maximum entropy-inverse reinforcement learning and the game matrix are used to find the reward function for BV behavior. BV is simulated using a deep Q-network technique based on the reward function [34]. According to Yin et al, air warfare is competitive, and the opponent needs to be more explicit, making it hard to choose the best approach.…”

Section: Literature Reviewmentioning

confidence: 99%

Recent Developments of Game Theory and Reinforcement Learning Approaches: A Systematic Review

Jain,

Kumar,

Bhat

2024

IEEE Access

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In the ever-changing world of decision-making, when game theory and reinforcement learning(RL) come together, they create a fascinating combination that shows a new way to solve complex problems in many different fields. The combination of game theory and RL is a powerful convergence that opens up a hopeful new frontier for dealing with difficult decision-making problems in many different fields. Research on the convergence of game theory and RL has shown to be beneficial, providing essential insights into challenging decision-making issues in various disciplines. This study investigates the recent developments of game theory and RL approaches through a systematic review and highlights the significance of game theory in boosting reinforcement algorithms and increasing the interaction of autonomous vehicles, safeguarding edge caching, and more. It offers a thorough account of the developments at the confluence of game theory and RL. The reviewed papers mainly focus on broad themes and address three important research questions: the impact of game theory on multi-agent reinforcement learning (MARL), the significant contributions of game theory to RL, and the significant impact areas. Following the methodology, search outcomes, and study areas is a discussion on game theory-related terminology, followed by study findings. The review's conclusions are offered with ideas for further study and open research questions. The importance of game theory in advancing MARL, the potential of game theory in promoting RL strategies, and the opportunities for combining game theory and RL in cutting-edge fields like mobile edge caching and cyber-physical systems(CPS) are all emphasized in the conclusion. This review article advances our knowledge of the theoretical underpinnings and real-world applications of game theory and RL, laying the groundwork for future improvements in decision-making techniques and algorithms.

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Section: Literature Reviewmentioning

confidence: 99%

Recent Developments of Game Theory and Reinforcement Learning Approaches: A Systematic Review

Jain,

Kumar,

Bhat

2024

IEEE Access

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“…Some researchers combine RNNs and CNNs to integrate both temporal and spatial features into their models (Liao et al 2023;Huang, Mo, and Lv 2022;Bhattacharyya, Huang, and Czarnecki 2023;Zhang and Li 2022). Transformers, with their renowned success in many domains, have also demonstrated superior performance in trajectory prediction (Li et al 2022;Zeng et al 2023;Li et al 2023a). Compared to physicsbased and statistics-based methods, these data-driven approaches have generally demonstrated superior prediction performance, especially for tasks requiring long-term predictions (beyond 3 seconds).…”

Section: Related Workmentioning

confidence: 99%

BAT: Behavior-Aware Human-Like Trajectory Prediction for Autonomous Driving

Liao,

Li,

Shen

et al. 2024

AAAI

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The ability to accurately predict the trajectory of surrounding vehicles is a critical hurdle to overcome on the journey to fully autonomous vehicles. To address this challenge, we pioneer a novel behavior-aware trajectory prediction model (BAT) that incorporates insights and findings from traffic psychology, human behavior, and decision-making. Our model consists of behavior-aware, interaction-aware, priority-aware, and position-aware modules that perceive and understand the underlying interactions and account for uncertainty and variability in prediction, enabling higher-level learning and flexibility without rigid categorization of driving behavior. Importantly, this approach eliminates the need for manual labeling in the training process and addresses the challenges of non-continuous behavior labeling and the selection of appropriate time windows. We evaluate BAT's performance across the Next Generation Simulation (NGSIM), Highway Drone (HighD), Roundabout Drone (RounD), and Macao Connected Autonomous Driving (MoCAD) datasets, showcasing its superiority over prevailing state-of-the-art (SOTA) benchmarks in terms of prediction accuracy and efficiency. Remarkably, even when trained on reduced portions of the training data (25%), our model outperforms most of the baselines, demonstrating its robustness and efficiency in predicting vehicle trajectories, and the potential to reduce the amount of data required to train autonomous vehicles, especially in corner cases. In conclusion, the behavior-aware model represents a significant advancement in the development of autonomous vehicles capable of predicting trajectories with the same level of proficiency as human drivers. The project page is available on our GitHub.

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“…MobileNet series network is a lightweight convolutional neural network proposed by Google. The MobileNetV1 [22] (MV1) network was first proposed in 2017, and improvements have been made to this foundation with the successive introduction of the MobileNetV2 [23] (MV2) and MobileNetV3 [24] (MV3) networks.The MobileNet family of networks uses Depthwise Separable Convolution (DSC) [25], which significantly reduces the number of parameters in the network model and improves the speed of network operation.…”

Section: B Mobilenetmentioning

confidence: 99%

Research on Road Object Detection Model Based on YOLOv4 of Autonomous Vehicle

Wang,

Liu

et al. 2024

IEEE Access

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The YOLOv4 network is widely used in object detection tasks as a representative network, but there is also the problem that the complexity of the network model affects the detection speed. In this paper, we propose an improved MV2_S_YE object detection algorithm based on the YOLOv4 network to improve the detection accuracy while increasing the road object detection speed. Firstly, the backbone network CSPDarknet53 of the YOLOv4 network is replaced by the Mobilenetv2 network to reduce the number of parameters of the network; secondly, the channel attention mechanism is introduced, and the SENet module is embedded in the structure of the PANet to optimize the object detection accuracy; finally, the EIOU loss function is used to replace the CIOU loss function to improve the object detection accuracy further. The MV2_S_YE network is obtained and tested on Pascal VOC, Udacity, and KAIST datasets. To evaluate our approach, we compared MV2-S-YE with YOLOv4, YOLOv4-tiny, YOLOv7-tiny and YOLOv8s. The results show that MV2-S-YE 的

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Lane Change Strategies for Autonomous Vehicles: A Deep Reinforcement Learning Approach Based on Transformer

Cited by 38 publications

References 53 publications

Recent Developments of Game Theory and Reinforcement Learning Approaches: A Systematic Review

Recent Developments of Game Theory and Reinforcement Learning Approaches: A Systematic Review

BAT: Behavior-Aware Human-Like Trajectory Prediction for Autonomous Driving

Research on Road Object Detection Model Based on YOLOv4 of Autonomous Vehicle

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