Convolutional Neural Network-Based Lane-Change Strategy via Motion Image Representation for Automated and Connected Vehicles
Convolutional Neural Network-Based Lane-Change Strategy via Motion Image Representation for Automated and Connected Vehicles
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DOI：
10.1109/tnnls.2023.3265662
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发表时间：
2023-04-18
影响因子：
10.4
通讯作者：
Nakano, Kimihiko
中科院分区：
文献类型：
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作者：
Cheng, Shuo;Wang, Zheng;Nakano, Kimihiko
自动化和互联车辆（ACV）的车道变换决策模块是最关键和最具挑战性的问题之一。基于人类的驾驶模式和卷积神经网络（CNN）的特征提取和策略学习能力，提出了一种基于CNN的动态运动图像表示的车道变换决策方法。人类驾驶员在大脑中下意识地构建动态交通场景表征后会采取适当的驾驶操作，因此本研究首次提出了动态运动图像表征方法，以揭示运动敏感区域（MSA）内的信息性交通状况，从而提供周围汽车的全貌。然后，本文开发了一个CNN模型，从MSA运动图像的标记数据集中提取底层特征并学习驱动策略。此外，增加了一个安全约束层，以避免车辆碰撞。我们建立了一个仿真平台的基础上模拟城市流动性（SUMO）收集交通数据集和测试我们提出的方法。此外，真实世界的交通数据集也涉及到进一步研究所提出的方法的性能。基于规则的策略和基于强化学习（RL）的方法被用来与我们的方法进行比较。所有的结果表明，所提出的方法执行换道决策比流行的方法，这表明我们的计划有巨大的潜力，以加快部署的ACV，值得进一步研究。
The lane-change decision-making module of automated and connected vehicles (ACVs) is one of the most crucial and challenging issues to be addressed. Motivated by human beings' underlying driving paradigm and the convolutional neural network's (CNN) dramatic capability of extracting features and learning strategies, this article proposes a CNN-based lane-change decision-making method via the dynamic motion image representation. Human drivers take proper driving maneuvers after they subconsciously construct the dynamic traffic scene representation in their brains, so this study first proposes the dynamic motion image representation method to reveal informative traffic situations in the motion-sensitive area (MSA), which provides a full view of surrounding cars. Then, this article develops a CNN model to extract the underlying features and learn driving policies from labeled datasets of MSA motion images. Besides, a safety-constrained layer is added to avoid vehicle collisions. We build a simulation platform based on the simulation of urban mobility (SUMO) to collect traffic datasets and test our proposed method. In addition, real-world traffic datasets are also involved to further investigate the proposed method's performance. The rule-based strategy and reinforcement learning (RL)-based method are used to compare with our approach. All results demonstrate that the proposed method performs lane-change decision-making much better than prevailing methods, which suggests our scheme has huge potential to accelerate the deployment of ACVs and is worth further study.