School of Mechanical Engineering, Guangxi University, Nanning 530004, China.
Sensors (Basel). 2023 Jan 6;23(2):660. doi: 10.3390/s23020660.
To explore the potential relationship between the leading vehicle and the following vehicle during car-following, we proposed a novel car-following model combining a convolutional neural network (CNN) with a long short-term memory (LSTM) network. Firstly, 400 car-following periods were extracted from the natural driving database and the OpenACC car-following experiment database. Then, we developed a CNN-LSTM car-following model, and the CNN is employed to analyze the potential relationship between the vehicle's dynamic parameters and to extract the features of car-following behavior to generate the feature vector. The LSTM network is adopted to save the feature vector and predict the speed of the following vehicle. Finally, the CNN-LSTM model is trained and tested with the extracted car-following trajectories data and compared with the classical car-following models (LSTM model, intelligent driver model). The results show that the accuracy and the ability to learn the heterogeneity of the proposed model are better than the other two. Furthermore, the CNN-LSTM model can accurately reproduce the hysteresis phenomenon of congested traffic flow and apply to heterogeneous traffic flow mixed with adaptive cruise control vehicles on the freeway, which indicates that it has strong generalization ability.
为了探索跟驰过程中领头车与跟随车之间的潜在关系，我们提出了一种将卷积神经网络（CNN）与长短期记忆（LSTM）网络相结合的新型跟驰模型。首先，从自然驾驶数据库和 OpenACC 跟驰实验数据库中提取了 400 个跟驰周期。然后，我们开发了一个 CNN-LSTM 跟驰模型，CNN 用于分析车辆动态参数之间的潜在关系，并提取跟驰行为的特征，以生成特征向量。LSTM 网络用于保存特征向量并预测跟随车的速度。最后，利用提取的跟驰轨迹数据对 CNN-LSTM 模型进行训练和测试，并与经典跟驰模型（LSTM 模型、智能驾驶员模型）进行比较。结果表明，所提出模型的准确性和学习异质性的能力均优于其他两种模型。此外，CNN-LSTM 模型可以准确再现拥挤交通流的滞后现象，并适用于高速公路上混合自适应巡航控制车辆的异质交通流，这表明它具有较强的泛化能力。
