具有自动参数优化和跨数据集适应性的高精度汽车跟随模型

IF 2.6 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC World Electric Vehicle Journal Pub Date : 2023-12-07 DOI:10.3390/wevj14120341
Pinpin Qin, Shenglin Bin, Yanzhi Pang, Xing Li, Fumao Wu, Shiwei Liu
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引用次数: 0

摘要

尽管网络超参数对深度学习跟车模型的影响很大,但对深度学习跟车模型的网络超参数研究相对较少。因此,本研究提出了一种结合粒子群优化(PSO)和门控循环单元(GRU)网络的汽车跟随模型。使用自然驾驶数据库中的数据对PSO-GRU车辆跟随模型进行训练和测试。结果表明,与智能驾驶员模型(IDM)和GRU跟车模型相比,PSO-GRU跟车模型对跟车速度模拟的均方误差(MSE)分别降低了88.36%和72.92%,平均绝对百分比误差(MAPE)分别降低了64.81%和50.14%,预测精度更高。使用东南大学无人机视频轨迹数据库的数据集3和NGSIM的I-80数据集研究汽车跟随模型的跨数据集适应性,即验证其可移植性。与GRU跟车模型相比,PSO-GRU跟车模型将测试结果的标准差分别降低了60.64%和32.89%,突出了其更强的预测稳定性和更好的可转移性。验证跟车模型产生走走停停现象的能力,可以更全面地评价其可转移性。通过排仿真试验,PSO-GRU跟车模型在产生走走停停感觉方面优于GRU跟车模型,证明了其优越的可转移性。因此,与其他跟车模型相比,PSO-GRU跟车模型具有更高的预测精度和跨数据集适应性。
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A High-Precision Car-Following Model with Automatic Parameter Optimization and Cross-Dataset Adaptability
Despite the significant impact of network hyperparameters on deep learning car-following models, there has been relatively little research on network hyperparameters of deep learning car-following models. Therefore, this study proposes a car-following model that combines particle swarm optimization (PSO) and gated recurrent unit (GRU) networks. The PSO-GRU car-following model is trained and tested using data from the natural driving database. The results demonstrate that compared to the intelligent driver model (IDM) and the GRU car-following model, the PSO-GRU car-following model reduces the mean squared error (MSE) for the speed simulation of following vehicles by 88.36% and 72.92%, respectively, and reduces the mean absolute percentage error (MAPE) by 64.81% and 50.14%, respectively, indicating a higher prediction accuracy. Dataset 3 from the drone video trajectory database of Southeast University and NGSIM’s I-80 dataset are used to study the car-following model’s cross-dataset adaptability, that is, to verify its transferability. Compared to the GRU car-following model, the PSO-GRU car-following model reduces the standard deviation of the test results by 60.64% and 32.89%, highlighting its more robust prediction stability and better transferability. Verifying the ability of the car-following model to produce the stop-and-go phenomenon can evaluate its transferability more comprehensively. The PSO-GRU car-following model outperforms the GRU car-following model in creating stop-and-go sensations through platoon simulation tests, demonstrating its superior transferability. Therefore, the proposed PSO-GRU car-following model has higher prediction accuracy and cross-dataset adaptability compared to other car-following models.
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来源期刊
World Electric Vehicle Journal
World Electric Vehicle Journal Engineering-Automotive Engineering
CiteScore
4.50
自引率
8.70%
发文量
196
审稿时长
8 weeks
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