Data generation for connected and automated vehicle tests using deep learning models.
Data generation for connected and automated vehicle tests using deep learning models.
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DOI：
10.1016/j.aap.2023.107192
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发表时间：
2023-06
期刊：
影响因子：
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通讯作者：
Ye Li;Fei Liu;Lu Xing;Yi He;Changyin Dong;Chen Yuan;Jiguang Chen;Lu Tong
中科院分区：
文献类型：
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作者：
Ye Li;Fei Liu;Lu Xing;Yi He;Changyin Dong;Chen Yuan;Jiguang Chen;Lu Tong
在基于仿真的无人驾驶汽车（CAV）测试与评估中，背景车辆的运动轨迹直接影响到CAV的性能和实验结果。收集的真实的轨迹数据受到样本大小和多样性的限制，并且可能排除对CAV测试至关重要的关键属性组合。因此，必须增加可获得的轨迹数据的丰富性。在这项研究中，我们开发了具有梯度惩罚的Wasserstein生成对抗网络（WGAN-GP）和用于轨迹数据生成的变分自动编码器和生成对抗网络（VAE-GAN）的混合模型。这些模型能够学习观察到的数据空间的压缩表示，并通过在潜在空间中采样然后映射回原始空间来生成数据。将真实的数据和生成的数据应用于具有协同自适应巡航控制（CACC）的CAV跟驰模型中，以碰撞前时间（TTC）指标评价安全性能。结果表明，两种生成模型生成的数据在保持与真实的样本一定相似性的同时，具有合理的差异性。当将真实的和生成的轨迹数据应用于CAV的车辆跟驰模型时，生成的轨迹数据增加了TTC小于阈值的新临界碎片的数量。根据临界碎片的比例，WGAN-GP模型的性能优于VAE-GAN模型。研究结果为CAV的试验和安全性能改进提供了有益的启示。
For the simulation-based test and evaluation of connected and automated vehicles (CAVs), the trajectory of the background vehicle has a direct effect on the performance of CAVs and experiment outcomes. The collected real trajectory data are limited by the sample size and diversity, and may exclude critical attribute combinations that are of vital importance for CAVs’ tests. Consequently, it is indispensable to increase the richness of accessible trajectory data. In this study, we developed the Wasserstein generative adversarial network with gradient penalty (WGAN-GP) and a hybrid model of variational autoencoder and generative adversarial network (VAE-GAN) for trajectory data generation. These models are capable of learning a compressed representation of the observed data space, and generating data by sampling in the latent space and then mapping back to the original space. The real data and the generated data are applied in the car-following model of CAVs with cooperative adaptive cruise control (CACC) to evaluate safety performance using the time-to-collision (TTC) index. The results indicate that the generated data of the two generative models have reasonable differences while maintaining a certain similarity with the real samples. When real and generated trajectory data are applied to the car-following model of CAVs, the generated trajectory data increases the number of new critical fragments whose TTC is smaller than the threshold. The WGAN-GP model performs better than the VAE-GAN model according to the ratio of critical fragments. Findings of this study provide useful insights for CAVs’ tests and safety performance improvement.