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使用深度学习模型进行连接和自动化车辆测试的数据生成.

Ye Li1, Fei Liu2, Lu Xing3

  • 1School of Traffic and Transportation Engineering, Central South University, Changsha, Hunan 410075, China; Hunan Key Laboratory of Smart Roadway and Cooperative Vehicle-Infrastructure Systems, Changsha University of Science & Technology, Changsha 410114, Hunan, China.

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概括
此摘要是机器生成的。

像WGAN-GP和VAE-GAN这样的生成模型通过创建多样化的轨迹数据来增强连接和自动化车辆 (CAV) 测试. 在生成关键驾驶场景以提高CAV安全性能方面,WGAN-GP被证明是优越的.

关键词:
连接和自动化车辆合作性自适应巡航控制系统生成性的对抗性网络.安全评估 安全评估变量自动编码器变量自动编码器

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科学领域:

  • 人工智能的人工智能
  • 机器人技术 机器人技术 机器人技术
  • 运输工程 运输工程

背景情况:

  • 连接和自动化车辆 (CAV) 需要进行广泛的测试以验证安全性.
  • 对CAV的真实世界轨迹数据通常在样本大小和多样性上是有限的.
  • 对于CAV测试至关重要的关键场景可能不在收集的数据集中.

研究的目的:

  • 开发先进的生成模型,以创建现实的和多样化的背景车辆轨迹数据.
  • 评估生成的轨迹数据在改善CAV安全性绩效评估中的有效性.
  • 为了比较Wasserstein生成对抗网络与梯度惩罚 (WGAN-GP) 和变异自编码生成对抗网络 (VAE-GAN) 模型的性能.

主要方法:

  • 开发和实施WGAN-GP和VAE-GAN模型用于轨迹数据生成.
  • 在潜空间中学习了轨迹数据的压缩表示.
  • 通过从潜伏空间取样并向后映射生成新的轨迹数据.
  • 将现实和生成的数据集成到CAV的合作自适应巡航控制 (CACC) 汽车跟踪模型中.
  • 使用碰撞时间指数 (TTC) 评估安全性能.

主要成果:

  • 生成的轨迹数据显示出与真实数据样本的差异和相似之处.
  • 使用生成的轨迹数据在CAV模拟中增加了关键碎片 (低TTC) 的发生率.
  • 在生成关键碎片方面,WGAN-GP表现出了比VAE-GAN更好的性能,这一点由更高的比率表明.
  • 这两种模型都成功地扩大了测试轨迹数据的多样性.

结论:

  • 像WGAN-GP和VAE-GAN这样的生成模型对于增强CAV轨迹数据集是有效的.
  • 生成的数据,特别是来自WGAN-GP的数据,可以揭示CAV的更关键的安全场景.
  • 这种方法提高了基于模拟的CAV测试的稳定性和全面性.
  • 结果支持改进安全性能评估和CAV的开发.