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CoDEA:从自然驾驶数据中提取和增强合作变车道场景的框架
1School of Traffic and Transportation Engineering Central South University, Changsha 410075, China.
Accident; analysis and prevention
|February 11, 2026
概括
合作驾驶系统需要更多的现实数据来验证. CoDEA 管道通过从自然驾驶数据中提取行为并通过交互意识模块来增强它们来生成现实的合作驾驶数据集.
科学领域:
- 智能运输系统 智能运输系统
- 自主驾驶技术的发展
- 数据科学为移动提供了数据科学.
背景情况:
- 现代交通面临着交通增加,基础设施有限和安全问题等挑战.
- 合作驾驶提供了安全,拥堵和流动性的解决方案,但缺乏用于验证的真实数据.
- 现有的数据集不足以培训和评估复杂的合作驾驶算法.
研究的目的:
- 为了解决合作驾驶系统现实数据的稀缺性.
- 引入一个新的管道,CoDEA (合作驾驶提取和增强),用于生成现实的合作驾驶数据集.
- 促进合作驾驶算法的开发和验证.
主要方法:
- 从大规模的自然驾驶数据 (NDD) 中提取合作的车道改变行为.
- 通过交互意识的上下文编码 (IA-CE) 模块来增强DiffTraj框架,用于实现现实的场景生成.
- 使用计算指标 (RMSE,MAE) 和特征分布比较来评估生成的轨迹.
主要成果:
- CoDEA成功地从真实世界的驾驶数据中提取了关键的动力学和合作特征.
- 该IA-CE模块可以生成多样化和现实的合作变车道轨迹.
- 生成的数据与现实世界的模式具有很强的相似性,同时引入了有价值的多样性.
结论:
- CoDEA管道为生成高质量的合作驾驶数据集提供了强大的解决方案.
- 这种方法弥合了现实世界复杂性和算法测试要求之间的差距.
- 通过改进数据可用性,CoDEA为推进合作变轨控制算法奠定了基础.
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