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HDGT:通过场景编码进行多代理轨迹预测的异质驱动图形变压器
概括
本研究介绍了用于自动驾驶的异质驾驶图形变压器 (HDGT),有效编码复杂的驾驶场景. 在轨道预测任务中,HDGT实现了最先进的性能.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器人技术 机器人技术 机器人技术
背景情况:
- 自动驾驶系统需要准确地呈现复杂的驾驶场景.
- 驾驶场景包含具有丰富的语义和空间关系的多元元素 (代理人,车道,标志).
- 现有的方法与空间信息的相对性和自我中心性质作斗争.
研究的目的:
- 开发一种新的骨干模型,用于编码自动驾驶中的驾驶场景.
- 在驾驶场景中有效地表现异质元素及其多样化的关系.
- 为了提高下游任务的性能,例如轨迹预测.
主要方法:
- 提出异质驱动图形变压器 (HDGT) 模型.
- 构建一个异构的图形,具有不同的节点和边缘类型,代表驾驶场景元素.
- 使用局部节点中心的坐标系统来编码相对空间信息.
- 在图形神经网络 (GNN) 中使用层次变压器结构来进行异质数据聚合.
主要成果:
- HDGT将驾驶场景模拟为一个异质图,捕捉复杂的对象间关系.
- 该模型成功地以以自我为中心的方式编码空间关系.
- 在轨道预测基准上取得了最先进的表现,包括INTERACTION预测挑战和Waymo开放运动挑战.
结论:
- 异质驾驶图形变压器 (HDGT) 为自动驾驶场景表示提供了一个强大的框架.
- HDGT对异质图形构造和空间编码的方法显著提高了轨迹预测的准确性.
- 这种模型为智能汽车感知和运动预测提供了有希望的进步.
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