无需昂贵的模块化和3D手动注释的端到端自动驾驶
IEEE transactions on pattern analysis and machine intelligence
|September 23, 2025
概括
本研究介绍了UAD,一个无监督的端到端自动驾驶框架. 通过消除注释要求和减少计算开销,UAD显著提高了驾驶性能和效率.
科学领域:
- 计算机视觉 计算机视觉
- 机器人技术 机器人技术 机器人技术
- 机器学习 机器学习
背景情况:
- 目前的端到端自动驾驶 (E2EAD) 模型通常依赖于监督感知和预测,需要广泛的3D注释.
- 这种对注释的依赖阻碍了数据的扩展,并在培训和推理过程中产生了大量的计算成本.
- 现有的E2EAD模型可以模仿模块化架构,导致效率低下.
研究的目的:
- 为基于视觉的自动驾驶提出UAD,一个端到端的框架,利用无监督的借口任务来实现基于视觉的自动驾驶.
- 克服E2EAD中监督学习的局限性,特别是注释要求和计算开销.
- 为了提高开环评估性能和闭环驾驶质量.
主要方法:
- 开发了UAD,这是一个端到端的框架,为自动驾驶提供了无监督的借口任务.
- 引入了一个角度感知借口,可以在没有手动注释的情况下预测角度智能的空间对象性和时间动态.
- 实施了一种自我监督的培训策略,重点是增强视图下的轨迹一致性,以提高方向盘的稳定性.
主要成果:
- UAD在nuScenes上实现了最先进的开放循环性能,与UniAD相比,平均碰撞率的相对改善为38.7%.
- 在CARLA中表现出强大的闭环驾驶,在Town05 Long基准上获得了98.5%的路线完成得分.
- 与UniAD相比,消耗的培训资源减少了44.3%,并实现了比UniAD快3.4倍的推断.
结论:
- 与监督E2EAD方法相比,UAD提供了显著的性能优势.
- 无监督的方法大大减少了数据注释需求和计算需求.
- 无人驾驶系统 (UAD) 为自动驾驶系统提供了一个更高效,更可扩展的解决方案.
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