强大的BEV 3D对象检测,用于轮胎喷气式汽车
Dongsheng Yang1, Xiaojie Fan1, Wei Dong1
1The BYD Auto Industry Company Limited, Shenzhen 518000, China.
Sensors (Basel, Switzerland)
|July 27, 2024
概括
一种新的几何导向自动调整大小的核心变压器 (GARKT) 方法可以提高自动驾驶汽车在轮胎爆破时的感知. 这种方法即使在完全破胎的情况下也保持了强大的性能,确保了更安全的操作.
科学领域:
- 计算机视觉 计算机视觉
- 自主系统 自主系统
- 机器人技术 机器人技术 机器人技术
背景情况:
- 鸟眼视图 (BEV) 方法对于自动驾驶汽车的感知至关重要,比LiDAR提供了优势.
- 由于依赖于精确的摄像头校准,现有的BEV方法在轮胎爆破时失败,造成安全风险.
研究的目的:
- 为自动驾驶汽车开发一个强大的BEV感知方法,能够承受轮胎爆破场景.
- 为了解决当前BEV方法的局限性,当摄像头校准受到轮胎膨胀的影响时.
主要方法:
- 专门为轮胎爆破情况提出了几何引导自动调整内核变压器 (GARKT) 方法.
- 开发了一种用于轮胎爆破的相机偏差模型,并利用具有自动大小内核的几何先验.
- 编码可调整尺寸的感知区域并将它们平整起来以生成BEV表示.
主要成果:
- GARKT在一个新的爆出数据集上获得了0.439的nuScenes检测得分 (NDS).
- 保持了强大的0.431的NDS,即使轮胎完全破裂,也超过了其他基于变压器的BEV方法.
- 在单个GPU上以每秒大约20.5的近实时性能.
结论:
- GARKT方法显著提高了自动驾驶汽车在轮胎爆破期间感知系统的稳定性和安全性.
- GARKT提供了一种实用的解决方案,用于在具有挑战性的现实驾驶条件下保持可靠的感知.
- 拟议的方法为未来的自动驾驶安全系统提供了一个有希望的方向.
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