Differentiable Raycasting for Self-supervised Occupancy Forecasting

Tarasha Khurana, Peiyun Hu, Achal Dave, Jason Ziglar, David Held, Deva Ramanan
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引用次数: 18

Abstract

Motion planning for safe autonomous driving requires learning how the environment around an ego-vehicle evolves with time. Ego-centric perception of driveable regions in a scene not only changes with the motion of actors in the environment, but also with the movement of the ego-vehicle itself. Self-supervised representations proposed for large-scale planning, such as ego-centric freespace, confound these two motions, making the representation difficult to use for downstream motion planners. In this paper, we use geometric occupancy as a natural alternative to view-dependent representations such as freespace. Occupancy maps naturally disentangle the motion of the environment from the motion of the ego-vehicle. However, one cannot directly observe the full 3D occupancy of a scene (due to occlusion), making it difficult to use as a signal for learning. Our key insight is to use differentiable raycasting to"render"future occupancy predictions into future LiDAR sweep predictions, which can be compared with ground-truth sweeps for self-supervised learning. The use of differentiable raycasting allows occupancy to emerge as an internal representation within the forecasting network. In the absence of groundtruth occupancy, we quantitatively evaluate the forecasting of raycasted LiDAR sweeps and show improvements of upto 15 F1 points. For downstream motion planners, where emergent occupancy can be directly used to guide non-driveable regions, this representation relatively reduces the number of collisions with objects by up to 17% as compared to freespace-centric motion planners.
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自监督入住率预测的可微投射
安全自动驾驶的运动规划需要了解自动驾驶汽车周围的环境如何随着时间的推移而变化。场景中以自我为中心的可驾驶区域感知不仅随着环境中角色的运动而变化,而且随着自我车辆本身的运动而变化。针对大规模规划提出的自监督表示,如以自我为中心的自由空间,混淆了这两种运动,使得下游运动规划者难以使用该表示。在本文中,我们使用几何占位作为依赖于视图的表示(如自由空间)的自然替代。占用地图自然地将环境的运动与自我车辆的运动分离开来。然而,人们无法直接观察到场景的完整3D占用(由于遮挡),因此很难将其用作学习的信号。我们的关键见解是使用可微分光线投射将未来的占用预测“渲染”到未来的激光雷达扫描预测中,这可以与自监督学习的地面真相扫描进行比较。可微分光线投射的使用使得占用率作为预测网络中的内部表示形式出现。在没有真实占用的情况下,我们定量评估了光线投射激光雷达扫描的预测,并显示了高达15个F1点的改进。对于下游运动规划器,紧急占用可以直接用于引导不可驾驶区域,与以自由空间为中心的运动规划器相比,这种表示相对减少了与物体碰撞的数量,最多可减少17%。
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