PillarFlow: End-to-end Birds-eye-view Flow Estimation for Autonomous Driving
This addresses a critical perception task for autonomous driving systems, but it appears incremental as it builds on existing flow estimation methods.
The paper tackles the problem of accurately estimating the state of surrounding obstacles for autonomous driving by proposing an end-to-end deep learning framework for LIDAR-based flow estimation in bird's eye view, which improves tracking performance of dynamic and static objects.
In autonomous driving, accurately estimating the state of surrounding obstacles is critical for safe and robust path planning. However, this perception task is difficult, particularly for generic obstacles/objects, due to appearance and occlusion changes. To tackle this problem, we propose an end-to-end deep learning framework for LIDAR-based flow estimation in bird's eye view (BeV). Our method takes consecutive point cloud pairs as input and produces a 2-D BeV flow grid describing the dynamic state of each cell. The experimental results show that the proposed method not only estimates 2-D BeV flow accurately but also improves tracking performance of both dynamic and static objects.