To address driving in near-accident scenarios, we propose a hierarchical reinforcement and imitation learning (H-ReIL) approach that consists of low-level policies learned by IL for discrete driving modes, and a high-level policy learned by RL that switches between different driving modes.
Panoptic segmentation is a complex full scene parsing task requiring simultaneous instance and semantic segmentation at high resolution.
Although cameras are ubiquitous, robotic platforms typically rely on active sensors like LiDAR for direct 3D perception.
We propose an end-to-end learning approach for panoptic segmentation, a novel task unifying instance (things) and semantic (stuff) segmentation.
Ranked #20 on Panoptic Segmentation on Cityscapes val (using extra training data)