DeepIPC: Deeply Integrated Perception and Control for an Autonomous Vehicle in Real Environments
Oskar Natan,
Jun Miura
Abstract:In this work, we introduce DeepIPC, a novel end-to-end model tailored for autonomous driving, which seamlessly integrates perception and control tasks. Unlike traditional models that handle these tasks separately, DeepIPC innovatively combines a perception module, which processes RGBD images for semantic segmentation and generates bird's eye view (BEV) mappings, with a controller module that utilizes these insights along with GNSS and angular speed measurements to accurately predict navigational waypoints. Thi… Show more
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