67 citations · 114 across the 5 of their papers we have counts for
5 papers
Towards Compact Autonomous Driving Perception with Balanced Learning and Multi-sensor Fusion
Oskar Natan, Jun Miura
We present a novel compact deep multi-task learning model to handle various autonomous driving perception tasks in one forward pass. The model performs multiple views of semantic s…
Seq-DeepIPC: Sequential Sensing for End-to-End Control in Legged Robot Navigation
Oskar Natan, Jun Miura
We present Seq-DeepIPC, a sequential end-to-end perception-to-control model for legged robot navigation in real-world environments. Seq-DeepIPC advances intelligent sensing for aut…
DeepIPCv2: LiDAR-powered Robust Environmental Perception and Navigational Control for Autonomous Vehicle
Oskar Natan, Jun Miura
We propose DeepIPCv2, an end-to-end autonomous driving framework that integrates LiDAR-based environmental perception with command-specific control learning. Unlike prior camera-re…
DeepIPC: Deeply Integrated Perception and Control for an Autonomous Vehicle in Real Environments
Oskar Natan, Jun Miura
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 th…
End-to-end Autonomous Driving with Semantic Depth Cloud Mapping and Multi-agent
Oskar Natan, Jun Miura
Focusing on the task of point-to-point navigation for an autonomous driving vehicle, we propose a novel deep learning model trained with end-to-end and multi-task learning manners…