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Found 3 papers, 3 papers with code
DeepIPCv2: LiDAR-powered Robust Environmental Perception and Navigational Control for Autonomous Vehicle
To evaluate its performance, we conduct several tests by deploying the model to predict a set of driving records and perform real automated driving under three different conditions.
DeepIPC: Deeply Integrated Perception and Control for an Autonomous Vehicle in Real Environments
In this work, we introduce DeepIPC, a novel end-to-end model tailored for autonomous driving, which seamlessly integrates perception and control tasks.
End-to-end Autonomous Driving with Semantic Depth Cloud Mapping and Multi-agent
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 to perform both perception and control tasks simultaneously.
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