DeepIPCv2: LiDAR-powered Robust Environmental Perception and Navigational Control for Autonomous Vehicle
arxiv(2023)
摘要
We present DeepIPCv2, an autonomous driving model that perceives the
environment using a LiDAR sensor for more robust drivability, especially when
driving under poor illumination conditions where everything is not clearly
visible. DeepIPCv2 takes a set of LiDAR point clouds as the main perception
input. Since point clouds are not affected by illumination changes, they can
provide a clear observation of the surroundings no matter what the condition
is. This results in a better scene understanding and stable features provided
by the perception module to support the controller module in estimating
navigational control properly. 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. We also conduct
ablation and comparative studies with some recent models to justify its
performance. Based on the experimental results, DeepIPCv2 shows a robust
performance by achieving the best drivability in all driving scenarios.
Furthermore, to support future research, we will upload the codes and data to
https://github.com/oskarnatan/DeepIPCv2.
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