Abstract—In a fully autonomous driving framework, where vehicles

operate without human intervention, information sharing

plays a fundamental role. In this context, new network solutions

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have to be designed to handle the large volumes of data generated

by the rich sensor suite of the cars in a reliable and efficient

way. Among all the possible sensors, Light Detection and Ranging

(LiDAR) can produce an accurate 3D point cloud representation

of the surrounding environment, which in turn generates high

data rates. For this reason, efficient point cloud compression

is paramount to alleviate the burden of data transmission over

bandwidth-constrained channels and to facilitate real-time communications.

In this paper, we propose a pipeline to efficiently

compress LiDAR observations in an automotive scenario. First,

we leverage the capabilities of RangeNet++, a Deep Neural

Network (DNN) used to semantically infer point labels, to reduce

the channel load by selecting the most valuable environmental

data to be disseminated. Second, we compress the selected points

using Draco, a 3D compression algorithm which is able to obtain

compression up to the quantization error. Our experiments,

validated on the Semantic KITTI dataset, demonstrate that it

is possible to compress and send the information at the frame

rate of the LiDAR, thus achieving real-time performance.

Index Terms—Autonomous driving, compression, Draco, Deep

Neural Networks (DNNs), RangeNet++, point cloud, LiDAR.

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