UPAQ: A Framework for Real-Time and Energy-Efficient 3D Object Detection in Autonomous Vehicles

Engineering notes

Experimental results on the Jetson Orin Nano embedded platform indicate that UPAQ achieves up to 5.62× and 5.13× model compression rates, up to 1.97× and 1.86× boost in inference speed, and up to 2.07× and 1.87× reduction in energy consumption compared to state-of-the-art model compression frameworks, on the Pointpillar and SMOKE models respectively.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

To enhance perception in autonomous vehicles (AVs), recent efforts are concentrating on 3D object detectors, which deliver more comprehensive predictions than traditional 2D object detectors, at the cost of increased memory footprint and computational resource usage. We present a novel framework called UPAQ, which leverages semi-structured pattern pruning and quantization to improve the efficiency of LiDAR point-cloud and camera-based 3D object detectors on resource-constrained embedded AV platforms. Experimental results on the Jetson Orin Nano embedded platform indicate that UPAQ achieves up to 5.62× and 5.13× model compression rates, up to 1.97× and 1.86× boost in inference speed, and up to 2.07× and 1.87× reduction in energy consumption compared to state-of-the-art model compression frameworks, on the Pointpillar and SMOKE models respectively.

Links and sources

• Official / arXiv page
• PDF from original source

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