研究远程驾驶中压缩点云的3D目标检测，提升安全性与传输效率。
Teleoperated Driving: a New Challenge for 3D Object Detection in Compressed Point Clouds
- 基于SELMA数据集扩展标注，构建适用于远程驾驶的点云检测框架。
- 在压缩率、推理速度与检测精度间取得平衡，满足3GPP网络延迟要求。
- 为远程驾驶场景提供可落地的点云压缩与检测方案，适合车联网开发者参考。
近年来，互联设备的发展推动了信息娱乐、教育和工业应用等领域的进步，传感器数量增加及软硬件性能提升加速了这一趋势。其中，远程驾驶（Teleoperated Driving, TD）显著受益于这些技术进展。在该场景中，操控员通过车辆生成的传感器数据远程安全驾驶，数据通过车对外通信（V2X）交换。本文聚焦于从点云数据中检测车辆与行人，以支持安全的远程驾驶操作。我们利用多模态、开源且合成的SELMA数据集，并扩充了3D物体的真值边界框以支持目标检测任务。系统评估了主流压缩算法与检测模型在压缩效率、编解码与推理时间、检测准确率等方面的性能。同时，衡量了压缩与检测对V2X网络的数据速率与延迟影响，验证其是否符合TD应用的3GPP标准要求。
原文摘要 · Abstract (English)
In recent years, the development of interconnected devices has expanded in many fields, from infotainment to education and industrial applications. This trend has been accelerated by the increased number of sensors and accessibility to powerful hardware and software. One area that significantly benefits from these advancements is Teleoperated Driving (TD). In this scenario, a controller drives safely a vehicle from remote leveraging sensors data generated onboard the vehicle, and exchanged via Vehicle-to-Everything (V2X) communications. In this work, we tackle the problem of detecting the presence of cars and pedestrians from point cloud data to enable safe TD operations. More specifically, we exploit the SELMA dataset, a multimodal, open-source, synthetic dataset for autonomous driving, that we expanded by including the ground-truth bounding boxes of 3D objects to support object detection. We analyze the performance of state-of-the-art compression algorithms and object detectors under several metrics, including compression efficiency, (de)compression and inference time, and detection accuracy. Moreover, we measure the impact of compression and detection on the V2X network in terms of data rate and latency with respect to 3GPP requirements for TD applications.
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