解决自动驾驶中不同激光雷达传感器间的感知差异问题
S2S-Net: Addressing the Domain Gap of Heterogeneous Sensor Systems in LiDAR-Based Collective Perception
- 设计S2S-Net架构，增强跨传感器域的感知鲁棒性
- 在SCOPE数据集上，新方法性能优于现有方法44个百分点
- 适合研究车联网协同感知与传感器异构性问题的学者
协同感知（CP）已成为克服自动驾驶中个体感知局限性的有前景方法。尽管已有多种实现方案，但连接与自动化车辆（CAV）使用不同传感器系统所引发的传感器到传感器（Sensor2Sensor）域差距仍基本未被解决，主要因缺乏包含异构传感器配置的车载数据集。近期发布的SCOPE数据集通过为每辆CAV提供三种不同激光雷达传感器的数据，填补了这一空白。本研究首次针对车对车（V2V）协同感知中的传感器域差距提出解决方案。首先，我们提出传感器域鲁棒架构S2S-Net；随后在SCOPE数据集上深入分析了当前主流协同感知方法及S2S-Net的域适应能力。结果表明，所有评估的先进协同感知方法均严重受制于传感器域差距，而S2S-Net在未见传感器域中仍保持高精度，相比现有方法最高提升44个百分点。
原文摘要 · Abstract (English)
Collective Perception (CP) has emerged as a promising approach to overcome the limitations of individual perception in the context of autonomous driving. Various approaches have been proposed to realize collective perception; however, the Sensor2Sensor domain gap that arises from the utilization of different sensor systems in Connected and Automated Vehicles (CAVs) remains mostly unaddressed. This is primarily due to the paucity of datasets containing heterogeneous sensor setups among the CAVs. The recently released SCOPE datasets address this issue by providing data from three different LiDAR sensors for each CAV. This study is the first to address the Sensor2Sensor domain gap in vehicle-to-vehicle (V2V) collective perception. First, we present our sensor-domain robust architecture S2S-Net. Then an in-depth analysis of the Sensor2Sensor domain adaptation capabilities of state-of-the-art CP methods and S2S-Net is conducted on the SCOPE dataset. This study shows that, all evaluated state-of-the-art mehtods for collective perception highly suffer from the Sensor2Sensor domain gap, while S2S-Net demonstrates the capability to maintain very high performance in unseen sensor domains and outperforms the evaluated state-of-the-art methods by up to 44 percentage points.
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