用脉冲神经网络实现低功耗4D雷达目标检测
SpikingRTNH: Spiking Neural Network for 4D Radar Object Detection
- 用脉冲神经元替代传统激活函数，提升能效
- 在K-Radar数据集上能耗降低78%，精度达51.1% AP 3D
- 适合自动驾驶系统中对低功耗感知有要求的场景
4D雷达作为自动驾驶中3D目标检测的关键传感器，具备恶劣天气下稳定感知和高密度点云识别物体形状的优势。然而处理高密度数据需要大量计算资源与能耗。本文提出SpikingRTNH，首个基于4D雷达数据的脉冲神经网络（SNN）3D目标检测模型。通过将传统ReLU激活函数替换为漏电积分-放电（LIF）脉冲神经元，显著提升能效。受人类认知机制启发，引入生物自上而下的推理（BTI），按从高密度到低密度顺序处理点云，有效利用噪声较低、重要性更高的点。在K-Radar数据集上的实验表明，采用BTI的SpikingRTNH能耗降低78%，检测性能与对应人工神经网络（ANN）相当（3D AP 51.1%，BEV AP 57.0%）。该结果验证了脉冲神经网络在自动驾驶低功耗4D雷达感知中的可行性。代码已开源。
原文摘要 · Abstract (English)
Recently, 4D Radar has emerged as a crucial sensor for 3D object detection in autonomous vehicles, offering both stable perception in adverse weather and high-density point clouds for object shape recognition. However, processing such high-density data demands substantial computational resources and energy consumption. We propose SpikingRTNH, the first spiking neural network (SNN) for 3D object detection using 4D Radar data. By replacing conventional ReLU activation functions with leaky integrate-and-fire (LIF) spiking neurons, SpikingRTNH achieves significant energy efficiency gains. Furthermore, inspired by human cognitive processes, we introduce biological top-down inference (BTI), which processes point clouds sequentially from higher to lower densities. This approach effectively utilizes points with lower noise and higher importance for detection. Experiments on K-Radar dataset demonstrate that SpikingRTNH with BTI significantly reduces energy consumption by 78% while achieving comparable detection performance to its ANN counterpart (51.1% AP 3D, 57.0% AP BEV). These results establish the viability of SNNs for energy-efficient 4D Radar-based object detection in autonomous driving systems. All codes are available at https://github.com/kaist-avelab/k-radar.
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