The rapid development of 3D object detection systems for self-driving cars has significantly improved accuracy. However, these systems struggle to generalize across diverse driving environments, which can lead to safety-critical failures in detecting traffic participants. To address this, we propose a method that utilizes unlabeled repeated traversals of multiple locations to adapt object detectors to new driving environments. By incorporating statistics computed from repeated LiDAR scans, we guide the adaptation process effectively. Our approach enhances LiDAR-based detection models using spatial quantized historical features and introduces a lightweight regression head to leverage the statistics for feature regularization. Additionally, we leverage the statistics for a novel self-training process to stabilize the training. The framework is detector model-agnostic and experiments on real-world datasets demonstrate significant improvements, achieving up to a 20-point performance gain, especially in detecting pedestrians and distant objects. Code is available at https://github.com/zhangtravis/Hist-DA.

翻译：三维目标检测系统在自动驾驶领域的快速发展显著提升了检测精度。然而，这些系统难以泛化到多样的驾驶环境，可能导致检测交通参与者时出现安全关键性故障。针对这一问题，我们提出一种方法，利用多个地点未标注的重复穿越来使目标检测器适应新驾驶环境。通过融合重复激光雷达扫描计算所得的统计特征，我们有效引导了适应过程。该方法利用空间量化历史特征增强基于激光雷达的检测模型，并引入轻量级回归头以利用统计特征进行特征正则化。此外，我们创新性地利用这些统计特征设计自训练流程以稳定训练过程。本框架与检测器模型无关，在真实世界数据集上的实验表明其实现了显著性能提升，尤其在检测行人和远处目标时最高可提升20个百分点。代码开源地址：https://github.com/zhangtravis/Hist-DA。