Automated vehicles require an accurate perception of their surroundings for safe and efficient driving. Lidar-based object detection is a widely used method for environment perception, but its performance is significantly affected by adverse weather conditions such as rain and fog. In this work, we investigate various strategies for enhancing the robustness of lidar-based object detection by processing sequential data samples generated by lidar sensors. Our approaches leverage temporal information to improve a lidar object detection model, without the need for additional filtering or pre-processing steps. We compare $10$ different neural network architectures that process point cloud sequences including a novel augmentation strategy introducing a temporal offset between frames of a sequence during training and evaluate the effectiveness of all strategies on lidar point clouds under adverse weather conditions through experiments. Our research provides a comprehensive study of effective methods for mitigating the effects of adverse weather on the reliability of lidar-based object detection using sequential data that are evaluated using public datasets such as nuScenes, Dense, and the Canadian Adverse Driving Conditions Dataset. Our findings demonstrate that our novel method, involving temporal offset augmentation through randomized frame skipping in sequences, enhances object detection accuracy compared to both the baseline model (Pillar-based Object Detection) and no augmentation.

翻译：自动驾驶汽车需要准确感知周围环境以确保安全高效的行驶。基于激光雷达的目标检测是一种广泛使用的环境感知方法，但其性能在雨、雾等恶劣天气条件下会显著下降。本文研究了通过处理激光雷达传感器生成的时序数据样本来增强目标检测鲁棒性的多种策略。我们的方法利用时间信息改进激光雷达目标检测模型，且无需额外的滤波或预处理步骤。我们比较了10种处理点云序列的神经网络架构，包括一种新颖的数据增强策略——在训练过程中引入序列帧之间的时间偏移，并通过实验评估了所有策略在恶劣天气条件下对激光雷达点云的有效性。本研究全面评估了利用时序数据缓解恶劣天气对激光雷达目标检测可靠性影响的有效方法，并在nuScenes、Dense和加拿大恶劣驾驶条件数据集等公开数据集上进行了验证。实验结果表明，我们提出的通过序列中随机跳帧实现时间偏移增强的新方法，相较于基线模型（基于柱状体的目标检测）和无增强方案，显著提升了目标检测精度。