  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.
