Autonomous systems necessitate fast and reliable detection capabilities. The advancement of autonomous driving has intensified the demand for sophisticated obstacle detection algorithms, resulting in the integration of various sensors like LiDAR, radar, and cameras into vehicles. LiDAR is suitable for obstacle detection since it can detect the localization and intensity information of objects more precisely than radar while handling illumination and weather conditions better than cameras. However, despite an extensive body of literature exploring object detection utilizing LiDAR data, few solutions are viable for real-time deployment in vehicles due to computational constraints. Our research begins by evaluating state-of-the-art models for fast object detection using LiDAR on the Zenseact Open Dataset, focusing particularly on how their performance varies with the distance to the object. Our analysis of the dataset revealed that distant objects where often defined by very few points, posing challenges for detection. To address this, we experimented with point cloud superimposition with 1-4 previous frames to enhance point cloud density. However, we encountered issues with the handling of dynamic objects under rigid transformations. We addressed this by the inclusion of a time feature for each point to denote its origin time step. Initial experiments underscored the crucial role of this time feature in model success. Although superimposition initially decreased mean average precision except within 210-250 m, mean average recall improved beyond 80-100 m. This observation encouraged us to explore varying the number of superimposed point clouds across different ranges, optimizing the configuration for each range. Experimentation with this adaptive approach yielded promising results, enhancing the overall mAF1 score for the model. Additionally, our research highlights shortcomings in existing datasets that must be addressed to develop robust detectors and establish appropriate benchmarks.