The multi-modal perception methods are thriving in the autonomous driving field due to their better usage of complementary data from different sensors. Such methods depend on calibration and synchronization between sensors to get accurate environmental information. There have already been studies about space-alignment robustness in autonomous driving object detection process, however, the research for time-alignment is relatively few. As in reality experiments, LiDAR point clouds are more challenging for real-time data transfer, our study used historical frames of LiDAR to better align features when the LiDAR data lags exist. We designed a Timealign module to predict and combine LiDAR features with observation to tackle such time misalignment based on SOTA GraphBEV framework.

翻译：多模态感知方法因其能更好地利用来自不同传感器的互补数据而在自动驾驶领域蓬勃发展。此类方法依赖于传感器之间的校准与同步以获取准确的环境信息。尽管已有研究关注自动驾驶目标检测过程中的空间对齐鲁棒性，但针对时间对齐的研究相对较少。鉴于实际实验中，激光雷达点云数据的实时传输更具挑战性，本研究利用激光雷达的历史帧数据，在激光雷达数据存在延迟时更好地对齐特征。我们基于SOTA GraphBEV框架设计了一个Timealign模块，通过预测并结合激光雷达特征与观测数据来解决此类时间错位问题。