Timealign: A multi-modal object detection method for time misalignment fusing in autonomous driving
This addresses a specific synchronization problem for autonomous driving systems, but it is incremental as it builds on existing GraphBEV framework.
The paper tackled time misalignment between LiDAR and other sensors in autonomous driving by using historical LiDAR frames to align features, achieving improved object detection performance with concrete gains reported in the abstract.
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.