  Cooperative perception via communication among intelligent traffic agents has
great potential to improve the safety of autonomous driving. However, limited
communication bandwidth, localization errors and asynchronized capturing time
of sensor data, all introduce difficulties to the data fusion of different
agents. To some extend, previous works have attempted to reduce the shared data
size, mitigate the spatial feature misalignment caused by localization errors
and communication delay. However, none of them have considered the
asynchronized sensor ticking times, which can lead to dynamic object
misplacement of more than one meter during data fusion. In this work, we
propose Time-Aligned COoperative Object Detection (TA-COOD), for which we adapt
widely used dataset OPV2V and DairV2X with considering asynchronous LiDAR
sensor ticking times and build an efficient fully sparse framework with
modeling the temporal information of individual objects with query-based
techniques. The experiment results confirmed the superior efficiency of our
fully sparse framework compared to the state-of-the-art dense models. More
importantly, they show that the point-wise observation timestamps of the
dynamic objects are crucial for accurate modeling the object temporal context
and the predictability of their time-related locations.
