  Fusing different sensor modalities can be a difficult task, particularly if
they are asynchronous. Asynchronisation may arise due to long processing times
or improper synchronisation during calibration, and there must exist a way to
still utilise this previous information for the purpose of safe driving, and
object detection in ego vehicle/ multi-agent trajectory prediction.
Difficulties arise in the fact that the sensor modalities have captured
information at different times and also at different positions in space.
Therefore, they are not spatially nor temporally aligned. This paper will
investigate the challenge of radar and LiDAR sensors being asynchronous
relative to the camera sensors, for various time latencies. The spatial
alignment will be resolved before lifting into BEV space via the transformation
of the radar/LiDAR point clouds into the new ego frame coordinate system. Only
after this can we concatenate the radar/LiDAR point cloud and lifted camera
features. Temporal alignment will be remedied for radar data only, we will
implement a novel method of inferring the future radar point positions using
the velocity information. Our approach to resolving the issue of sensor
asynchrony yields promising results. We demonstrate velocity information can
drastically improve IoU for asynchronous datasets, as for a time latency of 360
milliseconds (ms), IoU improves from 49.54 to 53.63. Additionally, for a time
latency of 550ms, the camera+radar (C+R) model outperforms the camera+LiDAR
(C+L) model by 0.18 IoU. This is an advancement in utilising the
often-neglected radar sensor modality, which is less favoured than LiDAR for
autonomous driving purposes.
