  Perception that involves multi-object detection and tracking, and trajectory
prediction are two major tasks of autonomous driving. However, they are
currently mostly studied separately, which results in most trajectory
prediction modules being developed based on ground truth trajectories without
taking into account that trajectories extracted from the detection and tracking
modules in real-world scenarios are noisy. These noisy trajectories can have a
significant impact on the performance of the trajectory predictor and can lead
to serious prediction errors. In this paper, we build an end-to-end framework
for detection, tracking, and trajectory prediction called ODTP (Online
Detection, Tracking and Prediction). It adopts the state-of-the-art online
multi-object tracking model, QD-3DT, for perception and trains the trajectory
predictor, DCENet++, directly based on the detection results without purely
relying on ground truth trajectories. We evaluate the performance of ODTP on
the widely used nuScenes dataset for autonomous driving. Extensive experiments
show that ODPT achieves high performance end-to-end trajectory prediction.
DCENet++, with the enhanced dynamic maps, predicts more accurate trajectories
than its base model. It is also more robust when compared with other generative
and deterministic trajectory prediction models trained on noisy detection
results.
