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.

自动驾驶中，多目标检测、跟踪和轨迹预测是关键任务，本文介绍了一种名为ODTP的端到端框架，采用了先进的在线多目标跟踪模型QD-3DT进行感知，并根据检测结果直接训练轨迹预测器DCENet++，实验表明ODTP在轨迹预测方面取得了高性能，DCENet++相较于其他噪声检测结果训练的生成式和确定性轨迹预测模型更为准确和鲁棒。

单目图像道路用户检测、跟踪和预测的端到端框架