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.

翻译：感知任务涉及多目标检测与跟踪，轨迹预测则是自动驾驶的两大核心任务。然而，当前这两项任务大多被分开独立研究，导致多数轨迹预测模块基于真实轨迹数据开发，而未考虑现实场景中检测与跟踪模块提取的轨迹存在噪声。这些含噪轨迹会显著影响轨迹预测器的性能，并可能导致严重的预测错误。本文构建了一个名为ODTP（在线检测、跟踪与预测）的检测-跟踪-轨迹预测端到端框架。该框架采用先进在线多目标跟踪模型QD-3DT进行感知，并直接基于检测结果训练轨迹预测器DCENet++，而非完全依赖真实轨迹。我们在自动驾驶领域广泛使用的nuScenes数据集上评估了ODTP的性能。大量实验表明，ODTP实现了高性能的端到端轨迹预测。DCENet++通过增强动态地图，相比基础模型预测出更精确的轨迹。与基于含噪检测结果训练的其他生成式及确定性轨迹预测模型相比，该模型展现出更强的鲁棒性。