An End-to-End Framework of Road User Detection, Tracking, and Prediction from Monocular Images

TL;DR

This paper introduces ODTP, an end-to-end framework integrating detection, tracking, and trajectory prediction for autonomous driving, which directly trains on noisy detection data to improve real-world prediction accuracy.

Contribution

It presents a novel end-to-end framework that trains trajectory prediction directly on detection outputs, enhancing robustness and accuracy in noisy, real-world scenarios.

Findings

ODTP achieves high end-to-end trajectory prediction performance.

DCENet++ outperforms its base model with more accurate trajectories.

The framework is more robust compared to other models trained on noisy data.

Abstract

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.