Self-supervised learning (SSL) is an emerging technique that has been successfully employed to train convolutional neural networks (CNNs) and graph neural networks (GNNs) for more transferable, generalizable, and robust representation learning. However its potential in motion forecasting for autonomous driving has rarely been explored. In this study, we report the first systematic exploration and assessment of incorporating self-supervision into motion forecasting. We first propose to investigate four novel self-supervised learning tasks for motion forecasting with theoretical rationale and quantitative and qualitative comparisons on the challenging large-scale Argoverse dataset. Secondly, we point out that our auxiliary SSL-based learning setup not only outperforms forecasting methods which use transformers, complicated fusion mechanisms and sophisticated online dense goal candidate optimization algorithms in terms of performance accuracy, but also has low inference time and architectural complexity. Lastly, we conduct several experiments to understand why SSL improves motion forecasting. Code is open-sourced at \url{https://github.com/AutoVision-cloud/SSL-Lanes}.
翻译:自监督学习(SSL)是一种新兴技术,已经成功地用于培训进化神经网络(CNNs)和图形神经网络(GNNs),以进行更可转让、普遍和有力的代表性学习。然而,在自主驾驶运动预测方面,它的潜力很少得到探讨。在本研究中,我们报告了将自监督观点纳入运动预测的首次系统探索和评估。我们首先提议调查四个新的自监督学习任务,以理论理由进行动态预测,并对具有挑战性的大型Argoversal数据集进行定量和定性比较。第二,我们指出,我们的辅助性SSL(GNS)的学习不仅设计了利用变异器、复杂的聚变机制以及精密的在线密集目标候选人优化算法,在性能精确性能方面,而且具有较低的推论时间和建筑复杂性。最后,我们进行了一些实验,以了解为什么SLSL改进运动预测。代码在\url{https://github.com/Autoviion-cloud/SSLanes}的软件来源开放。