Accumulating substantial volumes of real-world driving data proves pivotal in the realm of trajectory forecasting for autonomous driving. Given the heavy reliance of current trajectory forecasting models on data-driven methodologies, we aim to tackle the challenge of learning general trajectory forecasting representations under limited data availability. We propose to augment both HD maps and trajectories and apply pre-training strategies on top of them. Specifically, we take advantage of graph representations of HD-map and apply vector transformations to reshape the maps, to easily enrich the limited number of scenes. Additionally, we employ a rule-based model to generate trajectories based on augmented scenes; thus enlarging the trajectories beyond the collected real ones. To foster the learning of general representations within this augmented dataset, we comprehensively explore the different pre-training strategies, including extending the concept of a Masked AutoEncoder (MAE) for trajectory forecasting. Extensive experiments demonstrate the effectiveness of our data expansion and pre-training strategies, which outperform the baseline prediction model by large margins, e.g. 5.04\%, 3.84\% and 8.30\% in terms of $MR_6$, $minADE_6$ and $minFDE_6$.

在有限的数据可用性下，我们提出了利用高精度地图和轨迹的图表示以及预训练策略相结合的方法来学习通用的轨迹预测表示，进而对轨迹预测模型进行改进，从而在数据扩展和预训练策略方面取得了显著的效果。

基于合成驾驶数据的轨迹预测预训练