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 $minFDE_6$.

翻译：积累大量真实驾驶数据在自动驾驶轨迹预测领域至关重要。鉴于当前轨迹预测模型严重依赖数据驱动方法，我们旨在解决有限数据条件下学习通用轨迹预测表征的挑战。我们提出增强高清地图与轨迹数据，并在此基础上应用预训练策略。具体而言，我们利用高清地图的图表示，通过向量变换重塑地图，以高效扩充有限场景数量。此外，采用基于规则的模型根据增强场景生成轨迹，从而在真实采集轨迹之外扩展轨迹规模。为促进增强数据集中通用表征的学习，我们全面探索了不同预训练策略，包括将掩码自编码器（MAE）概念扩展到轨迹预测。大量实验证明了数据扩充与预训练策略的有效性，在$MR_6$、$minADE_6$和$minFDE_6$指标上分别以5.04%、3.84%和8.30%的幅度大幅超越基准预测模型。