Accurate trajectory prediction of nearby vehicles is crucial for the safe motion planning of automated vehicles in dynamic driving scenarios such as highway merging. Existing methods cannot initiate prediction for a vehicle unless observed for a fixed duration of two or more seconds. This prevents a fast reaction by the ego vehicle to vehicles that enter its perception range, thus creating safety concerns. Therefore, this paper proposes a novel transformer-based trajectory prediction approach, specifically trained to handle any observation length larger than one frame. We perform a comprehensive evaluation of the proposed method using two large-scale highway trajectory datasets, namely the highD and exiD. In addition, we study the impact of the proposed prediction approach on motion planning and control tasks using extensive merging scenarios from the exiD dataset. To the best of our knowledge, this marks the first instance where such a large-scale highway merging dataset has been employed for this purpose. The results demonstrate that the prediction model achieves state-of-the-art performance on highD dataset and maintains lower prediction error w.r.t. the constant velocity across all observation lengths in exiD. Moreover, it significantly enhances safety, comfort, and efficiency in dense traffic scenarios, as compared to the constant velocity model.

翻译：准确预测周围车辆的轨迹对于自动驾驶汽车在动态驾驶场景（如高速公路汇入）中的安全运动规划至关重要。现有方法需对车辆进行固定时长（两秒或以上）的观测后才能启动预测，这阻碍了自车对进入其感知范围的车辆做出快速反应，从而引发安全隐患。为此，本文提出一种基于Transformer的创新轨迹预测方法，专门训练以处理任意长度（超过一帧）的观测数据。我们利用两个大规模高速公路轨迹数据集（即highD和exiD）对所提方法进行了全面评估。此外，我们借助exiD数据集中丰富的汇入场景，研究了该预测方法对运动规划与控制任务的影响。据我们所知，这是首次将如此大规模的高速公路汇入数据集用于此类研究。结果表明，该预测模型在highD数据集上达到了最先进的性能，并在exiD数据集上所有观测长度下均保持比恒定速度模型更低的预测误差。此外，与恒定速度模型相比，该模型在密集交通场景中显著提升了安全性、舒适性和效率。