Motion prediction for intelligent vehicles typically focuses on estimating the most probable future evolutions of a traffic scenario. Estimating the gap acceptance, i.e., whether a vehicle merges or crosses before another vehicle with the right of way, is often handled implicitly in the prediction. However, an infrastructure-based maneuver planning can assign artificial priorities between cooperative vehicles, so it needs to evaluate many more potential scenarios. Additionally, the prediction horizon has to be long enough to assess the impact of a maneuver. We, therefore, present a novel long-term prediction approach handling the gap acceptance estimation and the velocity prediction in two separate stages. Thereby, the behavior of regular vehicles as well as priority assignments of cooperative vehicles can be considered. We train both stages on real-world traffic observations to achieve realistic prediction results. Our method has a competitive accuracy and is fast enough to predict a multitude of scenarios in a short time, making it suitable to be used in a maneuver planning framework.

翻译：智能车辆的移动预测通常侧重于估计交通场景中最可能的未来演化。间隙接受度（即车辆是否在享有优先通行权的其他车辆之前并入或穿越）的估计往往在预测中被隐式处理。然而，基于基础设施的机动规划可在协同车辆之间分配人工优先级，因此需要评估更多潜在场景。此外，预测时域必须足够长以评估机动行为的影响。为此，我们提出一种新颖的长期预测方法，将间隙接受度估计与速度预测分两个独立阶段处理。由此可同时考虑常规车辆行为以及协同车辆的优先级分配。我们基于真实交通观测数据对两个阶段进行训练，以实现贴近实际的预测结果。该方法具有竞争力的精度，且能在短时间内快速预测大量场景，适用于机动规划框架。