Safety-critical situations in road traffic often result from incorrect estimation of the future behavior of other road users. Therefore, many Advanced Driver Assistance Systems (ADAS) need prediction models to ensure safety. Physical prediction models offer the advantage of general use and work quite well for short prediction horizons, while for longer periods of time, maneuver-based models offer better performance which, however, strongly depends on the data used to train them. An additional challenge for prediction is the fact that the surrounding traffic can change its path, i.e. for safety not only one maneuver should be considered but regular updates are required. Against this background, we propose a method that uses three physics-based predictions – corresponding to different prediction assumptions and models – combined with possible maneuver-based trajectories derived from environmental knowledge. Continuous monitoring is used to select the most likely of the three physics-based models. This choice then influences the environment-based prediction and the output of both models is fused afterwards. The output of the resulting Multiple Model Trajectory Prediction (MMTP) has been validated with measured data from two different scenarios – a city junction and a highway – with a good prediction performance and without the need for special measurements as commonly required for maneuver-based prediction.

道路交通中的安全关键情况通常是由于对其他道路使用者未来行为的错误估计造成的。因此，许多高级驾驶辅助系统 (ADAS) 需要预测模型来确保安全。物理预测模型具有普遍使用的优势，并且在较短的预测范围内工作得很好，而在较长的时间段内，基于机动的模型提供更好的性能，然而，这在很大程度上取决于用于训练它们的数据。预测的另一个挑战是周围交通可以改变其路径这一事实，即为了安全，不仅应考虑一次机动，而且需要定期更新。在此背景下，我们提出了一种方法，该方法使用三种基于物理学的预测——对应于不同的预测假设和模型——结合从环境知识得出的可能的基于机动的轨迹。连续监测用于选择三种基于物理的模型中最可能的。然后，此选择会影响基于环境的预测，然后融合两个模型的输出。生成的多模型轨迹预测 (MMTP) 的输出已经通过来自两个不同场景（城市路口和高速公路）的测量数据进行了验证，具有良好的预测性能，并且不需要像基于机动的预测通常需要的特殊测量. 连续监测用于选择三种基于物理的模型中最可能的。然后，此选择会影响基于环境的预测，然后融合两个模型的输出。生成的多模型轨迹预测 (MMTP) 的输出已经通过来自两个不同场景（城市路口和高速公路）的测量数据进行了验证，具有良好的预测性能，并且不需要像基于机动的预测通常需要的特殊测量. 连续监测用于选择三种基于物理的模型中最可能的。然后，此选择会影响基于环境的预测，然后融合两个模型的输出。生成的多模型轨迹预测 (MMTP) 的输出已经通过来自两个不同场景（城市路口和高速公路）的测量数据进行了验证，具有良好的预测性能，并且不需要像基于机动的预测通常需要的特殊测量.