Accurately forecasting the motion of traffic actors is crucial for the deployment of autonomous vehicles at a large scale. Current trajectory forecasting approaches primarily concentrate on optimizing a loss function with a specific metric, which can result in predictions that do not adhere to physical laws or violate external constraints. Our objective is to incorporate explicit knowledge priors that allow a network to forecast future trajectories in compliance with both the kinematic constraints of a vehicle and the geometry of the driving environment. To achieve this, we introduce a non-parametric pruning layer and attention layers to integrate the defined knowledge priors. Our proposed method is designed to ensure reachability guarantees for traffic actors in both complex and dynamic situations. By conditioning the network to follow physical laws, we can obtain accurate and safe predictions, essential for maintaining autonomous vehicles' safety and efficiency in real-world settings.In summary, this paper presents concepts that prevent off-road predictions for safe and reliable motion forecasting by incorporating knowledge priors into the training process.

翻译：准确预测交通参与者的运动对于大规模部署自动驾驶车辆至关重要。当前的轨迹预测方法主要专注于优化带有特定度量的损失函数，这可能导致预测结果不符合物理规律或违反外部约束。我们的目标是将显式知识先验融入网络，使其能够预测符合车辆运动学约束和驾驶环境几何结构的未来轨迹。为此，我们引入了一种非参数剪枝层和注意力层来集成定义的知识先验。所提出的方法旨在确保交通参与者在复杂动态场景中的可达性保证。通过使网络遵循物理规律，我们能够获得准确且安全的预测，这对于在现实世界中保持自动驾驶车辆的安全性和效率至关重要。总之，本文通过在训练过程中融入知识先验，提出了防止越野预测的概念，从而实现安全可靠的运动预测。