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汽车至关重要。当前的轨迹预测方法主要侧重于优化具有特定度量的损失函数，这可能导致预测结果不符合物理定律或违反外部约束。我们的目标是将显式的知识先验融入网络中，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化剪枝层和注意力层来整合定义的知识先验。所提出的方法旨在确保交通参与者在复杂动态情境中的可达性保障。通过引导网络遵循物理定律，我们能够获得准确且安全的预测，这对于在真实场景中维护自动驾驶汽车的安全性和效率至关重要。总之，本文通过将知识先验融入训练过程，提出了防止越野预测的概念，以实现安全可靠的运动预测。