Trajectory prediction is crucial for autonomous driving, enabling vehicles to navigate safely by anticipating the movements of surrounding road users. However, current deep learning models often lack trustworthiness as their predictions can be physically infeasible and illogical to humans. To make predictions more trustworthy, recent research has incorporated prior knowledge, like the social force model for modeling interactions and kinematic models for physical realism. However, these approaches focus on priors that suit either vehicles or pedestrians and do not generalize to traffic with mixed agent classes. We propose incorporating interaction and kinematic priors of all agent classes--vehicles, pedestrians, and cyclists with class-specific interaction layers to capture agent behavioral differences. To improve the interpretability of the agent interactions, we introduce DG-SFM, a rule-based interaction importance score that guides the interaction layer. To ensure physically feasible predictions, we proposed suitable kinematic models for all agent classes with a novel pedestrian kinematic model. We benchmark our approach on the Argoverse 2 dataset, using the state-of-the-art transformer HPTR as our baseline. Experiments demonstrate that our method improves interaction interpretability, revealing a correlation between incorrect predictions and divergence from our interaction prior. Even though incorporating the kinematic models causes a slight decrease in accuracy, they eliminate infeasible trajectories found in the dataset and the baseline model. Thus, our approach fosters trust in trajectory prediction as its interaction reasoning is interpretable, and its predictions adhere to physics.

翻译：轨迹预测对于自动驾驶至关重要，通过预测周围道路使用者的运动，使车辆能够安全行驶。然而，当前的深度学习模型通常缺乏可信度，因为其预测可能在物理上不可行或对人类而言不合逻辑。为了使预测更加可信，近期研究引入了先验知识，例如用于建模交互的社会力模型和用于物理真实性的运动学模型。然而，这些方法侧重于适用于车辆或行人的先验知识，无法推广到包含混合交通参与者类别的场景。我们提出融合所有参与者类别（车辆、行人和骑行者）的交互与运动学先验知识，并采用类别特定的交互层来捕捉参与者的行为差异。为了提高参与者交互的可解释性，我们引入了DG-SFM，这是一种基于规则的交互重要性评分，用于指导交互层。为了确保物理上可行的预测，我们为所有参与者类别提出了合适的运动学模型，其中包括一种新颖的行人运动学模型。我们在Argoverse 2数据集上对我们的方法进行了基准测试，使用最先进的Transformer模型HPTR作为基线。实验表明，我们的方法提高了交互的可解释性，揭示了错误预测与偏离交互先验之间的相关性。尽管引入运动学模型导致精度略有下降，但它们消除了数据集中和基线模型中存在的不可行轨迹。因此，我们的方法增强了轨迹预测的可信度，因为其交互推理是可解释的，且预测符合物理规律。