Automated vehicles are envisioned to navigate safely in complex mixed-traffic scenarios alongside human-driven vehicles. To promise a high degree of safety, accurately predicting the maneuvers of surrounding vehicles and their future positions is a critical task and attracts much attention. However, most existing studies focused on reasoning about positional information based on objective historical trajectories without fully considering the heterogeneity of driving behaviors. Therefore, this study proposes a trajectory prediction framework that combines Mixture Density Networks (MDN) and considers the driving heterogeneity to provide probabilistic and personalized predictions. Specifically, based on a certain length of historical trajectory data, the situation-specific driving preferences of each driver are identified, where key driving behavior feature vectors are extracted to characterize heterogeneity in driving behavior among different drivers. With the inputs of the short-term historical trajectory data and key driving behavior feature vectors, a probabilistic LSTMMD-DBV model combined with LSTM-based encoder-decoder networks and MDN layers is utilized to carry out personalized predictions. Finally, the SHapley Additive exPlanations (SHAP) method is employed to interpret the trained model for predictions. The proposed framework is tested based on a wide-range vehicle trajectory dataset. The results indicate that the proposed model can generate probabilistic future trajectories with remarkably improved predictions compared to existing benchmark models. Moreover, the results confirm that the additional input of driving behavior feature vectors representing the heterogeneity of driving behavior could provide more information and thus contribute to improving the prediction accuracy.

翻译：自动驾驶汽车被设想为能够在复杂的混合交通场景中与人类驾驶车辆一起安全行驶。为了确保高度安全性，准确预测周围车辆的操作及其未来位置是一项关键任务，并引起了广泛关注。然而，大多数现有研究主要基于客观历史轨迹推理位置信息，未充分考虑驾驶行为的异质性。因此，本研究提出了一种结合混合密度网络（MDN）并考虑驾驶异质性的轨迹预测框架，以提供概率性和个性化的预测。具体而言，基于一定长度的历史轨迹数据，识别每位驾驶员在特定情境下的驾驶偏好，提取关键驾驶行为特征向量以表征不同驾驶员之间的驾驶行为异质性。利用短期历史轨迹数据和关键驾驶行为特征向量作为输入，采用基于LSTM编码器-解码器网络和MDN层的概率性LSTMMD-DBV模型进行个性化预测。最后，采用SHapley加性解释（SHAP）方法对训练后的模型进行解释。所提出的框架基于大规模车辆轨迹数据集进行测试。结果表明，与现有基准模型相比，所提出的模型能够生成概率性未来轨迹且预测性能显著提升。此外，结果证实，代表驾驶行为异质性的驾驶行为特征向量的额外输入可提供更多信息，从而有助于提高预测精度。