Probabilistic Prediction of Longitudinal Trajectory Considering Driving
  Heterogeneity with Interpretability
Shuli Wang Kun Gao Lanfang Zhang Yang Liu Lei Chen
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.