# Probabilistic Prediction of Longitudinal Trajectory Considering Driving Heterogeneity with Interpretability

arXiv: https://arxiv.org/abs/2312.12123 · HTML: https://mlsanity.com/paper/2312.12123/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 52 references that are in the map.
- First posted on arXiv 19 Dec 2023.

## Builds on (most cited first, 14 of 14)

- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks](https://mlsanity.com/paper/1803.10892/) (2018)
- [DESIRE: Distant Future Prediction in Dynamic Scenes with Interacting Agents](https://mlsanity.com/paper/1704.04394/) (2017)
- [SoPhie: An Attentive GAN for Predicting Paths Compliant to Social and Physical Constraints](https://mlsanity.com/paper/1806.01482/) (2018)
- [Multimodal Trajectory Predictions for Autonomous Driving using Deep Convolutional Networks](https://mlsanity.com/paper/1809.10732/) (2018)
- [Convolutional Social Pooling for Vehicle Trajectory Prediction](https://mlsanity.com/paper/1805.06771/) (2018)
- [Multi-modal Factorized Bilinear Pooling with Co-Attention Learning for Visual Question Answering](https://mlsanity.com/paper/1708.01471/) (2017)
- [Causal Interpretability for Machine Learning -- Problems, Methods and Evaluation](https://mlsanity.com/paper/2003.03934/) (2020)
- [Multi-Modal Trajectory Prediction of Surrounding Vehicles with Maneuver based LSTMs](https://mlsanity.com/paper/1805.05499/) (2018)
- [Conditional Generative Neural System for Probabilistic Trajectory Prediction](https://mlsanity.com/paper/1905.01631/) (2019)
- [Statistical stability indices for LIME: obtaining reliable explanations for Machine Learning models](https://mlsanity.com/paper/2001.11757/) (2020)
- [Graph-Based Spatial-Temporal Convolutional Network for Vehicle Trajectory Prediction in Autonomous Driving](https://mlsanity.com/paper/2109.12764/) (2021)
- [Naturalistic Driver Intention and Path Prediction using Recurrent Neural Networks](https://mlsanity.com/paper/1807.09995/) (2018)
- [DeepTrack: Lightweight Deep Learning for Vehicle Path Prediction in Highways](https://mlsanity.com/paper/2108.00505/) (2021)

## Built on by (most cited first, 1 of 1)

- [A Structure-Aware Lane Graph Transformer Model for Vehicle Trajectory Prediction](https://mlsanity.com/paper/2405.20121/) (2024)

## Abstract (arXiv)

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.