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Goal-based Neural Physics Vehicle Trajectory Prediction Model

Rui Gan, Haotian Shi, Pei Li, Keshu Wu, Bocheng An, Linheng Li, Junyi Ma, Chengyuan Ma, Bin Ran

TL;DR

The paper presents the Goal-based Neural Physics Vehicle Trajectory Prediction Model (GNP), a two-stage framework that first infers multiple driving goals using a mode-aware transformer and then completes trajectories with a neural differential equations model embedded in a physics-based neural social force. This design balances long-term predictive accuracy with interpretability by exposing goal distributions and the forces shaping motion, including goal attraction and inter-vehicle/lane repulsion learned through neural networks. Empirical evaluation on NGSIM and HighD demonstrates state-of-the-art performance for long-horizon predictions, complemented by interpretable visualizations of forces and intention modes. Ablation studies confirm the importance of intention-mode modeling and the attraction-dominated trajectory completion, underscoring the value of combining neural learning with physics-informed dynamics for autonomous driving applications.

Abstract

Vehicle trajectory prediction plays a vital role in intelligent transportation systems and autonomous driving, as it significantly affects vehicle behavior planning and control, thereby influencing traffic safety and efficiency. Numerous studies have been conducted to predict short-term vehicle trajectories in the immediate future. However, long-term trajectory prediction remains a major challenge due to accumulated errors and uncertainties. Additionally, balancing accuracy with interpretability in the prediction is another challenging issue in predicting vehicle trajectory. To address these challenges, this paper proposes a Goal-based Neural Physics Vehicle Trajectory Prediction Model (GNP). The GNP model simplifies vehicle trajectory prediction into a two-stage process: determining the vehicle's goal and then choosing the appropriate trajectory to reach this goal. The GNP model contains two sub-modules to achieve this process. The first sub-module employs a multi-head attention mechanism to accurately predict goals. The second sub-module integrates a deep learning model with a physics-based social force model to progressively predict the complete trajectory using the generated goals. The GNP demonstrates state-of-the-art long-term prediction accuracy compared to four baseline models. We provide interpretable visualization results to highlight the multi-modality and inherent nature of our neural physics framework. Additionally, ablation studies are performed to validate the effectiveness of our key designs.

Goal-based Neural Physics Vehicle Trajectory Prediction Model

TL;DR

The paper presents the Goal-based Neural Physics Vehicle Trajectory Prediction Model (GNP), a two-stage framework that first infers multiple driving goals using a mode-aware transformer and then completes trajectories with a neural differential equations model embedded in a physics-based neural social force. This design balances long-term predictive accuracy with interpretability by exposing goal distributions and the forces shaping motion, including goal attraction and inter-vehicle/lane repulsion learned through neural networks. Empirical evaluation on NGSIM and HighD demonstrates state-of-the-art performance for long-horizon predictions, complemented by interpretable visualizations of forces and intention modes. Ablation studies confirm the importance of intention-mode modeling and the attraction-dominated trajectory completion, underscoring the value of combining neural learning with physics-informed dynamics for autonomous driving applications.

Abstract

Vehicle trajectory prediction plays a vital role in intelligent transportation systems and autonomous driving, as it significantly affects vehicle behavior planning and control, thereby influencing traffic safety and efficiency. Numerous studies have been conducted to predict short-term vehicle trajectories in the immediate future. However, long-term trajectory prediction remains a major challenge due to accumulated errors and uncertainties. Additionally, balancing accuracy with interpretability in the prediction is another challenging issue in predicting vehicle trajectory. To address these challenges, this paper proposes a Goal-based Neural Physics Vehicle Trajectory Prediction Model (GNP). The GNP model simplifies vehicle trajectory prediction into a two-stage process: determining the vehicle's goal and then choosing the appropriate trajectory to reach this goal. The GNP model contains two sub-modules to achieve this process. The first sub-module employs a multi-head attention mechanism to accurately predict goals. The second sub-module integrates a deep learning model with a physics-based social force model to progressively predict the complete trajectory using the generated goals. The GNP demonstrates state-of-the-art long-term prediction accuracy compared to four baseline models. We provide interpretable visualization results to highlight the multi-modality and inherent nature of our neural physics framework. Additionally, ablation studies are performed to validate the effectiveness of our key designs.
Paper Structure (17 sections, 15 equations, 4 figures, 2 tables)

This paper contains 17 sections, 15 equations, 4 figures, 2 tables.

Table of Contents

  1. Abstract
  2. 1. Introduction
  3. 2. Methodology
  4. 2.1 Problem Definition
  5. 2.2 Model Architecture
  6. 2.3 Goal Prediction
  7. 2.4 Goal-based Neural Social Force Trajectory Prediction
  8. 3.Results and discussion
  9. 3.1 Experimental Dataset and Setting
  10. 3.2 Metrics
  11. 3.3 Baseline models
  12. 3.4 Quantitative Results
  13. 3.5 Visualization Results
  14. 3.6 Ablation experiments
  15. 4. Conclusion
  16. ...and 2 more sections

Figures (4)

  • Figure 1: Model architecture proposed in this paper. This dual sub-module framework first estimates the intentions and predicts multiple possible goals, then forecasts the full trajectories using a deep-learning enhanced social force model.
  • Figure 2: Interpretability of vehicle trajectory predictions visualized in three example scenarios.Yellow arrow denotes the goal attraction force, blue arrow denotes the combined repulsive forces generated by the neighboring vehicles and the black arrow indicates the combined repulsive forces exerted by the lane lines.
  • Figure 3: Clustered intention modes from HighD (a) and Ngsim (b) dataset
  • Figure 4: Multiple prediction results on 3 different bahaviors: straight ahead (a, b), left lane change (c, d), and right lane change (e,f)