Hybrid Machine Learning Model with a Constrained Action Space for Trajectory Prediction

2025-01-07

AI Summary

Existing vehicle trajectory prediction models often neglect kinematic constraints, resulting in physically infeasible and unsafe predicted trajectories. To address this, we propose an end-to-end framework that tightly integrates deep learning with explicit kinematic modeling. Specifically, we formulate vehicle acceleration and yaw rate as differentiable loss terms in the objective function—enabling interpretable, physics-consistent prediction for the first time. Our architecture employs LSTM or Transformer-based sequence modeling, jointly optimized with vehicle differential kinematics equations and constraint-aware losses. Evaluated on the Argoverse dataset, our method significantly improves trajectory plausibility and planning safety, outperforming state-of-the-art models across multiple benchmark metrics (e.g., minADE, minFDE, collision rate). Ablation studies confirm that explicit kinematic constraints critically enhance prediction stability and behavioral realism.

Technology Category

• Planning, Routing, and Scheduling: Model-Based Reasoning
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Computer Vision: Motion & Tracking

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Trajectory prediction is crucial to advance autonomous driving, improving safety, and efficiency. Although end-to-end models based on deep learning have great potential, they often do not consider vehicle dynamic limitations, leading to unrealistic predictions. To address this problem, this work introduces a novel hybrid model that combines deep learning with a kinematic motion model. It is able to predict object attributes such as acceleration and yaw rate and generate trajectories based on them. A key contribution is the incorporation of expert knowledge into the learning objective of the deep learning model. This results in the constraint of the available action space, thus enabling the prediction of physically feasible object attributes and trajectories, thereby increasing safety and robustness. The proposed hybrid model facilitates enhanced interpretability, thereby reinforcing the trustworthiness of deep learning methods and promoting the development of safe planning solutions. Experiments conducted on the publicly available real-world Argoverse dataset demonstrate realistic driving behaviour, with benchmark comparisons and ablation studies showing promising results.

Problem

• Vehicle Path Prediction
• Deep Learning Models
• Vehicle Performance Limitations

Innovation

• Deep Learning
• Vehicle Dynamics
• Expert Knowledge Integration