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

TL;DR

This paper presents a hybrid deep learning and kinematic model for trajectory prediction in autonomous driving, incorporating expert knowledge to ensure physically feasible and safe predictions, validated on real-world data.

Contribution

It introduces a novel hybrid model that constrains the action space of deep learning with kinematic knowledge, improving prediction realism and safety.

Findings

Enhanced trajectory realism in predictions

Improved safety and robustness through action space constraints

Promising results on the Argoverse dataset

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.