EPN: An Ego Vehicle Planning-Informed Network for Target Trajectory Prediction

TL;DR

This paper introduces EPN, a novel neural network that improves multimodal vehicle trajectory prediction by incorporating the ego vehicle's planned path and an endpoint prediction module, significantly reducing prediction errors.

Contribution

The paper proposes EPN, a new trajectory prediction model that integrates ego vehicle planning and endpoint prediction to enhance accuracy in dynamic driving scenarios.

Findings

Achieves over 30% reduction in RMSE, ADE, FDE on NGSIM dataset.

Achieves over 64% reduction in RMSE, ADE, FDE on HighD dataset.

Effectively models mutual influence between vehicles in trajectory prediction.

Abstract

Trajectory prediction plays a crucial role in improving the safety of autonomous vehicles. However, due to the highly dynamic and multimodal nature of the task, accurately predicting the future trajectory of a target vehicle remains a significant challenge. To address this challenge, we propose an Ego vehicle Planning-informed Network (EPN) for multimodal trajectory prediction. In real-world driving, the future trajectory of a vehicle is influenced not only by its own historical trajectory, but also by the behavior of other vehicles. So, we incorporate the future planned trajectory of the ego vehicle as an additional input to simulate the mutual influence between vehicles. Furthermore, to tackle the challenges of intention ambiguity and large prediction errors often encountered in methods based on driving intentions, we propose an endpoint prediction module for the target vehicle. This module predicts the target vehicle endpoints, refines them using a correction mechanism, and generates a multimodal predicted trajectory. Experimental results demonstrate that EPN achieves an average reduction of 34.9%, 30.7%, and 30.4% in RMSE, ADE, and FDE on the NGSIM dataset, and an average reduction of 64.6%, 64.5%, and 64.3% in RMSE, ADE, and FDE on the HighD dataset. The code will be open sourced after the letter is accepted.