A Novel Temporal Multi-Gate Mixture-of-Experts Approach for Vehicle Trajectory and Driving Intention Prediction

TL;DR

This paper introduces a novel multi-task learning model called TMMOE that simultaneously predicts vehicle trajectories and driving intentions, leveraging temporal convolutional networks and expert layers for improved accuracy in automated driving systems.

Contribution

The paper presents a new Temporal Multi-Gate Mixture-of-Experts model that jointly predicts vehicle trajectories and driving intentions, capturing their correlations for enhanced prediction performance.

Findings

TMMOE outperforms LSTM models on CitySim dataset.

Achieves highest classification accuracy for driving intention prediction.

Demonstrates superior regression accuracy for vehicle trajectory forecasting.

Abstract

Accurate Vehicle Trajectory Prediction is critical for automated vehicles and advanced driver assistance systems. Vehicle trajectory prediction consists of two essential tasks, i.e., longitudinal position prediction and lateral position prediction. There is a significant correlation between driving intentions and vehicle motion. In existing work, the three tasks are often conducted separately without considering the relationships between the longitudinal position, lateral position, and driving intention. In this paper, we propose a novel Temporal Multi-Gate Mixture-of-Experts (TMMOE) model for simultaneously predicting the vehicle trajectory and driving intention. The proposed model consists of three layers: a shared layer, an expert layer, and a fully connected layer. In the model, the shared layer utilizes Temporal Convolutional Networks (TCN) to extract temporal features. Then the expert layer is built to identify different information according to the three tasks. Moreover, the fully connected layer is used to integrate and export prediction results. To achieve better performance, uncertainty algorithm is used to construct the multi-task loss function. Finally, the publicly available CitySim dataset validates the TMMOE model, demonstrating superior performance compared to the LSTM model, achieving the highest classification and regression results. Keywords: Vehicle trajectory prediction, driving intentions Classification, Multi-task