MSMA: Multi-agent Trajectory Prediction in Connected and Autonomous Vehicle Environment with Multi-source Data Integration

TL;DR

This paper introduces MSMA, a deep learning framework that fuses multi-source sensor and communication data for accurate trajectory prediction of surrounding vehicles in autonomous driving scenarios, especially with high connected vehicle penetration.

Contribution

The paper presents a novel multi-source data fusion approach using cross-attention in a deep learning model for trajectory prediction in connected autonomous vehicle environments.

Findings

Data fusion improves prediction accuracy in mixed traffic scenarios.

High connected vehicle penetration enhances the benefits of multi-source data integration.

The proposed method outperforms baseline models in the CARLA simulation environment.

Abstract

The prediction of surrounding vehicle trajectories is crucial for collision-free path planning. In this study, we focus on a scenario where a connected and autonomous vehicle (CAV) serves as the central agent, utilizing both sensors and communication technologies to perceive its surrounding traffics consisting of autonomous vehicles (AVs), connected vehicles (CVs), and human-driven vehicles (HDVs). Our trajectory prediction task is aimed at all the detected surrounding vehicles. To effectively integrate the multi-source data from both sensor and communication technologies, we propose a deep learning framework called MSMA utilizing a cross-attention module for multi-source data fusion. Vector map data is utilized to provide contextual information. The trajectory dataset is collected in CARLA simulator with synthesized data errors introduced. Numerical experiments demonstrate that in a mixed traffic flow scenario, the integration of data from different sources enhances our understanding of the environment. This notably improves trajectory prediction accuracy, particularly in situations with a high CV market penetration rate. The code is available at: https://github.com/xichennn/MSMA.