MSMA: コネクテッド自動運転環境におけるマルチソースデータ統合を用いたマルチエージェント軌道予測
MSMA: Multi-agent Trajectory Prediction in Connected and Autonomous Vehicle Environment with Multi-source Data Integration
CAVを中心にセンサと通信のマルチソースデータをクロスアテンションで統合し、周囲車両の軌道を予測する深層学習フレームワークMSMAを提案。CARLAで収集した混合交通データで予測精度の向上を実証した。
著者: Xi Chen, Rahul Bhadani, Zhanbo Sun, Larry Head
分類: cs.RO, cs.LG
原文アブストラクト
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.
