A novel spatio-temporal trajectory data-driven development approach for autonomous vehicles

Nowadays, autonomous driving has been attracted widespread attention from academia and industry. As we all know, deep learning is effective and essential for the development of AI components of Autonomous Vehicles (AVs). However, it is challenging to adopt multi-source heterogenous data in deep learning. Therefore, we propose a novel data-driven approach for the delivery of high-quality Spatio-Temporal Trajectory Data (STTD) to AVs, which can be deployed to assist the development of AI components with deep learning. The novelty of our work is that the meta-model of STTD is constructed based on the domain knowledge of autonomous driving. Our approach, including collection, preprocessing, storage and modeling of STTD as well as the training of AI components, helps to process and utilize huge amount of STTD efficiently. To further demonstrate the usability of our approach, a case study of vehicle behavior prediction using Long Short-Term Memory (LSTM) networks is discussed. Experimental results show that our approach facilitates the training process of AI components with the STTD.