Real-time Simulation and Testing of a Neural Network-based Autonomous Vehicle Trajectory Prediction Model

Abstract

Autonomous vehicle trajectory prediction is an important component of autonomous driving assistance algorithms (ADAAs), which can help autonomous driving systems (ADSs) better understand the traffic environment, assess critical tasks in advance thus improve traffic safety and traffic efficiency. However, some existing neural network-based trajectory prediction models focus on theoretical numerical analysis and are not tested in real time, leading to doubts about the practical usability of these trajectory prediction models. To address the above limitations, this study first proposes a collaborative simulation environment integrating traffic scenario construction, driving environment perception, and neural network modeling, afterwards used the co-simulation environment for trajectory data and driving environment data collection. In addition, based on the characteristics of the collected data, a trajectory prediction model based on Bi-Encoder-Decoder and deep neural network (DNN) is proposed and pre-trained. Finally, the pre-trained completed model is embedded in the co-simulation environment and tested in real-time with different batches of data. The simulation results show that the proposed trajectory prediction model can predict trajectories well under specific training data batches, and the best performing trajectory prediction model has a prospective time of 4.9 s and a prediction accuracy of 91.55%.