Affiliations: [a]
College of Electrical and Control Engineering, Xi’an University of Science and Technology, Xi’an, China
| [b] Xi’an Key Laboratory of Electrical Equipment Condition Monitoring and Power Supply Security, Xi’an, China
Correspondence:
[*]
Corresponding author. Jiaxuan Wang, College of Electrical and Control Engineering, Xi’an University of Science and Technology, Xi’an, China. E-mail: [email protected].
Abstract: Pedestrian trajectory prediction plays a crucial role in autonomous driving, as its accuracy directly affects the autonomous driving system’s comprehension of the environment and subsequent decision-making processes. Current trajectory prediction methods tend to oversimplify pedestrians to mere point coordinates, utilizing positional information to infer interactions among individuals while overlooking the temporal correlations between them, thereby excessively simplifying pedestrian characteristics. To address the aforementioned issues, this paper proposes a trajectory prediction model for autonomous driving applications, that takes into account both pedestrian motion characteristics and scene interaction. The model optimizes the LSTM unit structure twice, serving to learn correlations among long trajectories of pedestrians and to integrate multiple forms of information into the neighborhood interaction module. Furthermore, our model introduces dual attention mechanisms for individuals and scenes, focusing on the key motion points of individual pedestrians and their interactive behavior with others in busy scenarios. The efficacy of the model was validated on the MOT16 pedestrian dataset and the Daimler pedestrian path prediction dataset, outperforming mainstream methods with 8% and 10% reductions in Average Displacement Error and Final Displacement Error respectively.
Keywords: Trajectory prediction, automated driving, CNN-LSTM, deep learning
